Intelligent logistics monitoring method and system based on big data

Abstract

The invention discloses an intelligent logistics monitoring method and system based on big data, and belongs to the technical field of logistics planning. The system comprises a data acquisition module, a logistics analysis module, a monitoring management module and a visualization module. The data acquisition module is used for acquiring traffic data, logistics logs and transportation details of this time; the logistics analysis module is used for analyzing transportation details, marking point locations and calculating a first coefficient and a second coefficient between the point locations; the monitoring management module is used for calculating a third coefficient according to the first coefficient and the second coefficient, and sequentially setting intermediate points according to the third coefficient, so as to plan a transportation route and dynamically adjust the transportation route; and the visualization module is used for displaying a transportation route through a visualization interface, recording driving data and storing the driving data into a logistics log. According to the invention, through real-time data analysis and dynamic adjustment of the transportation route, the transportation efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics planning, and particularly to an intelligent logistics monitoring method and system based on big data. Background Art

[0002] With the rapid development of the global economy and the booming rise of e-commerce, the logistics industry is facing unprecedented challenges and opportunities. Against this background, the traditional logistics model gradually reveals problems such as low efficiency and high cost, and there is an urgent need to optimize it through advanced technical means. The rapid development of big data technology provides new ideas and methods for solving these problems.

[0003] At the present stage, the traditional logistics system has significant limitations in dealing with complex dynamic environments: problems such as relying on static path planning, insufficient timeliness of data processing, and weak multi-source information integration ability, resulting in rising transportation costs, difficult guarantee of timeliness, and a high loss rate of high-risk goods remaining high. Existing technologies usually make decisions based on historical experience or a single data dimension, and it is difficult to effectively integrate multi-modal information such as historical traffic conditions, cargo attributes, and real-time environmental variables. For example, traditional path planning models often ignore the difference in cargo value, resulting in the same risk assessment strategy being used for high-value goods and ordinary goods. And the update delay of real-time road conditions may cause vehicles to enter congested or accident areas by mistake, resulting in irreversible cargo damage and cost losses. Therefore, at the present stage, a more efficient and intelligent logistics planning technical solution is needed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent logistics monitoring method and system based on big data to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides an intelligent logistics monitoring method based on big data, including:

[0006] S100. Collect traffic data, logistics logs, and the details of the current transportation.

[0007] S200. Analyze the transportation details and mark the points, and calculate the coefficients between the points.

[0008] S300. Set intermediate points in sequence according to the coefficients, so as to plan the transportation route and adjust it dynamically.

[0009] S400. Display the transportation route on a visualization interface, record the driving data and store it in the logistics log.

[0010] In S100, the traffic data includes a route map and real-time road conditions. The route map is a plan view of the road on which the vehicle travels. The real-time road conditions include the real-time traffic flow of each road, specifically the vehicle density and the passing speed.

[0011] The logistics log includes status records of all trucks. Each truck corresponds to one status record, including the speed of the truck at each road position.

[0012] The transportation details include the starting position of the truck and the delivery list. The delivery list includes the transportation destination and freight value of each cargo.

[0013] The real-time road traffic flow data is obtained through the official websites of traffic management departments, navigation applications, and open data platforms. These channels provide real-time traffic flow information and road condition updates.

[0014] Each truck only maintains one dynamically updated status record. During the process of the truck transporting goods, the instantaneous position and instantaneous speed of the truck are regularly collected at a certain frequency and then added to the corresponding status record. The transportation destinations of all the goods on the truck are not unified, and the truck driver needs to stop at each transportation destination in turn to unload the goods.

[0015] S200 includes:

[0016] S201. Classify all goods according to whether the transportation destinations are the same. Mark the starting position of the truck as the starting point on the route map, and mark the delivery points according to the transportation destinations of each class. The sum of the freight values of all goods in each class is used as the value of the corresponding delivery point.

[0017] S202. Establish matching sets for the starting point and each delivery point respectively. Put all delivery points into the matching set of the starting point, and put the set of delivery points into all other delivery points except itself. On the route map, take the starting point q as the departure position and the delivery point w in the matching set of q as the arrival position, and plan all the connecting paths between the two.

[0018] When planning the connecting path for the starting point, select the starting position of the truck as the departure position. When planning the connecting path for each delivery point, select the corresponding transportation destination as the departure position.

[0019] S203. Analyze all status records and mark the speed at the corresponding road position on the route map. Calculate the standard deviation SD i and the average value SP i of all speeds within each connecting path, as well as the total value VAL sum of all delivery points. Obtain the length CD i of each connecting path, and substitute it into the formula to calculate the first coefficient XF qw between the starting point q and the delivery point w:

[0020]

[0021] In the formula, VAL w is the value of the delivery point w, j is the number of all connecting paths, and α is a constant greater than 0.

[0022] The first coefficient is used to evaluate economy and path stability. By integrating the vehicle driving speed in historical logistics logs and combining with the value of cargo transportation, it measures the value efficiency under the unit transportation cost. The standard deviation reflects the path fluctuation risk.

[0023] Realize preferential delivery of nodes with high cargo value and stable paths, significantly reducing the transportation risk and economic loss of high-value goods. By balancing path stability and cargo value, improve the comprehensive carrying efficiency of vehicles.

[0024] S204. Obtain the vehicle density MD of each connection path in real-time road conditions i and the passing speed NP i , and substitute them into the formula to calculate the risk coefficient FX of each connection path i :

[0025]

[0026] In the formula, γ and β are constants greater than 1; take the smallest risk coefficient as the second coefficient XS between the starting point q and the delivery point w qw ; and so on, calculate the first coefficient and the second coefficient between each delivery point respectively.

[0027] The second coefficient is used to evaluate the real-time risk dynamics, analyze the real-time road conditions, jointly determine the real-time risk according to the non-linear influence correlation between traffic density and passing speed, and screen the feasible path with the lowest risk in the current period.

[0028] Realize reducing the probability of delays and accidents caused by sudden road conditions during transportation. Respond to traffic environment changes in seconds, and improve the real-time performance of path planning.

[0029] S300 includes:

[0030] S301. Obtain the first coefficient XF u and the second coefficient XS u between the starting point and each delivery point, and substitute them into the formula to calculate the third coefficient XT of each delivery point u :

[0031]

[0032] In the formula, XS ave is the average value of the second coefficients of all delivery points in the matching set.

[0033] The third coefficient performs weighted fusion of economy and real-time risk, and is normalized based on the batch average risk at the same time. Dynamically generate a priority queue of delivery nodes: sort according to the third coefficient from high to low, and set intermediate points one by one until all nodes are covered.

[0034] Through the collaborative optimization of three coefficients, maximize the value return, minimize the risk, and optimize the path efficiency simultaneously.

[0035] S302. Set the delivery point with the largest third coefficient as the intermediate point Z1, calculate the third coefficient between Z1 and each delivery point in its matching set, and select the delivery point with the largest third coefficient as the intermediate point Z2.

[0036] S303. Delete the delivery points that have been set as intermediate points from the matching set of Z2, calculate the third coefficient between Z2 and the remaining delivery points in its matching set, and select the delivery point with the largest third coefficient as the intermediate point Z3.

[0037] S304. And so on until all delivery points are set as intermediate points. Place the starting point at the beginning, arrange all intermediate points in sequence according to the set time order, and connect the starting point to the intermediate point or adjacent intermediate points with the path corresponding to the minimum risk coefficient.

[0038] S305. After all paths are connected as the transportation route, the truck drives according to the transportation route and stops at each intermediate point to unload the goods in turn. During the driving process, calculate the third coefficient of the remaining intermediate points dynamically according to the real-time road conditions, and update the transportation route in real time through the third coefficient.

[0039] Integrate historical efficiency and real-time risk to generate a globally optimal delivery sequence that takes into account both economy and safety. Support real-time triggering of path adjustment during transportation to ensure the resilience of the overall logistics network.

[0040] In S400, the latest transportation route is displayed in real time through the in-vehicle visualization interface, and the truck driver drives according to the latest transportation route. During the driving process, regularly collect the speed of the truck at each road position and store it in the corresponding status record.

[0041] The intelligent logistics monitoring system based on big data includes a data collection module, a logistics analysis module, a monitoring and management module, and a visualization module.

[0042] The data collection module is used to collect traffic data, logistics logs, and the details of this transportation.

[0043] The logistics analysis module is used to analyze the transportation details, mark the points, and calculate the first coefficient and the second coefficient between the points.

[0044] The monitoring and management module is used to calculate the third coefficient according to the first coefficient and the second coefficient, set the intermediate points in sequence according to the third coefficient, so as to plan the transportation route and adjust it dynamically.

[0045] The visualization module is used to display the transportation route through the visualization interface.

[0046] The data acquisition module includes a transportation information acquisition unit, a logistics log acquisition unit, and a traffic data acquisition unit.

[0047] The transportation information acquisition unit is used to acquire the starting position of the truck and the delivery list. The delivery list includes the transportation destination and freight value of each cargo.

[0048] The delivery list parsing adopts visual OCR combined with NLP waybill structured decomposition to simultaneously identify keyword fields such as the address column, commodity category, and freight value.

[0049] The logistics log acquisition unit is used to acquire the status records of all trucks. Each truck corresponds to one status record, including the speed of the truck at each road position.

[0050] The in-vehicle IoT device reports GPS coordinates and speed data at a fixed frequency, and the edge gateway performs time series alignment and abnormal data cleaning. A lightweight communication protocol based on MQTT-SN is designed to support data breakpoint resumption in an intermittent network environment to ensure the integrity and order of data packets.

[0051] The traffic data acquisition unit is used to acquire the route map and real-time traffic conditions. The route map is a plan view of the roads where the vehicle travels. The real-time traffic conditions include the real-time traffic flow of each road, specifically the vehicle density and passing speed.

[0052] The RESTful API is used to connect to the real-time traffic condition data of the traffic management department and the navigation platform, analyze the vehicle density and passing speed, and use the WGS84 to GCJ-02 coordinate system conversion to ensure the geospatial consistency of multi-source data.

[0053] The logistics analysis module includes a point marking unit and a coefficient analysis unit.

[0054] The point marking unit is used to mark points and plan connection paths.

[0055] First, all cargos are classified according to whether their transportation destinations are the same. Mark the starting position of the truck as the starting point on the route map, and mark the delivery points according to the transportation destinations of each class.

[0056] The sum of the freight values of all cargos in each class is used as the value of the corresponding delivery point.

[0057] Secondly, a matching set is established for the starting point and each delivery point respectively. All delivery points are put into the matching set of the starting point, and all other delivery points except itself are put into the set of the delivery point.

[0058] Finally, taking the starting point q as the departure position and the delivery point w in the matching set of q as the arrival position on the route map, plan all the connection paths between the two.

[0059] The coefficient analysis unit is used to calculate the first coefficient and the second coefficient between points.

[0060] The calculation of the first coefficient includes:

[0061] First, analyze all status records and mark the speed at the corresponding road positions in the roadmap.

[0062] Second, calculate the standard deviation SD i and the average value SP i of all speeds within each connection path, as well as the total value VAL sum of all delivery points.

[0063] Finally, obtain the length CD i of each connection path, and calculate the first coefficient XF between the starting point q and the delivery point w according to the formula: qw .

[0064] where VAL w is the value of the delivery point w, j is the number of all connection paths, and α is a constant greater than 0.

[0065] The calculation of the second coefficient includes:

[0066] First, obtain the vehicle density MD i and the passing speed NP i of each connection path in the real-time traffic conditions.

[0067] Second, calculate the risk coefficient FX of each connection path according to the formula: i ; where γ and β are constants greater than 1.

[0068] Finally, take the minimum risk coefficient as the second coefficient XS qw between the starting point q and the delivery point w.

[0069] And so on, calculate the first coefficient and the second coefficient between each pair of delivery points respectively.

[0070] The monitoring and management module includes a route planning unit and a dynamic adjustment unit.

[0071] The route planning unit is used to plan the transportation route of the truck.

[0072] First, obtain the first coefficient XF u and the second coefficient XS u between the starting point and each delivery point.

[0073] According to the formula: calculate the third coefficient XT u of each delivery point. Where XS aveIt is the average value of the second coefficients of all delivery points in the matching set.

[0074] Secondly, set the delivery point with the largest third coefficient as the intermediate point Z1, calculate the third coefficients between Z1 and each delivery point in its matching set, and select the delivery point with the largest third coefficient as the intermediate point Z2.

[0075] Delete the delivery points that have been set as intermediate points in the matching set of Z2, calculate the third coefficients between Z2 and the remaining delivery points in its matching set, and select the delivery point with the largest third coefficient as the intermediate point Z3.

[0076] Finally, and so on until all delivery points are set as intermediate points. Place the starting point at the first position, and arrange all intermediate points in sequence in the order of the set time.

[0077] Connect the starting point to the intermediate point or adjacent intermediate points with the path corresponding to the minimum risk coefficient. After all paths are connected, it is used as the transportation route.

[0078] The dynamic adjustment unit is used to update the transportation route in real time.

[0079] During the driving process of the truck according to the transportation route, it stops at each intermediate point in turn to unload goods. When driving, calculate the third coefficients of the remaining intermediate points dynamically according to the real-time road conditions, and update the transportation route in real time through the third coefficients.

[0080] The visualization module displays the latest transportation route through the in-vehicle visualization interface, and collects the speed of the truck at each road position and stores it in the corresponding status record.

[0081] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0082] Deep integration of multi-source data: Traditional logistics monitoring systems mostly rely on a single data source and lack multi-dimensional integration with real-time traffic, cargo attributes, and historical logistics logs. This solution integrates traffic management data, real-time road conditions of the navigation platform, in-vehicle sensors, and structured information of waybills through API interfaces, covering dynamic data throughout the transportation link.

[0083] Dynamic assessment of path risks: The static risk models of the prior art are difficult to adapt to real-time traffic changes and do not optimize the risk assessment priority in combination with the value of goods. This solution calculates the path stability through the historical speed standard deviation and the weight of the goods value, ensuring that high-value goods preferentially select routes with less fluctuations. Integrate vehicle density and passing speed, and evaluate path risks in real time to avoid temporarily constructed or accident areas.

[0084] Multi-objective collaborative optimization: In the existing technology, path planning mostly adopts single-objective optimization, making it difficult to balance conflicting objectives such as timeliness, cost, and safety. This solution generates a comprehensive priority that integrates economy (the first coefficient) and real-time risk (the second coefficient) based on a multi-attribute decision-making model, and reversely optimizes the delivery node sequence. By sequentially deleting redundant delivery points, the resource consumption of full-scale calculation is reduced.

[0085] Through the systematic design of multi-source data fusion, dynamic risk assessment, and multi-objective collaborative optimization, this technical solution solves the problems of data islands, response lags, and single-objective optimization defects in traditional logistics monitoring systems. This solution can reduce the comprehensive transportation cost and improve the response speed to abnormal events, providing special protection capabilities for high-value goods. Brief Description of the Drawings

[0086] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0087] Figure 1 is a schematic flowchart of the intelligent logistics monitoring method based on big data of the present invention;

[0088] Figure 2 is a schematic structural diagram of the intelligent logistics monitoring system based on big data of the present invention. Detailed Embodiments

[0089] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0090] Please refer to Figure 1 , the present invention provides an intelligent logistics monitoring method based on big data, including:

[0091] S100. Collect traffic data, logistics logs, and the details of this transportation.

[0092] S200. Analyze the transportation details and mark the points, and calculate the coefficients between the points.

[0093] S300. Set intermediate points in sequence according to the coefficients, thereby planning the transportation route and dynamically adjusting it.

[0094] S400. Display the transportation route on a visual interface, record the driving data, and store it in the logistics log.

[0095] In S100, the traffic data includes a route map and real-time traffic conditions. The route map is a plan view of the road on which the vehicle is traveling. The real-time traffic conditions include the real-time traffic volume of each road, specifically, the vehicle density and the passing speed.

[0096] The logistics log includes the status records of all trucks. Each truck corresponds to a status record, including the speed of the truck at each road location.

[0097] The transport details include the truck's origin location and a shipping manifest that includes the purpose and value of each shipment.

[0098] Real-time road traffic data is obtained through the official website of the traffic management department, navigation applications and open data platforms, which provide real-time traffic flow information and road condition updates.

[0099] Each truck only maintains one dynamically updated status record. During the transportation of goods by trucks, the instantaneous position and instantaneous speed of the truck are collected regularly at a certain frequency and added to the corresponding status record. The transportation destinations of all goods on the truck are not uniform, and the truck driver needs to stop at each transportation destination in turn to unload the goods.

[0100] S200 includes:

[0101] S201, classify all goods according to whether the transportation purpose is the same. Mark the starting point of the truck on the route map, mark the delivery point according to the transportation purpose of each category, and the sum of the freight values ​​of all goods in each category is used as the value of the corresponding delivery point.

[0102] S202: Create matching sets for the starting point and each delivery point respectively. The starting point's matching set contains all delivery points, and the delivery point's set contains all other delivery points except itself. In the route map, the starting point q is the departure position, and the delivery point w in q's matching set is the arrival position, and all connecting paths between the two are planned.

[0103] When planning a connection path for the starting point, select the starting point of the truck as the departure location. When planning a connection path for each delivery point, select the corresponding transportation destination as the departure location.

[0104] S203, analyze all status records, mark the speed at the corresponding road position in the route map, and calculate the standard deviation SD of all speeds in each connection path i and the average SP i , and the total value VAL of all delivery points sum . Get the length CD of each connection path i , substitute into the formula to calculate the first coefficient XF between the starting point q and the delivery point w qw :

[0105]

[0106] Where VAL w is the value of the delivery point w, j is the number of all connection paths, and α is a constant greater than 0.

[0107] The first coefficient is used to evaluate the economy and path stability. It measures the value efficiency under unit transportation cost by integrating the vehicle driving speed in the historical logistics log (including the mean and standard deviation) and combining it with the cargo transportation value (based on the weighted calculation of the cargo category in the waybill). The standard deviation reflects the path fluctuation risk.

[0108] Prioritize the delivery of high-value cargo and stable-route nodes, significantly reducing the transportation risk and economic losses of high-value cargo (such as avoiding sections with large speed fluctuations for valuables). By balancing route stability and cargo value, the overall transportation efficiency of vehicles can be improved.

[0109] S204, obtaining the vehicle density MD of each connection path in the real-time road condition i and the passing speed NP i , substitute the formula to calculate the risk factor FX of each connection path i :

[0110]

[0111] Where γ and β are constants greater than 1; the minimum risk coefficient is taken as the second coefficient XS between the starting point q and the delivery point w qw ; Similarly, the first coefficient and the second coefficient between each delivery point are calculated respectively.

[0112] The second coefficient is used to evaluate real-time risk dynamics, analyze real-time traffic conditions, jointly determine real-time risks based on the nonlinear impact correlation between traffic density and travel speed, and screen the feasible path with the lowest risk in the current period (such as avoiding congested or accident sections).

[0113] Reduce the probability of delays and accidents caused by unexpected road conditions during transportation (such as avoiding temporary construction areas). Respond to changes in the traffic environment within seconds and improve the real-time performance of route planning.

[0114] S300 includes:

[0115] S301, obtaining the first coefficient XF between the starting point and each delivery point u and the second coefficient XS u , substitute into the formula to calculate the third coefficient XT for each delivery point u :

[0116]

[0117] In the formula, XS ave It is the average value of the second coefficient of all delivery points in the matching set.

[0118] The third coefficient combines economic efficiency (the first coefficient) with real-time risk (the second coefficient) by weight, and normalizes it based on the average risk of the batch. Dynamically generate a priority queue for delivery nodes: sort them from high to low according to the third coefficient, and set intermediate points one by one until all nodes are covered.

[0119] Through the coordinated optimization of the three coefficients, the maximum return on cargo value, the minimum risk and the optimization of path efficiency can be achieved at the same time.

[0120] S302, set the delivery point with the largest third coefficient as the middle point Z1, calculate the third coefficient between Z1 and each delivery point in its matching set, and select the delivery point with the largest third coefficient as the middle point Z2.

[0121] S303, delete the delivery points that have been set as the middle point in the Z2 matching set, calculate the third coefficient between Z2 and the remaining delivery points in its matching set, and select the delivery point with the largest third coefficient to set as the middle point Z3.

[0122] S304, and so on, until all delivery points are set as intermediate points. The starting point is placed first, and all intermediate points are arranged in sequence according to the set time sequence, and the starting point and the intermediate point or adjacent intermediate points are connected using the path corresponding to the minimum risk coefficient.

[0123] S305: After all paths are connected, they are used as the transportation route. The truck travels along the transportation route and stops at each intermediate point to unload the goods. During the driving process, the third coefficient of the remaining intermediate points is dynamically calculated according to the real-time road conditions, and the transportation route is updated in real time by the third coefficient.

[0124] By integrating historical efficiency and real-time risks, we can generate the global optimal delivery sequence that takes into account both economy and safety (e.g., high-value goods are delivered first during low-risk periods). We can also support real-time triggering of route adjustments during transportation (e.g., re-planning of accident sections) to ensure the resilience of the overall logistics network.

[0125] In S400, the latest transportation route is displayed in real time through the on-board visual interface, and the truck driver drives according to the latest transportation route. During the driving process, the speed of the truck at each road position is regularly collected and stored in the corresponding status record.

[0126] See also Figure 2 The present invention provides an intelligent logistics monitoring system based on big data, including a data acquisition module, a logistics analysis module, a monitoring management module and a visualization module.

[0127] The data collection module is used to collect traffic data and logistics logs, as well as the transportation details.

[0128] The logistics analysis module is used to analyze the transportation details and mark the points, and calculate the first coefficient and the second coefficient between the points.

[0129] The monitoring and management module is used to calculate the third coefficient based on the first coefficient and the second coefficient, and to set the intermediate points in sequence according to the third coefficient, so as to plan the transportation route and adjust it dynamically.

[0130] The visualization module is used to display the transportation route through a visualization interface.

[0131] The data collection module includes a transportation information collection unit, a logistics log collection unit and a traffic data collection unit.

[0132] The transport information collection unit is used to collect the starting location of the truck and the shipping list, which includes the transport purpose and freight value of each cargo.

[0133] Shipping list parsing uses visual OCR combined with NLP to structure the waybill, while identifying key fields such as the address bar, product category, and freight value.

[0134] The logistics log collection unit is used to collect the status records of all trucks. Each truck corresponds to a status record, including the speed of the truck at each road location.

[0135] The vehicle-mounted IoT device reports GPS coordinates and speed data at a fixed frequency, and the edge gateway performs timing alignment and abnormal data cleaning. A lightweight communication protocol based on MQTT-SN is designed to support data breakpoint continuation in intermittent network environments, ensuring data packet integrity and orderliness.

[0136] The traffic data collection unit is used to collect route maps and real-time traffic conditions. The route map is a plan view of the road on which vehicles travel. The real-time traffic conditions include the real-time traffic volume of each road, specifically the vehicle density and traffic speed.

[0137] The RESTful API is used to connect to the real-time traffic data of the traffic management department and the navigation platform, analyze the vehicle density and traffic speed, and use the WGS84 to GCJ-02 coordinate system conversion to ensure the geospatial consistency of multi-source data.

[0138] The logistics analysis module includes a point marking unit and a coefficient analysis unit.

[0139] The point marking unit is used to mark points and plan connection paths.

[0140] First, all goods are classified according to whether they have the same transportation purpose. The starting point of the truck is marked on the route map, and the delivery point is marked according to the transportation purpose of each category.

[0141] The sum of the freight values ​​of all goods of each category is taken as the value of the corresponding delivery point.

[0142] Secondly, a matching set is established for the starting point and each delivery point respectively. All delivery points are put into the matching set of the starting point, and all other delivery points except the starting point are put into the matching set of the delivery point.

[0143] Finally, in the route map, the starting point q is used as the departure location, and the delivery point w in the matching set of q is used as the arrival location, and all connection paths between the two are planned.

[0144] The coefficient analysis unit is used to calculate the first coefficient and the second coefficient between the points.

[0145] The first coefficient calculation includes:

[0146] First, all state records are analyzed and the speeds are marked at the corresponding road locations in the route map.

[0147] Secondly, the standard deviation SD of all speeds within each connection path is calculated i and the average SP i , and the total value VAL of all delivery points sum .

[0148] Finally, get the length CD of each connection path i , according to the formula: Calculate the first coefficient XF between the starting point q and the delivery point w qw .

[0149] Among them, VAL w is the value of the delivery point w, j is the number of all connection paths, and α is a constant greater than 0.

[0150] The second coefficient calculation includes:

[0151] First, obtain the vehicle density MD of each connection path in the real-time traffic conditions i and the passing speed NP i .

[0152] Secondly, according to the formula: Calculate the risk factor FX for each connection path i ; Among them, γ and β are constants greater than 1.

[0153] Finally, the minimum risk factor is taken as the second coefficient XS between the starting point q and the delivery point w qw .

[0154] In this way, the first coefficient and the second coefficient between each delivery point are calculated respectively.

[0155] The monitoring management module includes a route planning unit and a dynamic adjustment unit.

[0156] The route planning unit is used to plan the transportation routes of trucks.

[0157] First, obtain the first coefficient XF between the starting point and each delivery point u and the second coefficient XS u .

[0158] According to the formula: Calculate the third coefficient XT for each delivery point u Among them, XS ave It is the average value of the second coefficient of all delivery points in the matching set.

[0159] Secondly, the delivery point with the largest third coefficient is set as the middle point Z1, the third coefficient between Z1 and each delivery point in its matching set is calculated, and the delivery point with the largest third coefficient is selected as the middle point Z2.

[0160] Delete the delivery points that have been set as the middle point in the Z2 matching set, calculate the third coefficient between Z2 and the remaining delivery points in its matching set, and select the delivery point with the largest third coefficient as the middle point Z3.

[0161] Finally, this process is repeated until all delivery points are set as intermediate points. The starting point is placed first, and all intermediate points are arranged in the order of the set time.

[0162] The starting point and the intermediate point or adjacent intermediate points are connected by the path corresponding to the minimum risk coefficient, and all paths are connected as transportation routes.

[0163] The dynamic adjustment unit is used to update the transportation routes in real time.

[0164] The truck stops at each intermediate point to unload the goods in the process of driving along the transportation route. The third coefficient of the remaining intermediate points is dynamically calculated according to the real-time road conditions during driving, and the transportation route is updated in real time through the third coefficient.

[0165] The visualization module displays the latest transportation route through the on-board visualization interface, collects the speed of the truck at each road location and stores it in the corresponding status record.

[0166] Embodiment 1:

[0167] Assume there is a starting point A1, and delivery points B1, B2, and B3, the first coefficient and the second coefficient between them are:

[0168] A1-B1: first coefficient: 1.2; second coefficient: 2.4;

[0169] A1-B2: first coefficient: 1.5; second coefficient: 3.6;

[0170] A1-B3: first coefficient: 1.3; second coefficient: 2.7;

[0171] The average value of the second coefficient of all delivery points is 2.9. Substitute the formula to calculate the third coefficient of each delivery point:

[0172] B1 third coefficient:

[0173] B2 third coefficient:

[0174] B3 third coefficient:

[0175] Select delivery point B1 as the middle point.

[0176] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0177] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent logistics monitoring method based on big data, characterized by: The method includes: S100, collect traffic data and logistics logs, as well as the transportation details; S200, analyzing the transportation details and marking the points, and calculating the coefficients between the points; S300, setting the intermediate points in sequence according to the coefficients, thereby planning the transportation route and dynamically adjusting it; S400, the visual interface displays the transport route, records the driving data and stores it in the logistics log.

2. The intelligent logistics monitoring method based on big data according to claim 1 is characterized by: In S100, traffic data includes route maps and real-time traffic conditions; the route map is a plan view of the road on which vehicles travel; the real-time traffic conditions include the real-time traffic volume of each road, specifically the vehicle density and traffic speed; the logistics log includes the status records of all trucks, and each truck corresponds to a status record, including the speed of the truck at each road location; The transport details include the truck's origin location and a shipping manifest that includes the purpose and value of each shipment.

3. The intelligent logistics monitoring method based on big data according to claim 2 is characterized in that: S200 includes: S201, classify all goods according to whether they have the same transportation purpose; mark the starting point of the truck on the route map, mark the delivery point according to the transportation purpose of each category, and the sum of the freight values ​​of all goods in each category is used as the value of the corresponding delivery point; S202, establish matching sets for the starting point and each delivery point respectively, put all the delivery points in the matching set of the starting point, and put all the other delivery points except itself in the set of the delivery point; in the route map, take the starting point q as the departure position, and the delivery point w in the matching set of q as the arrival position, and plan all the connection paths between the two; S203, analyzing all status records, marking the speed at the corresponding road position in the route map; calculating the standard deviation SD of all speeds in each connection path i and the average SP i , and the total value VAL of all delivery points sum ; Get the length CD of each connection path i , substitute into the formula to calculate the first coefficient XF between the starting point q and the delivery point w qw : Where VAL w is the value of the delivery point w, j is the number of all connection paths, and α is a constant greater than 0; S204, obtaining the vehicle density MD of each connection path in the real-time road condition i and the passing speed NP i , substitute the formula to calculate the risk factor FX of each connection path i : Where γ and β are constants greater than 1; the minimum risk coefficient is taken as the second coefficient XS between the starting point q and the delivery point w qw ; Similarly, the first coefficient and the second coefficient between each delivery point are calculated respectively.

4. The intelligent logistics monitoring method based on big data according to claim 3 is characterized by: S300 includes: S301, obtaining the first coefficient XF between the starting point and each delivery point u and the second coefficient XS u , substitute into the formula to calculate the third coefficient XT for each delivery point u : In the formula, XS ave is the average of the second coefficients of all delivery points in the matching set; S302, setting the delivery point with the largest third coefficient as the middle point Z1, calculating the third coefficient between Z1 and each delivery point in the matching set, and selecting the delivery point with the largest third coefficient as the middle point Z2; S303, deleting the delivery points that have been set as the middle point in the matching set Z2, calculating the third coefficient between Z2 and the remaining delivery points in the matching set, and selecting the delivery point with the largest third coefficient as the middle point Z3; S304, and so on, until all delivery points are set as intermediate points; the starting point is placed first, and all intermediate points are arranged in sequence according to the set time sequence, and the starting point and the intermediate point or adjacent intermediate points are connected by a path corresponding to the minimum risk coefficient; S305. After all paths are connected, they are used as transportation routes. The truck travels along the transportation route and stops at each intermediate point to unload the goods in turn. During the driving process, the third coefficients of the remaining intermediate points are dynamically calculated according to the real-time road conditions, and the transportation route is updated in real time using the third coefficients.

5. The intelligent logistics monitoring method based on big data according to claim 4 is characterized in that: In S400, the latest transportation route is displayed in real time through the on-board visual interface, and the truck driver drives according to the latest transportation route; During driving, the speed of the truck at each road position is collected regularly and stored in the corresponding status record.

6. Intelligent logistics monitoring system based on big data, characterized by: The system includes data acquisition module, logistics analysis module, monitoring management module and visualization module; The data collection module is used to collect traffic data and logistics logs, as well as the details of this transportation; The logistics analysis module is used to analyze the transportation details and mark the points, and calculate the first coefficient and the second coefficient between the points; The monitoring management module is used to calculate the third coefficient according to the first coefficient and the second coefficient, and to set the intermediate points in sequence according to the third coefficient, so as to plan the transportation route and adjust it dynamically; The visualization module is used to display the transportation route through a visualization interface.

7. The intelligent logistics monitoring system based on big data according to claim 6 is characterized by: The data collection module includes a transportation information collection unit, a logistics log collection unit, and a traffic data collection unit; The transport information collection unit is used to collect the starting location of the truck and the shipping list, which includes the transportation purpose and freight value of each cargo; The logistics log collection unit is used to collect the status records of all trucks. Each truck corresponds to a status record, including the speed of the truck at each road location; The traffic data collection unit is used to collect route maps and real-time traffic conditions; the route map is a plan view of the road on which vehicles travel; the real-time traffic conditions include the real-time traffic volume of each road, specifically the vehicle density and travel speed.

8. The intelligent logistics monitoring system based on big data according to claim 7 is characterized in that: The logistics analysis module includes a point marking unit and a coefficient analysis unit; The point marking unit is used to mark points and plan connection paths; First, all goods are classified according to whether they have the same transportation purpose; the starting point of the truck is marked on the route map, and the delivery point is marked according to the transportation purpose of each category; The sum of the freight values ​​of all goods of each category shall be taken as the value of the corresponding delivery point; Secondly, create matching sets for the starting point and each delivery point respectively. The starting point's matching set contains all the delivery points, and the delivery point's set contains all the other delivery points except itself. Finally, in the route map, take the starting point q as the departure location, and the delivery point w in the matching set of q as the arrival location, and plan all the connecting paths between the two; The coefficient analysis unit is used to calculate the first coefficient and the second coefficient between the points; The first coefficient calculation includes: First, all status records are analyzed and the speeds are marked at the corresponding road locations in the route map; Secondly, the standard deviation SD of all speeds within each connection path is calculated i and the average SP i , and the total value VAL of all delivery points sum ; Finally, get the length CD of each connection path i , according to the formula: Calculate the first coefficient XF between the starting point q and the delivery point w qw ; Among them, VAL w is the value of the delivery point w, j is the number of all connection paths, and α is a constant greater than 0; The second coefficient calculation includes: First, obtain the vehicle density MD of each connection path in the real-time traffic conditions i and the passing speed NP i ; Secondly, according to the formula: Calculate the risk factor FX for each connection path i ; Wherein, γ and β are constants greater than 1; Finally, the minimum risk factor is taken as the second coefficient XS between the starting point q and the delivery point w qw ; In this way, the first coefficient and the second coefficient between each delivery point are calculated respectively.

9. The intelligent logistics monitoring system based on big data according to claim 8 is characterized by: The monitoring and management module includes a route planning unit and a dynamic adjustment unit; The route planning unit is used to plan the transportation route of the truck; First, obtain the first coefficient XF between the starting point and each delivery point u and the second coefficient XS u ; According to the formula: Calculate the third coefficient XT for each delivery point u Among them, XS ave is the average of the second coefficients of all delivery points in the matching set; Secondly, the delivery point with the largest third coefficient is set as the middle point Z1, the third coefficient between Z1 and each delivery point in its matching set is calculated, and the delivery point with the largest third coefficient is selected as the middle point Z2; Delete the delivery points that have been set as the middle point in the Z2 matching set, calculate the third coefficient between Z2 and the remaining delivery points in its matching set, and select the delivery point with the largest third coefficient as the middle point Z3; Finally, this process is repeated until all delivery points are set as intermediate points. The starting point is placed first, and all intermediate points are arranged in the order of the set time. The starting point and the intermediate point or adjacent intermediate points are connected by the path corresponding to the minimum risk coefficient, and all the paths are connected as the transportation route; The dynamic adjustment unit is used to update the transportation route in real time; When the truck is traveling along the transport route, it stops at each intermediate point to unload the goods in turn; when driving, the third coefficient of the remaining intermediate points is dynamically calculated according to the real-time road conditions, and the transport route is updated in real time using the third coefficient.

10. The intelligent logistics monitoring system based on big data according to claim 9 is characterized in that: The visualization module displays the latest transportation route through the on-board visualization interface, collects the speed of the truck at each road location and stores it in the corresponding status record.

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