Goods motion trail safe positioning port logistics distribution method

Abstract

The invention relates to a cargo motion trail safe positioning port logistics distribution method, and the method comprises the steps: obtaining a building laser point cloud picture and a building plane coordinate picture of a fixed building in a port, forming a truck transportation priori motion trail, obtaining the position point overlapping and crossing information of each position point in the port plane coordinate map, and carrying out the positioning of the position point; once the real-time position of the truck is not successfully acquired and the truck is judged to be in the building, the positioning information of the truck in the building in the building laser point cloud picture obtained based on the acquired information of the WIFI signal released by the truck and the building laser point cloud picture is used as the current real-time position of the truck; once it is judged that the truck is located outside the building area, the next position point of the truck at the current position point is predicted according to the overlapping and crossing information of all the position points and the latest truck position point coordinate data and serves as the current real-time position of the truck; even if lorry position information cannot be accurately collected in a port area, lorry position positioning can be completed based on the WIFI signals, and the motion trail of goods can be mastered.

Description

Technical Field

[0001] The invention relates to the field of logistics, and in particular to a method for safely positioning a cargo movement trajectory in a port logistics distribution. Background Art

[0002] As an important form of logistics among various logistics distribution methods, port logistics distribution plays an important role in cargo transportation. Specifically, port logistics refers to the central port city using its own port advantages, relying on advanced software and hardware environment, strengthening its radiation capacity to the logistics activities around the port, highlighting the port's specialties in cargo collection, storage, and distribution, based on the port industry, supported by information technology, and aiming to optimize port resource integration, to develop a port comprehensive service system with characteristics covering all links of the logistics industry chain. Port logistics is a comprehensive logistics system in a special form. It is an irreplaceable and important node in the logistics process, completing the basic logistics services and derived value-added services in the entire supply chain logistics system.

[0003] However, the existing port logistics distribution method has shortcomings: when trucks transporting goods within the port area, due to the influence of buildings or other factors in the port area, the positioning information of the trucks will be untimely and inaccurate, resulting in the inability to accurately obtain the movement trajectory of the trucks within the port area, and it is also impossible to know the movement trajectory of the goods on the trucks; not only that, when trucks transport goods from the port area to the outside, they will also be unable to accurately locate the movement trajectory of the trucks and the goods they carry due to poor positioning information, which has a certain adverse impact on the safety of cargo transportation. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for safely locating the movement trajectory of goods in a port logistics distribution, which can timely and accurately locate the movement trajectory of goods in a port area, in view of the above-mentioned prior art.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for safely positioning the cargo movement trajectory in port logistics distribution, characterized in that it includes the following steps:

[0006] Step 1, pre-acquire a port laser point cloud map and a port plane coordinate map of the port area; wherein the port laser point cloud map is formed by port laser point cloud data obtained by scanning the port area with a laser, and the port plane coordinate map includes two-dimensional coordinates of each position in the port;

[0007] Step 2, filtering and processing the port laser point cloud map and the port plane coordinate map respectively, and obtaining the corresponding building laser point cloud map and building plane coordinate map of the fixed buildings in the port; wherein the fixed buildings in the port include truck-accessible buildings that trucks can drive in and out of and truck-inaccessible buildings that trucks cannot drive in and out of;

[0008] Step 3, in advance, within a preset test time period, the motion trajectories of each truck transporting goods when traveling in the port area are collected, and the truck transport prior motion sub-trajectories of each truck are obtained respectively, and the truck transport prior motion sub-trajectories are used to form a truck transport prior motion trajectory that represents the characteristics of the truck trajectory in the port; wherein the truck transport prior motion sub-trajectories are formed by a number of position point information of the truck transporting goods when traveling in the port area, and the position point information includes the three-dimensional coordinate values ​​of the truck at different position points and the appearance time of the truck at the corresponding position point; the trucks within the preset test time period perform normal cargo transportation according to their respective transportation routes;

[0009] Step 4, according to the port plane coordinate map and the obtained truck transport prior motion trajectory, obtain the position point overlap and intersection information when each position point in the port plane coordinate map overlaps and intersects with the truck transport prior motion trajectory; wherein the position point overlap and intersection information includes the three-dimensional coordinates of the position point, the overlap time when the position point overlaps with the truck transport prior motion trajectory, and the intersection probability of the position point overlapping and intersecting with the truck transport prior motion trajectory at the overlap time; the intersection probability is the ratio of the number of overlaps and intersections between the position point and the truck transport prior motion trajectory at the overlap time to the total number of overlaps and intersections between all position points on the port plane coordinate map and the truck transport prior motion trajectory at the overlap time;

[0010] Step 5, collecting the real-time location coordinate data of the truck carrying the goods when transporting in the port area to form a real-time movement trajectory of the truck that can characterize the movement trajectory of the goods; wherein, when the truck enters the port, the port management party installs a unified WIFI signal release device that releases WIFI signals to the outside;

[0011] Step 6: Perform real-time detection on the collection status of the real-time location point coordinate data of the truck, and make a judgment based on the detection result of the collection status:

[0012] When the real-time location coordinate data of the truck is collected, go to step 5; otherwise, go to step 7;

[0013] Step 7, making a judgment based on the latest collected truck location point coordinate data of the truck before the current collection state and the obtained building plane coordinate diagram of the fixed building in the port:

[0014] When the coordinate data of the truck location point of the truck coincides with any coordinate data in the plane coordinate diagram of the building, proceed to step 8; otherwise, proceed to step 9;

[0015] Step 8, starting WIFI signal information collection for the WIFI signal released by the cargo for the building corresponding to the plane coordinate map of the building whose coordinate data overlaps with the coordinate data of the truck location point of the truck, and processing the truck's in-building positioning information in the laser point cloud map of the building according to the collected WIFI signal information and the obtained building laser point cloud map, and using the obtained in-building positioning information as the current real-time position information of the truck; wherein the WIFI signal information includes WIFI signal strength;

[0016] Step 9, make a prediction based on the overlapped intersection information of the location points in the obtained port plane coordinate map and the coordinate data of the truck location point most recently collected before the current collection state, predict the location point coordinate data of the next location point of the truck at the current location point, and use the predicted location point coordinate data as the current real-time location information of the truck.

[0017] Improved, in the cargo movement trajectory safe positioning port logistics distribution method, in step 8, the calculation process of the positioning information of the truck in the building in the building laser point cloud map includes the following steps:

[0018] Step a1, constructing a WIFI location fingerprint database in the building based on the WIFI signal strength for each truck-enterable building in the port area; wherein the WIFI location fingerprint database in the building corresponds to the truck-enterable building one by one, and the WIFI location fingerprint database in the building is formed by a plurality of WIFI location fingerprint data, and the WIFI location fingerprint data includes the coordinate data of the location point in the building and the WIFI signal strength value of the WIFI signal released when the truck just enters the entrance of the building that can be detected at the location point;

[0019] Step a2, processing is performed based on the collected WIFI signal information and the WIFI location fingerprint database in the building corresponding to the building plane coordinate map whose coordinate data overlaps with the truck location point coordinate data of the truck:

[0020] When the WIFI signal information has the same WIFI signal strength as any WIFI location fingerprint data in the WIFI location fingerprint database in the building, proceed to step a3; otherwise, proceed to step a4;

[0021] Step a3, obtaining the location point coordinate data of any WIFI location fingerprint data, and using the obtained location point coordinate data as the location information of the truck in the building in the laser point cloud map of the building;

[0022] Step a4, selecting target WIFI location fingerprint data that matches the collected WIFI signal information from the WIFI location fingerprint database in the building, and using the location point coordinate data of the target WIFI location fingerprint data as the building positioning information of the truck in the building laser point cloud map.

[0023] Furthermore, in the cargo movement trajectory secure positioning port logistics distribution method, the selection process of the target WIFI location fingerprint data includes the following steps:

[0024] Step a41, respectively obtaining the WIFI signal strength value of each WIFI location fingerprint data in the WIFI location fingerprint database in the building;

[0025] Step a42, forming a WIFI signal strength sequence by taking all WIFI signal strength values ​​of the acquired WIFI location fingerprint database in the building in the order of the WIFI signal strength values ​​from small to large;

[0026] Step a43, taking the first WIFI signal strength value in the WIFI signal strength sequence and greater than the WIFI signal strength in the collected WIFI signal information as the WIFI signal strength closest to the actual WIFI signal strength released by the truck;

[0027] Step a44, using the coordinate data of the location point of the WIFI location fingerprint data to which the first WIFI signal strength value belongs as the preliminary positioning information of the truck in the building in the building laser point cloud map;

[0028] Step a45, obtaining the coordinate data of a previous position point corresponding to the preliminary positioning information in the building and the coordinate data of a subsequent position point corresponding to the preliminary positioning information in the building from the WIFI position fingerprint database in the building, and using the position point corresponding to the obtained coordinate data of the previous position point as the front position point to be measured and the position point corresponding to the coordinate data of the subsequent position point as the rear position point to be measured;

[0029] Step a46, making a judgment based on the building laser point cloud image and the obtained front position point to be measured and the rear position point to be measured:

[0030] When the front position point to be measured and the rear position point to be measured have matching position point coordinate data with the corresponding position points in the building laser point cloud map, it is determined that the preliminary positioning information inside the building is the positioning information of the truck inside the building in the building laser point cloud map; otherwise, the position point coordinate data of the rear position point to be measured is used as the positioning information of the truck inside the building in the building laser point cloud map.

[0031] Further improved, in the cargo movement trajectory safe positioning port logistics distribution method, in step 9, the process of predicting the coordinate data of the next location point of the truck at the current location point includes the following steps:

[0032] Step b1, based on the overlapped intersection information of the position points in the obtained port plane coordinate map and the coordinate data of the truck position points collected the most recently before the current collection state, obtain all the neighbor position points located in the port plane coordinate map and close to the latest position of the truck; wherein the number of neighbor position points close to the latest position of the truck is marked as M, and the mth neighbor position point among the M neighbor position points is marked as D m , neighbor location point D m The location point coordinate data is marked as 1≤m≤M;

[0033] D m :||D m -E new ||≤R;

[0034] Among them, E new is the truck location point that was collected the most recently before the current collection state. Truck location point E new The coordinate data of the truck position point is marked as R is the preset position point spacing threshold;

[0035] Step b2, according to each neighbor position point obtained and the port plane coordinate map, respectively obtain the historical passing probability of the current truck passing each neighbor position point in the preset test time period, and select the maximum historical passing probability from all the historical passing probabilities; wherein, the current truck passes the neighbor position point D m The historical probability of driving

[0036]

[0037] in, The current truck passes the neighboring point D within the preset test time period m The total number of times;

[0038] Step b3, according to the obtained overlapping cross information of each neighbor position point and the position point in the port plane coordinate map, the cross probability corresponding to each neighbor position point is matched respectively, and all the cross probabilities obtained by matching are combined to form a cross probability sequence representing the neighbor position point; wherein, the neighbor position point D m The crossover probability is marked as

[0039] Step b4, selecting the maximum crossing probability in the obtained crossing probability sequence, and comparing the maximum crossing probability with the maximum historical passing probability of the current truck to determine:

[0040] When the maximum crossing probability is greater than or equal to the maximum historical passing probability of the current truck, the neighbor position point coordinate data corresponding to the maximum crossing probability is used as the position point coordinate data of the next position point of the truck at the current position point; otherwise, the neighbor position point coordinate data corresponding to the maximum historical passing probability of the current truck is used as the position point coordinate data of the next position point of the truck at the current position point.

[0041] Improved, in the invention, the cargo movement trajectory safe positioning port logistics distribution method also includes a process of positioning the cargo movement trajectory of the cargo shipped out of the port area.

[0042] Furthermore, in the cargo movement trajectory safe positioning port logistics distribution method, the process of positioning the cargo movement trajectory of the cargo shipped out of the port area includes the following steps:

[0043] Step c1, classifying the logistics companies to which each truck entering and leaving the port area belongs, and obtaining a truck-company list representing the relationship between the truck and the logistics company to which it belongs; wherein in the truck-company list, each truck entering and leaving the port area corresponds to the logistics company to which it belongs;

[0044] Step c2, based on the obtained truck-company list, count the sequence of common path points that different trucks under each logistics company have passed in sequence when transporting goods on routes with the same starting point and the same end point;

[0045] Step c3, based on the obtained common path point sequences and the starting point and end point of the routes corresponding to the common path point sequences, respectively establish a list of transportation route path points when each logistics company executes a cargo transportation route with different starting point-end point pair restrictions; wherein the list of transportation route path points includes multiple transportation routes and common path points corresponding to each transportation route, and the common path points include the starting point and end point of the transportation route; different transportation routes correspond to different starting point-end point pairs;

[0046] Step c4, drawing the transport route curves corresponding to each start-end point pair according to the transport route path point list, and using the drawn transport route curves as transport route reference trajectories corresponding to the start-end point pair;

[0047] Step c5, real-time detection of the acquisition status of the real-time location point coordinate data of the truck outside the port area, and making a judgment based on the detection result of the acquisition status:

[0048] When the real-time location coordinate data of the truck is collected, the real-time location coordinate data of the truck carrying the goods when transporting outside the port area is collected to form a real-time movement trajectory curve of the truck that can represent the movement trajectory of the goods outside the port area; otherwise, proceed to step c6;

[0049] Step c6, obtaining the latest current track curve of the truck collected from the time the truck left the port area and drove to the current collection state, and using the obtained current track curve of the truck as a track prediction curve for predicting the current position of the truck;

[0050] Step c7, taking the position point corresponding to the coordinate data of the real-time position point of the truck collected the most recently before the current collection state as the trajectory prediction point for predicting the current position of the truck, and obtaining the previous position point of the trajectory prediction point according to the order of the truck passing by the truck on the truck trajectory prediction curve;

[0051] Step c8, respectively calculating the first slope value of the truck trajectory prediction curve at the previous position point and the second slope value at the trajectory prediction point, and making a judgment based on the first slope value and the second slope value:

[0052] When the product of the first slope value and the second slope value is a positive number, the process goes to step c9; otherwise, the process goes to step c10;

[0053] Step c9, obtaining the average transport speed of the truck during its travel along the truck trajectory prediction curve, calculating the product of the average transport speed and the time interval between the current moment and the trajectory prediction point position acquisition moment, taking the product as the predicted mileage of the truck, and taking the position in the direction of the truck trajectory prediction curve and the distance from the trajectory prediction point to the predicted mileage as the current position of the truck, and adding the current position of the truck to the cargo movement trajectory;

[0054] Step c10, selecting a transportation route reference trajectory having the same path points as the truck trajectory prediction curve from among all the obtained transportation route reference trajectories, and taking the transportation route reference trajectory as a preferred transportation route reference trajectory;

[0055] Step c11, taking the next path point located in the reference trajectory of the preferred transportation route and being the predicted point of the trajectory as the current position of the truck, and adding the current position of the truck to the cargo movement trajectory.

[0056] Compared with the prior art, the advantages of the present invention are: the cargo movement trajectory safe positioning port logistics distribution method of the invention obtains the building laser point cloud map and the building plane coordinate map of the fixed buildings in the port by screening and processing the port laser point cloud map and the port plane coordinate map of the port area, and forms the truck transportation prior motion trajectory based on the truck transportation prior motion sub-trajectory of all trucks in the port area during the preset test time period, and then obtains the position point overlap and intersection information when each position point in the port plane coordinate map overlaps and intersects with the truck transportation prior motion trajectory according to the port plane coordinate map and the truck transportation prior motion trajectory, and performs positioning status collection for the truck entering the port area. Once the truck position data is not successfully collected and it is determined that the truck is located in the building, the truck releases the WIFI signal information and the building is collected. The laser point cloud map of the object obtains the positioning information of the truck in the building in the laser point cloud map of the building, and the obtained positioning information in the building is used as the current real-time position information of the truck; once it is determined that the truck is outside the building area, a prediction is made based on the overlap and intersection information of the position points in the obtained port plane coordinate map and the coordinate data of the truck position points most recently collected before the current collection state, and the position point coordinate data of the next position point of the truck at the current position point is obtained, and the predicted position point coordinate data is used as the current real-time position information of the truck, and the real-time position information of the truck is used as the real-time position information of the goods carried by the truck. Even if the truck position information cannot be accurately collected in the port area, the truck position can be positioned based on the WIFI signal information to accurately grasp the movement trajectory of the goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a flow chart of a method for safely positioning the movement trajectory of goods in a port logistics distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below with reference to the accompanying drawings.

[0059] This embodiment provides a method for safely positioning the cargo movement trajectory in port logistics distribution. Figure 1 As shown, the cargo movement trajectory safe positioning port logistics distribution method includes the following steps:

[0060] Step 1, pre-acquire a port laser point cloud map and a port plane coordinate map of the port area; wherein the port laser point cloud map is formed by the port laser point cloud data obtained by laser scanning the port area, and the port plane coordinate map includes the two-dimensional coordinates of each position in the port; of course, the two-dimensional coordinates of each building area in the port area are also located in the port plane coordinate map; the laser scanning method of the port laser point cloud data can be a conventional laser scanning method;

[0061] Step 2, filtering and processing the port laser point cloud map and the port plane coordinate map respectively, and obtaining the corresponding building laser point cloud map and building plane coordinate map of the fixed buildings in the port; wherein the fixed buildings in the port include truck-accessible buildings that trucks can drive in and out of and truck-inaccessible buildings that trucks cannot drive in and out of; the truck-accessible buildings here refer to buildings that meet the conditions for truck access passages; the truck-inaccessible buildings refer to buildings that do not meet the conditions for truck access passages;

[0062] Step 3, in advance, the motion trajectories of each truck transporting goods while traveling in the port area are collected within a preset test time period, and the truck transport prior motion sub-trajectories of each truck are obtained respectively, and the truck transport prior motion trajectory representing the characteristics of the truck trajectory in the port is formed by all the truck transport prior motion sub-trajectories;

[0063] Among them, the truck transport prior motion sub-trajectory is formed by a number of position point information of the truck transporting goods when it travels in the port area, and the position point information includes the three-dimensional coordinate values ​​of the truck at different position points and the appearance time of the truck at the corresponding position point; the trucks in the preset test time period perform normal cargo transportation according to their respective transportation routes; it should be noted here that, considering the situation that there are different layers of ground in the height direction in the port area, the position point information here in this embodiment needs to be set as the three-dimensional coordinates of the position point; the preset test time period can be half a year or a year after the port area is officially built and put into operation, or other longer time periods;

[0064] Step 4, according to the port plane coordinate map and the obtained truck transport prior motion trajectory, obtain the position point overlap and intersection information when each position point in the port plane coordinate map overlaps and intersects with the truck transport prior motion trajectory;

[0065] The location point overlap intersection information includes the three-dimensional coordinates of the location point, the overlap time when the location point overlaps with the prior motion trajectory of the truck transportation, and the intersection probability of the location point overlapping and intersecting with the prior motion trajectory of the truck transportation at the overlap time;

[0066] The intersection probability is the ratio of the number of intersections between the location point and the priori motion trajectory of the truck at the time of overlap to the total number of intersections between all location points (including the location point) on the port plane coordinate map and the priori motion trajectory of the truck at the time of overlap;

[0067] For example, since each truck transport prior motion sub-trajectory in the obtained truck transport prior motion trajectory is the corresponding position point and the time value of the cargo passing the position point, therefore, in the process of processing the port plane coordinate map, once a certain position point Q1 on the port plane coordinate map is passed by a truck corresponding to any truck transport prior motion sub-trajectory, it is necessary to obtain the number of times the position point Q1 is passed by the truck at the time t; of course, at the time t, the position point Q1 here may also be passed by other trucks at the same time, so it is also necessary to count the number of times the position point Q1 is passed by other trucks at the time t; each time the position point Q1 is passed by any truck at the time t, it indicates that the position point Q1 overlaps and intersects with the truck transport prior motion trajectory once at the overlap time t; assuming that according to statistics, the total number of times the position point Q1 overlaps and intersects with the truck transport prior motion trajectory at the time t is marked as n Q1 Assuming that the total number of all the location points on the port plane map is M', the total number of times all the location points on the port plane coordinate map (including the location point Q1) overlap and intersect with the prior motion trajectory of truck transportation at the overlap time t is marked as N Q , N Q =n Q1 +n Q2 +…+n QM’ ; Then, the crossing probability of position point Q1 at the coincidence time t is n Q1 / N Q ;

[0068] Of course, according to the need to locate the movement trajectory of the truck and the goods carried by the truck, the coordinate accuracy of each position point here can be adjusted as needed to meet the needs of the length of the truck, the width of the truck and the size of the plane area occupied by the buildings in the port area;

[0069] Step 5, collecting the real-time location coordinate data of the truck carrying the goods when transporting in the port area to form a real-time movement trajectory of the truck that can characterize the movement trajectory of the goods; wherein, when the truck enters the port, the port management party installs a unified WIFI signal release device that releases WIFI signals to the outside; that is, as soon as the truck enters the port, the port management party will install the WIFI signal release device on the truck after necessary verification and permission to enter the port;

[0070] That is to say, once a truck enters the port area, it is required to install a WIFI signal release device of uniform standards and models, and the initial power of each WIFI signal release device should be uniform and fully loaded (when it is just installed on the truck, the remaining power is displayed as 100%);

[0071] Step 6: Perform real-time detection on the collection status of the real-time location point coordinate data of the truck, and make a judgment based on the detection result of the collection status:

[0072] When the coordinate data of the real-time location point of the truck is collected, it indicates that the collection of the positioning signal of the truck when it is traveling in the port area is normal, and the process goes to step 5; otherwise, it indicates that the collection of the positioning signal of the truck when it is traveling in the port area is abnormal, and corresponding measures need to be taken, and the process goes to step 7;

[0073] Step 7, making a judgment based on the latest collected truck location point coordinate data of the truck before the current collection state and the obtained building plane coordinate diagram of the fixed building in the port:

[0074] When the coordinate data of the truck location point of the truck coincides with any coordinate data in the plane coordinate diagram of the building, proceed to step 8; otherwise, proceed to step 9;

[0075] It should be noted that in step 6, when it is determined that the real-time location coordinate data of the truck is not collected normally, it means that the real-time location coordinate data of the truck was collected normally at the previous moment of the collection (i.e., the data collection action in which the real-time location coordinate data of the goods is not collected normally). Therefore, the real-time location coordinate data of the truck collected at the previous moment is the "truck location coordinate data of the truck collected the most recently before the current collection state" mentioned in step 7;

[0076] Step 8, starting WIFI signal information collection for the WIFI signal released by the cargo for the building corresponding to the plane coordinate map of the building whose coordinate data overlaps with the coordinate data of the truck location point of the truck, and processing the truck's in-building positioning information in the laser point cloud map of the building according to the collected WIFI signal information and the obtained building laser point cloud map, and using the obtained in-building positioning information as the current real-time position information of the truck; wherein the WIFI signal information includes WIFI signal strength;

[0077] Step 9, make a prediction based on the overlapped intersection information of the location points in the obtained port plane coordinate map and the coordinate data of the truck location point most recently collected before the current collection state, predict the location point coordinate data of the next location point of the truck at the current location point, and use the predicted location point coordinate data as the current real-time location information of the truck.

[0078] Specifically in this embodiment, in step 8, the calculation process of the positioning information of the truck in the building in the building laser point cloud map includes the following steps a1 to a4:

[0079] Step a1, constructing a WIFI location fingerprint database in the building based on the WIFI signal strength for each truck-enterable building in the port area; wherein the WIFI location fingerprint database in the building corresponds to the truck-enterable building one by one, and the WIFI location fingerprint database in the building is formed by a number of WIFI location fingerprint data, each WIFI location fingerprint data includes the coordinate data of the location point in the building and the WIFI signal strength value of the WIFI signal released when the truck just enters the entrance of the building that can be detected at the location point;

[0080] Step a2, processing is performed based on the collected WIFI signal information and the building WIFI location fingerprint database of the building corresponding to the building plane coordinate map whose coordinate data overlaps with the truck location point coordinate data of the truck:

[0081] When the WIFI signal information has the same WIFI signal strength as any WIFI location fingerprint data in the WIFI location fingerprint database in the building, it means that the truck has entered the position corresponding to any WIFI location fingerprint data, and then proceed to step a3; otherwise, proceed to step a4;

[0082] Step a3, obtaining the location point coordinate data of any WIFI location fingerprint data, and using the obtained location point coordinate data as the location information of the truck in the building in the laser point cloud map of the building;

[0083] Step a4, selecting target WIFI location fingerprint data that matches the collected WIFI signal information from the WIFI location fingerprint database in the building, and using the location point coordinate data of the target WIFI location fingerprint data as the building positioning information of the truck in the building laser point cloud map.

[0084] In addition, in the above step a4, the process of selecting the target WIFI location fingerprint data includes the following steps a41 to a46:

[0085] Step a41, respectively obtaining the WIFI signal strength value of each WIFI location fingerprint data in the WIFI location fingerprint database in the building;

[0086] Step a42, forming a WIFI signal strength sequence according to the order of the WIFI signal strength values ​​from the acquired WIFI location fingerprint database in the building;

[0087] Step a43, taking the first WIFI signal strength value in the WIFI signal strength sequence and greater than the WIFI signal strength in the collected WIFI signal information as the WIFI signal strength closest to the actual WIFI signal strength released by the truck;

[0088] It should be noted here that, because the WIFI signal release device on the truck will release the WIFI signal outward after being started, after the truck drives to a building in the port area, the WIFI signal strength released by the WIFI signal release device at the entrance of the building and inside the building is weaker than the WIFI signal strength at the corresponding position when the WIFI location fingerprint database is made; therefore, once the WIFI signal released by the WIFI signal release device on the truck is collected in the building, the actual strength of the WIFI signal is usually weak, and the WIFI signal strength that is stronger than the WIFI signal strength is used as the signal strength value for matching and judging the WIFI signal strength in the WIFI location fingerprint database, and here the first WIFI signal strength value that is greater than the collected WIFI signal strength is used as the WIFI signal strength closest to the actual release of the truck;

[0089] Step a44, using the coordinate data of the location point of the WIFI location fingerprint data to which the first WIFI signal strength value belongs as the preliminary positioning information of the truck in the building in the building laser point cloud map;

[0090] Step a45, obtaining the coordinate data of the previous position point corresponding to the preliminary positioning information in the building and the coordinate data of the next position point corresponding to the preliminary positioning information in the building from the WIFI position fingerprint database in the building, and using the position point corresponding to the obtained coordinate data of the previous position point as the front position point to be measured and the position point corresponding to the coordinate data of the next position point as the rear position point to be measured;

[0091] Step a46, making a judgment based on the building laser point cloud image and the obtained front position point to be measured and the rear position point to be measured:

[0092] When the front position point to be measured and the rear position point to be measured have matching position point coordinate data with the corresponding position points in the building laser point cloud map, it is determined that the preliminary positioning information inside the building is the positioning information of the truck inside the building in the building laser point cloud map; otherwise, the position point coordinate data of the rear position point to be measured is used as the positioning information of the truck inside the building in the building laser point cloud map.

[0093] In addition, it should be noted that in step 9 of this embodiment, the process of predicting the coordinate data of the next location of the truck at the current location includes the following steps b1 to b4:

[0094] Step b1, based on the overlapped intersection information of the position points in the obtained port plane coordinate map and the coordinate data of the truck position points collected the most recently before the current collection state, obtain all the neighbor position points located in the port plane coordinate map and close to the latest position of the truck; wherein the number of neighbor position points close to the latest position of the truck is marked as M, and the mth neighbor position point among the M neighbor position points is marked as D m , neighbor location point D m The location point coordinate data is marked as 1≤m≤M;

[0095] D m :||D m -E new ||≤R;

[0096] Among them, E new is the truck location point that was collected the most recently before the current collection state, that is, the latest location of the truck, truck location point E new The coordinate data of the truck position point is marked as R is the preset position point spacing threshold; ||D m -E new || is the position point D m and the truck position point E new The Euclidean distance between them;

[0097] Step b2, according to each neighbor position point obtained and the port plane coordinate map, respectively obtain the historical passing probability of the current truck passing each neighbor position point in the preset test time period, and select the maximum historical passing probability from all the historical passing probabilities; wherein, the current truck passes the neighbor position point D m The historical probability of driving

[0098]

[0099] in, The current truck passes the neighboring point D within the preset test time period m The total number of times; are all neighboring location points (neighboring location point D1, neighboring location point D2, ..., D M ) total number of times;

[0100] Step b3, according to the overlapped cross information of each neighbor position point and the position point in the port plane coordinate map, the cross probability corresponding to each neighbor position point is matched respectively, and all the cross probabilities obtained by matching are combined to form a cross probability sequence representing the neighbor position point; wherein, the neighbor position point D m The crossover probability is denoted as PDm ;

[0101] Since each neighbor position point is located in the port plane coordinate map, the crossover probability corresponding to each neighbor position point can be obtained by matching the neighbor position point with the position point in the port plane coordinate map;

[0102] Step b4, selecting the maximum crossing probability in the obtained crossing probability sequence, and comparing the maximum crossing probability with the maximum historical passing probability of the current truck to determine:

[0103] When the maximum crossing probability is greater than or equal to the maximum historical passing probability of the current truck, the neighbor position point coordinate data corresponding to the maximum crossing probability is used as the position point coordinate data of the next position point of the truck at the current position point; otherwise, the neighbor position point coordinate data corresponding to the maximum historical passing probability of the current truck is used as the position point coordinate data of the next position point of the truck at the current position point.

[0104] Since port logistics distribution includes not only the transportation and distribution of goods within the port area, but also the situation where goods are transported out of the port area by trucks and transported and distributed outside the port area. Therefore, the port logistics distribution method for safely locating the movement trajectory of goods shipped out of the port area also includes the process of locating the movement trajectory of goods shipped out of the port area. Specifically, in this embodiment, the process of locating the movement trajectory of goods shipped out of the port area includes the following steps c1 to c11:

[0105] Step c1, classifying the logistics companies to which each truck entering and leaving the port area belongs, and obtaining a truck-company list representing the relationship between the truck and the logistics company to which it belongs; wherein, in the truck-company list, each truck entering and leaving the port area corresponds to the logistics company to which it belongs;

[0106] Step c2, based on the obtained truck-company list, count the sequence of common path points that different trucks under each logistics company have passed in sequence when transporting goods on routes with the same starting point and the same end point;

[0107] For example, for route S with starting point A1 and end point B1 A1-B1 , different trucks under the name of logistics company W are executing route S A1-B1 When transporting goods, the sequence of common path points that these trucks pass in sequence is marked as S, S = (q1, q2, ..., q50);

[0108] Step c3, based on the obtained common path point sequences and the starting and ending points of the routes corresponding to the common path point sequences, respectively establish a list of transportation route path points when each logistics company executes a cargo transportation route with different starting-end point pair restrictions; wherein the transportation route path point list includes multiple transportation routes and common path points corresponding to each transportation route, and the common path points include the starting and end points of the transportation routes; different transportation routes correspond to different starting-end points; for example, for the above route S A1-B1 , the route S A1-B1 The corresponding start-end point pair is (A1, B1);

[0109] Step c4, drawing the transport route curves corresponding to each start-end point pair according to the transport route path point list, and using the drawn transport route curves as transport route reference trajectories corresponding to the start-end point pair;

[0110] Step c5, real-time detection of the acquisition status of the real-time location point coordinate data of the truck outside the port area, and making a judgment based on the detection result of the acquisition status:

[0111] When the real-time location point coordinate data of the truck is collected, it means that the real-time location point coordinate data of the truck outside the port area was collected normally at the previous moment of the collection, and the real-time location point coordinate data of the truck carrying the goods when transporting outside the port area is collected to form a real-time motion trajectory curve of the truck that can characterize the motion trajectory of the goods outside the port area; otherwise, it means that there is an abnormality in the collection of the positioning signal when the truck is traveling outside the port area, and corresponding measures need to be taken, and the process goes to step c6;

[0112] Step c6, obtaining the latest current track curve of the truck collected from the time the truck left the port area and drove to the current collection state, and using the obtained current track curve of the truck as a track prediction curve for predicting the current position of the truck;

[0113] Step c7, taking the position point corresponding to the coordinate data of the real-time position point of the truck collected the most recently before the current collection state as the trajectory prediction point for predicting the current position of the truck, and obtaining the previous position point of the trajectory prediction point according to the order of the truck passing by the truck on the truck trajectory prediction curve;

[0114] Step c8, respectively calculating the first slope value of the truck trajectory prediction curve at the previous position point and the second slope value at the trajectory prediction point, and making a judgment based on the first slope value and the second slope value:

[0115] When the product of the first slope value and the second slope value is a positive number, it means that the truck has been traveling in the same direction at the position points corresponding to the two slope values ​​outside the port area, and the process proceeds to step c9; otherwise, it means that the truck has not been traveling in the same direction at the position points corresponding to the two slope values ​​outside the port area, and the process proceeds to step c10;

[0116] Step c9, obtaining the average transport speed of the truck during its travel along the truck trajectory prediction curve, calculating the product of the average transport speed and the time interval between the current moment and the trajectory prediction point position acquisition moment, taking the product as the predicted mileage of the truck, and taking the position in the direction of the truck trajectory prediction curve and the distance from the trajectory prediction point to the predicted mileage as the current position of the truck, and adding the current position of the truck to the cargo movement trajectory;

[0117] Step c10, selecting a transportation route reference trajectory having the same path points as the truck trajectory prediction curve from among all the obtained transportation route reference trajectories, and taking the transportation route reference trajectory as a preferred transportation route reference trajectory;

[0118] Step c11, taking the next path point located in the reference trajectory of the preferred transportation route and being the predicted point of the trajectory as the current position of the truck, and adding the current position of the truck to the cargo movement trajectory.

[0119] Although the preferred embodiments of the present invention are described in detail above, it should be clearly understood that various modifications and variations are possible for those skilled in the art. Any modification, equivalent substitution, 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. A method for safely positioning cargo movement trajectories in port logistics distribution, characterized in that: The steps include: Step 1, pre-acquire a port laser point cloud map and a port plane coordinate map of the port area; wherein the port laser point cloud map is formed by port laser point cloud data obtained by scanning the port area with a laser, and the port plane coordinate map includes two-dimensional coordinates of each position in the port; Step 2, filtering and processing the port laser point cloud map and the port plane coordinate map respectively, and obtaining the corresponding building laser point cloud map and building plane coordinate map of the fixed buildings in the port; wherein the fixed buildings in the port include truck-accessible buildings that trucks can drive in and out of and truck-inaccessible buildings that trucks cannot drive in and out of; Step 3, in advance, within a preset test time period, the motion trajectories of each truck transporting goods when traveling in the port area are collected, and the truck transport prior motion sub-trajectories of each truck are obtained respectively, and the truck transport prior motion sub-trajectories are used to form a truck transport prior motion trajectory that represents the characteristics of the truck trajectory in the port; wherein the truck transport prior motion sub-trajectories are formed by a number of position point information of the truck transporting goods when traveling in the port area, and the position point information includes the three-dimensional coordinate values ​​of the truck at different position points and the appearance time of the truck at the corresponding position point; the trucks within the preset test time period perform normal cargo transportation according to their respective transportation routes; Step 4, according to the port plane coordinate map and the obtained truck transport prior motion trajectory, obtain the position point overlap and intersection information when each position point in the port plane coordinate map overlaps and intersects with the truck transport prior motion trajectory; wherein the position point overlap and intersection information includes the three-dimensional coordinates of the position point, the overlap time when the position point overlaps with the truck transport prior motion trajectory, and the intersection probability of the position point overlapping and intersecting with the truck transport prior motion trajectory at the overlap time; the intersection probability is the ratio of the number of overlaps and intersections between the position point and the truck transport prior motion trajectory at the overlap time to the total number of overlaps and intersections between all position points on the port plane coordinate map and the truck transport prior motion trajectory at the overlap time; Step 5, collecting the real-time location coordinate data of the truck carrying the goods when transporting in the port area to form a real-time movement trajectory of the truck that can characterize the movement trajectory of the goods; wherein, when the truck enters the port, the port management party installs a unified WIFI signal release device that releases WIFI signals to the outside; Step 6: Perform real-time detection on the collection status of the real-time location point coordinate data of the truck, and make a judgment based on the detection result of the collection status: When the real-time location coordinate data of the truck is collected, go to step 5; otherwise, go to step 7; Step 7, making a judgment based on the latest collected truck location point coordinate data of the truck before the current collection state and the obtained building plane coordinate diagram of the fixed building in the port: When the coordinate data of the truck location point of the truck coincides with any coordinate data in the plane coordinate diagram of the building, proceed to step 8; otherwise, proceed to step 9; Step 8, starting WIFI signal information collection for the WIFI signal released by the cargo for the building corresponding to the plane coordinate map of the building whose coordinate data overlaps with the coordinate data of the truck location point of the truck, and processing the truck's in-building positioning information in the laser point cloud map of the building according to the collected WIFI signal information and the obtained building laser point cloud map, and using the obtained in-building positioning information as the current real-time position information of the truck; wherein the WIFI signal information includes WIFI signal strength; Step 9, make a prediction based on the overlapped intersection information of the location points in the obtained port plane coordinate map and the coordinate data of the truck location point most recently collected before the current collection state, predict the location point coordinate data of the next location point of the truck at the current location point, and use the predicted location point coordinate data as the current real-time location information of the truck.

2. The method for safely positioning cargo movement trajectory in port logistics distribution according to claim 1 is characterized in that: In step 8, the calculation process of the location information of the truck in the building in the building laser point cloud map includes the following steps: Step a1, constructing a WIFI location fingerprint database in the building based on the WIFI signal strength for each truck-enterable building in the port area; wherein the WIFI location fingerprint database in the building corresponds to the truck-enterable building one by one, and the WIFI location fingerprint database in the building is formed by a plurality of WIFI location fingerprint data, and the WIFI location fingerprint data includes the coordinate data of the location point in the building and the WIFI signal strength value of the WIFI signal released when the truck just enters the entrance of the building that can be detected at the location point; Step a2, processing is performed based on the collected WIFI signal information and the WIFI location fingerprint database in the building corresponding to the building plane coordinate map whose coordinate data overlaps with the truck location point coordinate data of the truck: When the WIFI signal information has the same WIFI signal strength as any WIFI location fingerprint data in the WIFI location fingerprint database in the building, proceed to step a3; otherwise, proceed to step a4; Step a3, obtaining the location point coordinate data of any WIFI location fingerprint data, and using the obtained location point coordinate data as the location information of the truck in the building in the laser point cloud map of the building; Step a4, selecting target WIFI location fingerprint data that matches the collected WIFI signal information from the WIFI location fingerprint database in the building, and using the location point coordinate data of the target WIFI location fingerprint data as the building positioning information of the truck in the building laser point cloud map.

3. The method for safely positioning the cargo movement trajectory in port logistics distribution according to claim 2 is characterized in that: The selection process of the target WIFI location fingerprint data includes the following steps: Step a41, respectively obtaining the WIFI signal strength value of each WIFI location fingerprint data in the WIFI location fingerprint database in the building; Step a42, forming a WIFI signal strength sequence by taking all WIFI signal strength values ​​of the acquired WIFI location fingerprint database in the building in the order of the WIFI signal strength values ​​from small to large; Step a43, taking the first WIFI signal strength value in the WIFI signal strength sequence and greater than the WIFI signal strength in the collected WIFI signal information as the WIFI signal strength closest to the actual WIFI signal strength released by the truck; Step a44, using the coordinate data of the location point of the WIFI location fingerprint data to which the first WIFI signal strength value belongs as the preliminary positioning information of the truck in the building in the building laser point cloud map; Step a45, obtaining the coordinate data of a previous position point corresponding to the preliminary positioning information in the building and the coordinate data of a subsequent position point corresponding to the preliminary positioning information in the building from the WIFI position fingerprint database in the building, and using the position point corresponding to the obtained coordinate data of the previous position point as the front position point to be measured and the position point corresponding to the coordinate data of the subsequent position point as the rear position point to be measured; Step a46, making a judgment based on the building laser point cloud image and the obtained front position point to be measured and the rear position point to be measured: When the front position point to be measured and the rear position point to be measured have matching position point coordinate data with the corresponding position points in the building laser point cloud map, it is determined that the preliminary positioning information inside the building is the positioning information of the truck inside the building in the building laser point cloud map; otherwise, the position point coordinate data of the rear position point to be measured is used as the positioning information of the truck inside the building in the building laser point cloud map.

4. The method for safely positioning cargo movement trajectory in port logistics distribution according to claim 3 is characterized in that: It also includes the process of locating the movement trajectory of goods shipped out of the port area.

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