Method for analyzing and processing big data of mobile trajectories based on Beidou positioning

Abstract

The present invention relates to the technical field of Beidou data analysis, and particularly relates to a method for analyzing and processing big data of mobile trajectories based on Beidou positioning, comprising the following steps: performing spatio-temporal compensation encoding on the original Beidou trajectory data of multiple moving bodies to generate a spatio-temporal compensation trajectory matrix, wherein the spatio-temporal compensation encoding includes signal shielding degree and motion credibility; constructing a spatio-temporal conflict field of group trajectories based on the spatio-temporal compensation trajectory matrix, and calculating the implicit conflict potential energy values between each trajectory point; generating a credible distribution map of group trajectories; and outputting a collaborative trajectory set with conflict warning marks. The present invention not only improves the safety of group trajectories, but also ensures the high credibility of trajectories, is applicable to complex group trajectory tracking and dynamic environments, and can better handle the interaction behaviors and conflicts of multiple moving bodies.

Description

Technical Field

[0001] The present invention relates to the field of Beidou data analysis technology, and in particular to a mobile trajectory big data analysis and processing method based on Beidou positioning. Background Art

[0002] With the continuous advancement of science and technology and the development of artificial intelligence technology, the tracking and optimization of group trajectories based on Beidou positioning has become increasingly important in many fields, especially in scenarios such as intelligent transportation, unmanned driving, robot collaborative operations, and intelligent logistics. How to track and optimize the trajectories of multiple mobile objects in real time and efficiently to avoid trajectory conflicts and improve the safety and coordination of trajectories has become a technical problem that needs to be solved urgently.

[0003] Existing trajectory optimization methods adjust trajectories through simple distance calculations or collision avoidance algorithms. However, these traditional methods ignore the potential conflict risks between trajectories and the complexity of the environment. Especially in group movement or highly dynamic environments, the accuracy and response speed of traditional methods are difficult to meet real-time requirements. Most methods in the existing technology cannot effectively model complex group behaviors, resulting in it is still difficult to avoid conflicts or collisions in high-density and rapidly changing environments.

[0004] In addition, existing trajectory optimization and conflict warning systems usually lack a hierarchical conflict handling mechanism when dealing with dynamic interactions in complex environments. Although some methods attempt to detect conflicts through physical models, collision detection algorithms, etc., they do not fully consider factors such as the relationship between moving objects, credibility differences, and spatiotemporal changes. Therefore, how to effectively integrate multi-dimensional information and build an efficient and reliable group trajectory optimization and conflict warning system has become an important direction of current technical research. Summary of the invention

[0005] The present invention provides a mobile trajectory big data analysis and processing method based on Beidou positioning.

[0006] The mobile trajectory big data analysis and processing method based on Beidou positioning includes the following steps:

[0007] S1: Performing spatiotemporal compensation coding on the Beidou original trajectory data of multiple mobile objects to generate a spatiotemporal compensation trajectory matrix, wherein the spatiotemporal compensation coding includes a signal shielding degree δ and a motion credibility γ;

[0008] S2: constructing a group trajectory spatiotemporal conflict field based on the spatiotemporal compensation trajectory matrix, and calculating the implicit conflict potential energy value Ψ between each trajectory point;

[0009] S3: verifying the credibility of the implicit conflict potential energy value Ψ through a distributed consensus verification network to generate a credible distribution map of group trajectories;

[0010] S4: Optimize the trajectory self-organization according to the credible distribution map of the group trajectory, and output a collaborative trajectory set with conflict warning marks.

[0011] Optionally, the S1 specifically includes:

[0012] S11, Process the original Beidou trajectory data of each moving object to ensure the alignment of data from different data sources, and form a unified trajectory sequence after timestamp synchronization;

[0013] S12, Calculate the signal shielding degree of each trajectory point. The signal shielding degree reflects the visibility of satellite signals at each trajectory point. The higher the shielding degree, the weaker the signal of the trajectory point;

[0014] S13, Use the sliding window method to calculate the motion credibility of each trajectory point. The motion credibility is determined by evaluating the variance of the positioning within the window. The larger the variance, the higher the position uncertainty of the trajectory point and the lower the credibility;

[0015] S14, Construct a spatio-temporal compensation trajectory matrix, including the spatial coordinates, timestamp, signal shielding degree, and motion credibility of each trajectory point. During the spatial coordinate compensation process, adjust the coordinates of each trajectory point according to the trend of the historical trajectory.

[0016] Optionally, the signal shielding degree of each trajectory point is calculated as: , where represents the signal shielding degree of the trajectory point, is the trajectory point index, is the number of visible satellites of this trajectory point, is the theoretical maximum number of visible satellites;

[0017] Based on the sliding window, the motion credibility is calculated as: , where represents the motion credibility of the th trajectory point, is the positioning variance within the window, is the environmental adaptation factor;

[0018] Construct a spatio-temporal compensation trajectory matrix, denoted as , where the spatial dimension uses dynamic interpolation compensation;

[0019] Among them, represents the coordinates of the trajectory point, is the historical trajectory trend vector, and the compensated coordinates form the matrix elements, and are two different dimensions used to index the trajectory data matrix, representing different trajectory points respectively, is the The coordinate in the spatial coordinates of a trajectory point, is the coordinate in the spatial coordinates of the th trajectory point, representing the horizontal position of the trajectory point on the map, and both are used to identify different trajectory point positions. When constructing the spatio-temporal compensation matrix, each position corresponds to a specific trajectory point and coordinate, representing different dimensions of the same trajectory data, representing the abscissa and ordinate respectively, is the timestamp of the th trajectory point, representing the positioning time of the trajectory point, is the motion credibility of the th trajectory point, representing the positioning credibility of the point. The motion credibility is calculated based on the positioning variance within a sliding window. The smaller the variance, the higher the credibility, is the signal shielding degree of the

[0020] Optionally, the S2 specifically includes:

[0021] S21, defining the spatio-temporal neighborhood range: Define the spatio-temporal neighborhood range for the trajectory points of different moving objects, specifically including setting a time window and a spatial radius. The size of the time window is defined by the user, indicating that the time difference between the two trajectory points does not exceed this time window, and they are considered to be in the same neighborhood. The spatial radius is dynamically adjusted according to the average motion credibility of each pair of trajectory points. When the credibility is low, the neighborhood range increases, and vice versa;

[0022] S22, performing neighborhood scanning on the spatio-temporal compensation trajectory matrix: After the spatio-temporal compensation trajectory matrix is constructed, the next step is to perform neighborhood scanning. By setting the time window and spatial radius, all trajectory points are compared one by one. If a pair of trajectory points satisfies that the time difference is within the set time window and the spatial distance is also within the set spatial radius, this pair of trajectory points is considered to be in the same spatio-temporal neighborhood;

[0023] S23, calculating the implicit conflict potential energy value Ψ between trajectory points: After determining the trajectory points belonging to the same spatio-temporal neighborhood, calculate the implicit conflict potential between them. The calculation of the implicit conflict potential energy value Ψ includes the distance, motion state, and credibility difference between the two;

[0024] S24. Aggregate the implicit conflict potential energy values Ψ of all neighborhood pairs: After calculating the implicit conflict potential energy values of all trajectory point pairs, aggregate them to form an overall implicit conflict potential energy field. To screen out the trajectory point pairs that truly have the potential for conflict, a conflict threshold is introduced. When the implicit conflict potential energy of a pair of trajectory points exceeds this conflict threshold, it is considered a potential conflict pair. The setting of the threshold is based on the minimum motion credibility within the current neighborhood. When the motion credibility is low, the threshold is low, making it easier to identify potential conflicts.

[0025] Optionally, the S23 specifically includes:

[0026] Calculate the spatio-temporal distance between the compensated trajectory point pairs and compensate the distance according to the signal shielding degree;

[0027] Calculate the speed difference based on the motion vectors of the two and the degree of intersection of the motion directions, and estimate the probability of collision;

[0028] Calculate the credibility difference between the trajectory point pairs. The greater the credibility difference, the more likely there is an error in the trajectory of one of them, increasing the risk of conflict;

[0029] Through comprehensive calculation, obtain the implicit conflict potential energy value between each pair of trajectory points.

[0030] Optionally, the S3 specifically includes:

[0031] S31. Construct a Byzantine fault-tolerant network based on trajectory point nodes: Consider each trajectory point as a node to construct a Byzantine fault-tolerant network. The voting weight of each node is calculated according to the motion credibility and signal shielding degree of the trajectory point. The higher the weight, the stronger the verification ability of the node and the more reliable the signal;

[0032] S32. Perform hierarchical verification on the implicit conflict potential energy values: Calculate the implicit conflict potential energy values between each pair of trajectory points , the implicit conflict potential energy value represents the potential conflict degree between two trajectory points, and two-level verification is used to determine the type of conflict;

[0033] S33. Execute a distributed consensus verification network: After completing the conflict verification, execute a consensus protocol based on Byzantine fault tolerance to ensure that all nodes reach an agreement on the conflict and the verification results. The consensus protocol includes a proposal stage, a verification stage, and a confirmation stage. After the confirmation stage, a trusted verification block is generated;

[0034] S34. Construct a credible distribution map of the group trajectory based on the trusted verification block: Through the generation of the trusted verification block, construct a credible distribution map of the group trajectory, which includes:

[0035] Vertex: Each vertex of the group trajectory graph represents a trajectory point and includes the motion credibility and signal shielding degree of the trajectory point;

[0036] Edge: Each edge of the group trajectory graph represents the conflict relationship between two trajectory points, and the conflict relationship weight is calculated through the implicit conflict potential value and the difference in motion credibility between the trajectory points;

[0037] Trusted region: The trusted region is determined according to the conflict relationship weight and signal shielding degree, and the trusted region threshold is set by calculating the average shielding degree of all trajectory points.

[0038] Optionally, the two-level verification in S32 specifically includes:

[0039] Primary verification: If the conflict potential value between two trajectory points is greater than the core conflict threshold, then this pair of trajectory points is marked as a core conflict. The core conflict judgment criterion is to set the core conflict threshold according to the signal shielding degree of the trajectory point, and the core conflict threshold decreases as the shielding degree decreases;

[0040] Secondary verification: If the implicit conflict potential value is between the core conflict and the edge conflict threshold and meets the secondary verification condition, that is, an edge conflict is triggered. The edge conflict determination is based on the average value of the motion credibility of the trajectory points.

[0041] Optionally, in the proposed stage: The trajectory point node with high weight proposes a broadcast conflict verification request;

[0042] In the verification stage: Each trajectory point node calculates a verification signature according to the local implicit conflict potential value. The verification signature is obtained by performing an encrypted hash process on the conflict potential value, motion credibility, and shielding degree;

[0043] In the confirmation stage: Collect the verification signatures of all nodes and calculate the weighted sum. If the weighted sum of the verification signatures is greater than two-thirds of the total weight, a trusted verification block is generated, indicating that the conflict verification results of the current round are in agreement.

[0044] Optionally, S4 specifically includes:

[0045] S41, Conflict trajectory segment extraction: Screen out all potential conflict trajectory segments from the group trajectory trusted distribution map. The group trajectory trusted distribution map includes the mutual relationship and edge weight between each pair of trajectory points. Extract the pairs of trajectory points with edge weights greater than the core conflict threshold to form a high-risk conflict segment set;

[0046] S42, Potential field gradient optimization: Optimize the positions of the set of high-risk conflict segments to reduce potential conflicts. By calculating the gradient of the trajectory points in the potential field, determine the optimization direction for each trajectory point. During the optimization process, when adjusting the position of a trajectory point each time, make dynamic adjustments based on the distance relationship between the current trajectory point and other trajectory points;

[0047] S43, Multi-objective path planning: Use the multi-objective path planning method to optimize the positions of the trajectory points. Calculate the optimized set of trajectory points through the constrained Newton-Raphson algorithm to minimize the deviation between the original trajectory and the optimized trajectory, and consider the potential conflict potential values.

[0048] S44, Cooperative trajectory generation: Perform spatio-temporal interpolation on the optimized trajectory and the original trajectory to generate the final set of cooperative trajectories.

[0049] Optionally, the conflict trajectory segment extraction includes screening from the population trajectory credibility distribution map the trajectory point pairs associated with the edges that satisfy to form a set of high-risk conflict segments , where is the edge weight between trajectory point and trajectory point , is the core conflict threshold, represents the set of high-risk conflict segments, containing all the trajectory segments that meet the conflict conditions, represents the element of the conflict segment, belonging to the set , and each conflict segment consists of a set of trajectory points and and the time stamp to form a trajectory segment that may conflict in time and space, is the time stamp indicating the time or time interval when the trajectory segment occurs.

[0050] Advantages of the present invention:

[0051] In the present invention, by introducing a node-based fault-tolerant network and a conflict potential calculation model, it is possible to evaluate and optimize the conflict problems in the population trajectory. Through spatio-temporal neighborhood scanning, implicit conflict potential calculation, and credibility fusion, the system effectively eliminates potential trajectory conflicts, not only improving the safety of the population trajectory but also ensuring the high credibility of the trajectory. It is applicable to complex population trajectory tracking and dynamic environments and can better handle the interaction behaviors and conflicts of multiple moving objects.

[0052] In the present invention, through a hierarchical conflict warning mechanism, conflict information in trajectories is classified into multiple levels such as core conflicts, marginal conflicts, and safe trajectories. By using dynamically calculated conflict potential energy and credibility factors, different risk areas in the trajectories can be accurately marked and managed, thereby providing a reliable basis for real-time decision-making. The conflict classification mechanism enables the system not only to early warn high-risk areas but also to effectively monitor marginal conflicts, greatly improving the refinement and timeliness of trajectory conflict handling in group behaviors. Especially in complex environments, it can make a rapid response to avoid potential accidents.

[0053] The present invention realizes multi-objective path planning in high-risk conflict segments, can accurately optimize trajectories according to conflict potential energy and credibility. It can not only effectively adjust the movement directions of trajectory points to avoid possible collisions but also adjust path planning according to the dynamic changes of moving objects to ensure that collaborative trajectories can be optimized in real time in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention;

[0056] Figure 2 It is a schematic flowchart of the process of constructing a spatio-temporal conflict field for group trajectories according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0058] It should be pointed out that in the specification, it is mentioned that "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. indicate that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes the specific feature, structure or characteristic. In addition, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0059] Generally, terms can be understood at least in part from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or property in the singular sense, or can be used to describe a combination of features, structures, or properties in the plural sense. Additionally, the term "based on" can be understood to not necessarily be intended to convey a set of exclusive factors, but rather can alternatively, depending at least in part on the context, allow for the existence of other factors that are not necessarily explicitly described.

[0060] As Figure 1 - Figure 2 shown, a method for analyzing and processing big data of mobile trajectories based on Beidou positioning includes the following steps:

[0061] S1: Perform spatio-temporal compensation encoding on the original Beidou trajectory data of multiple mobile objects to generate a spatio-temporal compensation trajectory matrix. The spatio-temporal compensation encoding includes signal shielding degree δ and motion credibility γ;

[0062] S2: Construct a population trajectory spatio-temporal conflict field based on the spatio-temporal compensation trajectory matrix, and calculate the implicit conflict potential energy value Ψ between each trajectory point;

[0063] S3: Verify the credibility of the implicit conflict potential energy value Ψ through a distributed consensus verification network to generate a population trajectory credibility distribution map;

[0064] S4: Perform trajectory self-organization optimization according to the population trajectory credibility distribution map, and output a collaborative trajectory set with conflict warning marks.

[0065] S1 specifically includes:

[0066] S11: Process the original Beidou trajectory data of each mobile object to ensure the alignment of data from different data sources, and form a unified trajectory sequence after timestamp synchronization;

[0067] S12: Calculate the signal shielding degree of each trajectory point. The signal shielding degree reflects the visibility of satellite signals at each trajectory point. The trajectory point with a higher shielding degree has a weaker signal;

[0068] S13: Use the sliding window method to calculate the motion credibility of each trajectory point. The motion credibility is determined by evaluating the variance of the positioning within the window. The larger the variance, the higher the position uncertainty of the trajectory point and the lower the credibility;

[0069] S14: Construct a spatio-temporal compensation trajectory matrix, including the spatial coordinates, timestamp, signal shielding degree, and motion credibility of each trajectory point. During the spatial coordinate compensation process, adjust the coordinates of each trajectory point according to the trend of the historical trajectory. The compensated coordinates reflect the influence of signal quality and motion credibility on the positioning result. These adjusted coordinates, together with other information, constitute the compensated trajectory matrix.

[0070] Perform multi-source heterogeneous alignment on the original Beidou trajectory data stream of each moving object to obtain a trajectory sequence with timestamp synchronization , where represents the -th trajectory point in the trajectory sequence represents the -th trajectory point's timestamp, indicating the time corresponding to this trajectory point;

[0071] The signal shielding degree of each trajectory point is calculated as: , where represents the signal shielding degree of the trajectory point, is the trajectory point index, is the number of visible satellites of this trajectory point, is the theoretical maximum number of visible satellites;

[0072] Based on a sliding window, the motion credibility is calculated as: , where represents the motion credibility of the -th trajectory point, is the positioning variance within the window, is the environmental adaptation factor;

[0073] Construct a spatio-temporal compensation trajectory matrix, denoted as , where the spatial dimension uses dynamic interpolation compensation:

[0074] ;

[0075] ;

[0076] where represents the coordinates of the trajectory point, is the historical trajectory trend vector, and the compensated coordinates constitute the matrix elements, and are two different dimensions used to index the trajectory data matrix, representing different trajectory points respectively, is the -th trajectory point's coordinate in the spatial coordinates, indicating the horizontal position of this trajectory point on the map, is the -th trajectory point's coordinate in the spatial coordinates, indicating the vertical position of this trajectory point on the map, and are both used to identify different trajectory point positions. When constructing the spatio-temporal compensation matrix, each position corresponds to a specific trajectory point and coordinates, representing different dimensions of the same trajectory data, indicating the abscissa and ordinate respectively is the timestamp of the nth trajectory point, representing the positioning time of this trajectory point. is the motion credibility of the nth trajectory point, representing the reliability of the positioning of this point. The motion credibility is calculated based on the positioning variance within a sliding window. The smaller the variance, the higher the credibility. is the signal occlusion degree of the nth trajectory point, representing the degree of satellite signal occlusion at this point. A higher occlusion degree means poorer signal quality.

[0077] The motion credibility measures the uncertainty of positioning through variance. The smaller the variance, the higher the positioning accuracy and credibility; the larger the variance, the higher the positioning uncertainty and the lower the credibility.

[0078] Within the sliding window, the deviation degree of the positioning value of a trajectory point from other trajectory points in the window. The larger the variance, the more unstable or unreliable the positioning result of this trajectory point is, so the credibility will decrease accordingly. The motion credibility is calculated through the exponential decay of the positioning variance. The larger the variance, the stronger the degree of exponential decay, resulting in a decrease in the motion credibility. The negative exponential form exp means that as the variance increases, the decrease in credibility will be exponential, reflecting the strong impact of positioning uncertainty on credibility.

[0079] Environmental adaptation factor is a constant used to adjust the influence of positioning variance on motion credibility. It can be set according to different environmental conditions to determine the degree of influence of variance. plays a balancing role and controls the influence intensity of variance on credibility. If is smaller, then the influence on credibility when variance increases is larger; if is larger, then the increase in variance has a smaller influence on credibility.

[0080] In open areas, the satellite signal is relatively stable, and a larger can be selected; in complex environments such as cities or mountains, the positioning signal may be more interfered, should be appropriately reduced to enhance the sensitivity to positioning variance.

[0081] S2 specifically includes:

[0082] S21, Define the spatio-temporal neighborhood range: Define the spatio-temporal neighborhood range for the trajectory points of different moving objects, specifically including setting a time window and a spatial radius. The size of the time window is defined by the user, indicating that the time difference between the two trajectory points does not exceed this time window, and they are considered to be in the same neighborhood. The spatial radius is dynamically adjusted according to the average motion credibility of each pair of trajectory points. When the credibility is low, the neighborhood range increases, and vice versa. A minimum value is also set for the spatial radius to ensure that even in the case of high credibility, the neighborhood range will not be too narrow;

[0083] S22, Conduct neighborhood scanning on the spatio-temporal compensated trajectory matrix: After the spatio-temporal compensated trajectory matrix is constructed, neighborhood scanning is carried out next. By setting the time window and spatial radius, all trajectory points are compared one by one. If a pair of trajectory points satisfies that the time difference is within the set time window and the spatial distance is also within the set spatial radius, this pair of trajectory points is considered to be in the same spatio-temporal neighborhood. In this way, the system can effectively identify the pairs of trajectory points that are close to each other and may interact, and find the corresponding pairs of conflicting moving objects according to the corresponding moving objects associated with each trajectory point;

[0084] S23, Calculate the implicit conflict potential energy value Ψ between trajectory points: After determining the trajectory points belonging to the same spatio-temporal neighborhood, calculate the implicit conflict potential energy between them. The calculation of the implicit conflict potential energy value Ψ includes the distance, motion state, and credibility difference between the two;

[0085] S24, Aggregate the implicit conflict potential energy values Ψ of all neighborhood pairs: After the implicit conflict potential energy values of all trajectory point pairs are calculated, aggregation is carried out to form an overall implicit conflict potential energy field. To screen out the pairs of trajectory points that truly have the potential for conflict, a conflict threshold is introduced. When the implicit conflict potential energy of a pair of trajectory points exceeds this conflict threshold, it is considered a potential conflict pair. The setting of the threshold is based on the minimum motion credibility within the current neighborhood. When the motion credibility is low, the threshold is low, making it easy to identify potential conflicts.

[0086] By aggregating all eligible implicit conflict potential energy values, a complete conflict potential energy field is finally formed, which can be used for subsequent trajectory optimization and early warning decision-making.

[0087] S23 specifically includes:

[0088] Calculate the spatio-temporal distance between the compensated trajectory point pairs and compensate the distance according to the signal shielding degree;

[0089] Calculate the speed difference according to the motion vectors of the two and the degree of intersection of the motion directions, and estimate the probability of collision;

[0090] Calculate the credibility difference between the trajectory point pairs. The greater the credibility difference, the more likely there is an error in the trajectory of one of them, increasing the risk of conflict;

[0091] Through comprehensive calculation, obtain the implicit conflict potential energy value between each pair of trajectory points.

[0092] Define the spatio-temporal neighborhood range: Set the time window and the spatial radius , where:

[0093] , where, is the average motion credibility of the trajectory points within the neighborhood

[0094] is the dynamic adjustment coefficient, is the minimum spatial radius;

[0095] Perform an OR scan on the spatio-temporal compensated trajectory matrix: Extract the trajectory point pairs that meet the following conditions :

[0096] ;

[0097] Calculate the implicit conflict potential energy value between trajectory points :

[0098] , where:

[0099] represents the spatio-temporal distance between the trajectory point pair, , is the spatio-temporal distance after compensation by the spatio-temporal compensated trajectory matrix;

[0100] ; ;

[0101] is the velocity vector of the trajectory point;

[0102] represents the standard deviation of the positioning variance within the window;

[0103] is the th and th signal obscuration degrees of the trajectory points;

[0104] represents the credibility difference factor, , is the th and th motion credibilities of the trajectory points, represents the obscuration weight, ;

[0105] Aggregate the values to generate an implicit conflict potential field , , where is the conflict threshold , is the minimum motion credibility within the current neighborhood is an indicator function, with a value of 1 if the condition is met and 0 otherwise is a constant coefficient takes values in the range [0, 1] and is adjusted according to the actual situation. μ = 1 represents the maximum possible conflict threshold. In this case, for trajectory points with low credibility, their implicit conflict potential is more likely to exceed the set threshold, and potential conflicts can be identified more sensitively; μ = 0.5 represents a medium sensitivity threshold, which is suitable for general situations where the sensitivity to conflicts is moderate and false alarms do not occur too frequently; μ = 0.1 represents a low conflict sensitivity, which is suitable for situations where excessive intervention is not required and is used in environments with high credibility to reduce the triggering of conflicts

[0106] S3 specifically includes:[[]]

[0107] S31, constructing a Byzantine fault-tolerant network based on trajectory point nodes: regarding each trajectory point as a node to construct a Byzantine fault-tolerant network, and calculating the voting weight of each node according to the motion credibility and signal shielding degree of the trajectory point. The higher the weight, the stronger the verification ability of the node and the more reliable the signal. The motion credibility reflects the reliability of the trajectory point in positioning, while the shielding degree is an index to measure the signal quality of the trajectory point

[0108] S32, performing hierarchical verification on the implicit conflict potential values: calculating the implicit conflict potential values between each pair of trajectory points. The implicit conflict potential value represents the potential conflict degree between two trajectory points, and two-level verification is used to determine the type of conflict

[0109] S33, executing a distributed consensus verification network: after completing the conflict verification, execute a consensus protocol based on Byzantine fault tolerance to ensure that all nodes reach an agreement on the conflict and the verification results. The consensus protocol includes a proposal stage, a verification stage, and a confirmation stage. After the confirmation stage, a trusted verification block is generated

[0110] S34, constructing a credible distribution map of the group trajectory based on the trusted verification block: through the generation of the trusted verification block, construct a credible distribution map of the group trajectory, which includes:[[]]

[0111] Vertices: Each vertex of the group trajectory graph represents a trajectory point and includes the motion credibility and signal shielding degree of the trajectory point

[0112] Edge: Each edge in the group trajectory graph represents the conflict relationship between two trajectory points. The conflict relationship weight is calculated based on the implicit conflict potential value and the difference in motion credibility between the trajectory points. The intensity of the conflict relationship is closely related to the degree of difference between the two trajectory points;

[0113] Trusted region: The trusted region is determined based on the conflict relationship weight and the signal shielding degree. Trajectory points with a low signal shielding degree are regarded as potential unreliable regions and will therefore be marked as untrusted regions. The trusted region threshold is set by calculating the average shielding degree of all trajectory points. Regions with low shielding degrees are demarcated as untrusted, and the scope of the trusted region is dynamically adjusted based on the shielding degree and the conflict potential.

[0114] It should be noted that:

[0115] The above-mentioned trusted block is the block generated when executing the consensus protocol. This block contains verified conflict information and verification signatures and is a data structure indicating that a group of nodes have reached a consensus on the conflict relationship between certain trajectory points. The trusted block is a dynamic verification result, referring to the confirmation and consensus of all participating nodes on the conflicts between certain trajectory points.

[0116] The above-mentioned trusted region is the region generated when constructing the trusted distribution map of the group trajectory. The trusted region refers to the region in the graph where, based on factors such as the motion credibility and signal shielding degree of the trajectory points, it is determined which regions are "trusted", that is, the regions where these trajectory points are located are considered reliable because they have a high credibility and a low conflict risk, and the trajectory data in these regions can be trusted. The trusted region is divided based on the shielding degree and the conflict potential.

[0117] The two-level verification in S32 specifically includes:

[0118] Primary verification: If the conflict potential value between two trajectory points is greater than the core conflict threshold, then this pair of trajectory points is marked as a core conflict. The core conflict judgment criterion is to set the core conflict threshold based on the signal shielding degree of the trajectory points, and the core conflict threshold decreases as the shielding degree decreases;

[0119] Secondary verification: If the implicit conflict potential value is between the core conflict and the edge conflict threshold and meets the secondary verification conditions, then the edge conflict is triggered. The edge conflict judgment is based on the average value of the motion credibility of the trajectory points, and trajectory points with a higher motion credibility may trigger the edge conflict first.

[0120] In the proposal stage: The trajectory point node with a high weight proposes to broadcast a conflict verification request. Nodes with a higher weight are considered to have a stronger influence in the verification and will therefore initiate the conflict request first;

[0121] During the verification phase: Each trajectory point node calculates a verification signature based on the local implicit conflict potential value. The verification signature is obtained by performing an encrypted hash operation on the conflict potential value, motion credibility, and occlusion. The purpose of this step is to ensure that the conflict verification information is not tampered with;

[0122] During the confirmation phase: Collect the verification signatures of all nodes and calculate the weighted sum. If the weighted sum of the verification signatures is greater than two-thirds of the total weight, a trusted verification block is generated, indicating that the conflict verification results for the current round are in agreement.

[0123] In constructing a Byzantine fault-tolerant network based on trajectory point nodes:

[0124] The voting weight of each node is defined as: , where is the motion credibility of the trajectory point, is the signal occlusion of the trajectory point, is the trajectory point index;

[0125] Perform hierarchical verification on the implicit conflict potential value :

[0126] Primary verification: If then it is marked as a core conflict, and the core conflict threshold is calculated as:

[0127] ;

[0128] Secondary verification: If , then an edge conflict is triggered, and the edge conflict threshold is calculated as: ;

[0129] Execute an improved PBFT (Byzantine fault-tolerant) consensus protocol:

[0130] Proposal phase: High-weight nodes, i.e., , is the weight threshold, set to 0.5, and broadcast a conflict verification request;

[0131] Verification phase: Each node calculates a verification signature based on the local implicit conflict potential value : , represents a hash function, which is used to generate an output of a fixed length (signature) based on the input content. The role of the hash function is to encrypt the input data so that any minor change in the input data will cause a significant change in the output result. By matching the hash value with the expected signature value, it can be determined whether it is valid. It is the SHA-256 cryptographic hash algorithm.

[0132] Confirmation phase: When the conditions are met, a trusted verification block is generated. It is considered valid. is an indicator function used to determine whether the verification signature is valid. When the signature passes the verification, the value of this function is 1; otherwise, it is 0, which is expressed as:

[0133] Valid ;

[0134] Construct a credible distribution map of the group trajectory based on the verification block , where the vertex represents a trajectory point, and the attributes include , and the edge represents a verified conflict relationship, and the edge weight is calculated as:

[0135] ;

[0136] The trusted area is marked as connected subgraph, where the trusted area threshold is calculated as:

[0137] , is the average signal shielding degree of all trajectory points in the current neighborhood.

[0138] S4 specifically includes:

[0139] S41, Extraction of conflict trajectory segments: Screen out all potential conflict trajectory segments from the credible distribution map of the group trajectory. The credible distribution map of the group trajectory includes the mutual relationship and edge weight between each pair of trajectory points. Extract the pairs of trajectory points with edge weights greater than the core conflict threshold to form a set of high-risk conflict segments;

[0140] S42, Potential field gradient optimization: Optimize the positions of the set of high-risk conflict segments to reduce potential conflicts. By calculating the gradient of the trajectory points in the potential field, determine the optimization direction of each trajectory point. During the optimization process, when adjusting the position of each trajectory point, make dynamic adjustments according to the distance relationship between the current trajectory point and other trajectory points;

[0141] S43. Multi-objective path planning: Optimize the positions of trajectory points using a multi-objective path planning method. Calculate the optimized set of trajectory points through a constrained Newton-Raphson algorithm to minimize the deviation between the original trajectory and the optimized trajectory, and consider the potential conflict potential value. This process is not simply about reducing distance, but also requires weighing the severity of conflicts against the spatial relationships between trajectory points. The constraints further ensure that the credibility and occlusion of trajectory points do not exceed reasonable ranges during the optimization process. The ultimate goal is to ensure that each trajectory point still conforms to feasible spatial and spatio-temporal constraints after optimization.

[0142] S44. Cooperative trajectory generation: Perform spatio-temporal interpolation on the optimized trajectory and the original trajectory to generate the final set of cooperative trajectories. During this process, unoptimized trajectory segments are marked as edge conflict segments, and warning information is added to the generated cooperative trajectories to ensure the coordination and executability between all trajectory points. At the same time, the optimized trajectory path is smoothed through the interpolation method to avoid conflicts. The final set of cooperative trajectories will contain the spatio-temporal information of all trajectory points and can provide hierarchical conflict warnings during visualization or further analysis.

[0143] In summary, the above goals are to minimize conflicts between trajectory points in the group through the optimization of trajectory points and path planning, and generate a safer and more reliable set of trajectories. This process requires multi-faceted optimization, including conflict identification, path optimization, and cooperative generation, to ensure that the final result can effectively guide actual trajectory control and conflict avoidance.

[0144] Conflict trajectory segment extraction includes screening trajectory point pairs associated with edges that meet from the credible distribution map of the group trajectories to form a set of high-risk conflict segments . Among them, , is the edge weight between trajectory point and trajectory point , is the core conflict threshold, represents the set of high-risk conflict segments, containing all trajectory segments that meet the conflict conditions, represents the elements of the conflict segment, belonging to the set . Each conflict segment consists of a group of trajectory points and and the time stamp , indicating a trajectory segment that may have conflicts in time and space. is the time stamp indicating the time or time interval when this trajectory segment occurs;

[0145] Potential field gradient optimization: For each conflict segment , based on the credible distribution map Vertex attributes and edge weights are used to calculate the trajectory optimization direction:

[0146] , where is the adaptive step size, , is the trajectory point 's spatio-temporal neighborhood, The trajectory point 's signal shielding degree, The trajectory point 's motion credibility, is the adjustment factor of the adaptive step size;

[0147] Multi-objective path planning: The constrained Newton-Raphson algorithm is used to solve the optimization equation:

[0148] ;

[0149] Constraint conditions:

[0150] ;

[0151] ;

[0152] where is the optimized trajectory point, is the original trajectory point, is the optimization coefficient, is the trajectory point and the implicit conflict potential energy value between them, is the maximum shielding degree change, is the minimum credibility, is the adjustment factor;

[0153] Collaborative trajectory generation: The optimized trajectory points are fused with the original trajectory data through spatio-temporal interpolation, and edge conflict markers are added to the unoptimized trajectory segments to generate a collaborative trajectory set with hierarchical warnings , and the spatio-temporal interpolation fusion is as follows:

[0154] 1. Docking of optimized trajectory points with the original trajectory: The optimized trajectory points must be docked with the original trajectory points to maintain the consistency and continuity of the trajectory. To ensure a smooth transition and seamless connection of the trajectory, a spatio-temporal interpolation method is used. The purpose is to find the balance point with the original trajectory points between each optimized trajectory point, and the intermediate points between the two are calculated through the interpolation method. In this way, the trajectory can achieve a smooth transition from the original trajectory to the optimized trajectory. The interpolation method uses spline interpolation,

[0155] 2. For the trajectory segments that are not optimized during the optimization process, i.e., the trajectory segments that do not meet the conflict resolution criteria, edge conflict markers need to be added to the collaborative trajectory by marking the trajectory point pairs that do not meet the conflict resolution conditions during the optimization process. For example, if the conflict potential energy value of a certain trajectory segment is still relatively high, or does not reach the predetermined confidence threshold, the edge conflict risk can be marked on this trajectory segment. The marking method is to assign a special label or attribute to the trajectory segment, such as "edge conflict", "conflict risk", etc.

[0156] 3. Generate the collaborative trajectory set Tout After spatio-temporal interpolation and fusion of the optimized trajectory point set and the original trajectory, all the trajectory points constitute the final collaborative trajectory set Tout. Each trajectory point in the collaborative trajectory set Tout contains the following information:

[0157] Trajectory point coordinates: The coordinates of the optimized trajectory points or the interpolated coordinates.

[0158] Motion confidence: The confidence of the optimized trajectory points.

[0159] Occlusion: The signal occlusion of the optimized trajectory points.

[0160] Conflict marker: Mark whether there is a conflict, especially edge conflict or core conflict.

[0161] 4. Combine the conflict marker and confidence information to generate a hierarchical conflict warning system.

[0162] Core conflict: When the conflict potential energy value exceeds the core conflict threshold and the conflict of this trajectory segment cannot be eliminated through optimization, it is marked as a core conflict.

[0163] Edge conflict: For those trajectory segments whose conflict potential energy values are between the predetermined edge conflict threshold and the core conflict threshold, they can be marked as edge conflicts. These trajectory segments have a certain conflict risk but have not reached the core conflict level.

[0164] Safe trajectory: Those trajectory segments that have no conflict or a low conflict potential energy value after optimization can be marked as safe trajectories, indicating that they are reliable under the current spatio-temporal conditions.

[0165] 5. After completing the above steps, the finally output collaborative trajectory set Tout includes all the trajectory points and carries hierarchical warning information.

[0166] The present invention encompasses any alternatives, modifications, equivalent methods, and solutions that are made within the spirit and scope of the present invention. For the purpose of enabling the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention even without the description of these details. Additionally, in order to avoid unnecessary confusion to the essence of the present invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0167] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for analyzing and processing mobile trajectory big data based on Beidou positioning, characterized in that It includes the following steps: S1: Perform spatio-temporal compensation encoding on the original Beidou trajectory data of multiple moving objects to generate a spatio-temporal compensation trajectory matrix. The spatio-temporal compensation encoding includes signal occlusion degree and motion credibility; S2: Construct a spatio-temporal conflict field of the group trajectory based on the spatio-temporal compensation trajectory matrix, and calculate the implicit conflict potential energy value between each trajectory point; The specific content of S2 includes: S21, Define the spatio-temporal neighborhood range: Define the spatio-temporal neighborhood range for the trajectory points of different moving objects, specifically including setting a time window and a spatial radius. The size of the time window is defined by the user, indicating that the time difference between the two trajectory points does not exceed this time window, which means they are in the same neighborhood. The spatial radius is dynamically adjusted according to the average motion credibility of each pair of trajectory points. When the credibility is low, the neighborhood range increases, and vice versa; S22, Perform neighborhood scanning on the spatio-temporal compensation trajectory matrix: After the spatio-temporal compensation trajectory matrix is constructed, next perform neighborhood scanning. By setting the time window and spatial radius, compare all trajectory points one by one. If a pair of trajectory points satisfies that the time difference is within the set time window and the spatial distance is also within the set spatial radius, this pair of trajectory points is considered to be in the same spatio-temporal neighborhood; S23, Calculate the implicit conflict potential energy value Ψ between trajectory points: After determining the trajectory points belonging to the same spatio-temporal neighborhood, calculate the implicit conflict potential energy between them. The calculation of the implicit conflict potential energy value Ψ includes the distance, motion state, and credibility difference between the two; S24, Aggregate the implicit conflict potential energy values Ψ of all neighborhood pairs: After the implicit conflict potential energy values of all trajectory point pairs are calculated, perform aggregation to form an overall implicit conflict potential energy field. Introduce a conflict threshold. When the implicit conflict potential energy of a pair of trajectory points exceeds this conflict threshold, it is considered a potential conflict pair; The specific content of S23 includes: Calculate the spatio-temporal distance between the compensated trajectory point pairs, and compensate the distance according to the signal occlusion degree; Calculate the speed difference according to the motion vectors of the two, as well as the degree of intersection of the motion directions, and estimate the probability of collision; Calculate the credibility difference between the trajectory point pairs. The greater the credibility difference, the more likely there is an error in the trajectory of one of them, increasing the risk of conflict; Through comprehensive calculation, obtain the implicit conflict potential energy value between each pair of trajectory points; S3: Verify the credibility of the implicit conflict potential energy value through a distributed consensus verification network to generate a credible distribution map of the group trajectory; S4: Perform trajectory self-organization optimization according to the credible distribution map of the group trajectory, and output a collaborative trajectory set with conflict warning marks.

2. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 1, wherein, The specific content of S1 includes: S11, Process the original Beidou trajectory data of each moving object to ensure the alignment of data from different data sources, and form a unified trajectory sequence after timestamp synchronization; S12, Calculate the signal occlusion degree of each trajectory point. The signal occlusion degree reflects the visibility of the satellite signal at each trajectory point. The higher the occlusion degree of the trajectory point, the weaker the signal; S13, Use the sliding window method to calculate the motion credibility of each trajectory point. The motion credibility is determined by evaluating the variance of the positioning within the window. The greater the variance, the higher the position uncertainty of the trajectory point and the lower the credibility; S14. Construct a spatio-temporal compensation trajectory matrix, including the spatial coordinates, timestamps, signal obscuration degrees, and motion credibility of each trajectory point. During the spatial coordinate compensation process, adjust the coordinates of each trajectory point according to the trend of the historical trajectory.

3. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 2, wherein, The signal shielding degree is calculated as follows: , where represents the signal shielding degree of the trajectory point, is the trajectory point index, is the number of visible satellites of the trajectory point, is the theoretical maximum number of visible satellites; The motion credibility is calculated as follows: , where represents the motion credibility of the th trajectory point, is the positioning variance within the window, is the environmental adaptation factor; Construct a spatio-temporal compensation trajectory matrix, denoted as ; Among them, represents the coordinates of a trajectory point, and are two different dimensions used to index the trajectory data matrix, representing different trajectory points respectively, is the coordinate in the spatial coordinates of the th trajectory point, representing the horizontal position of this trajectory point on the map, is the coordinate in the spatial coordinates of the th trajectory point, representing the vertical position of this trajectory point on the map, is the timestamp of the th trajectory point, representing the positioning time of this trajectory point, is the motion credibility of the th trajectory point, is the signal shielding degree of the th trajectory point.

4. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 1, wherein, The specific steps of S3 are as follows: S31. Construct a Byzantine fault-tolerant network based on trajectory point nodes: Consider each trajectory point as a node to construct a Byzantine fault-tolerant network. The voting weight of each node is calculated based on the motion credibility and signal obscuration degree of the trajectory point. The higher the weight, the stronger the verification ability of the node and the more reliable the signal. S32, perform hierarchical verification on the implicit conflict potential energy value: calculate the implicit conflict potential energy value between each pair of trajectory points , the implicit conflict potential energy value represents the potential conflict degree between two trajectory points, and two-level verification is performed to determine the type of conflict; S33. Execute a distributed consensus verification network: After completing the conflict verification, execute a consensus protocol based on Byzantine fault tolerance to ensure that all nodes reach an agreement on the conflict and the verification results. The consensus protocol includes a proposal stage, a verification stage, and a confirmation stage. After the confirmation stage, a trusted verification block is generated. S34. Construct a population trajectory trust distribution map based on the trusted verification block: Through the generation of the trusted verification block, construct a population trajectory trust distribution map, which includes: Vertices: Each vertex of the population trajectory graph represents a trajectory point and includes the motion credibility and signal obscuration degree of the trajectory point. Edges: Each edge of the population trajectory graph represents the conflict relationship between two trajectory points. The conflict relationship weight is calculated through the implicit conflict potential value and the difference in motion credibility between the trajectory points. Trusted region: Determine the trusted region according to the conflict relationship weight and signal obscuration degree. Set the trusted region threshold by calculating the average obscuration degree of all trajectory points.

5. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 4, wherein The two-level verification in S32 specifically includes: Primary verification: If the conflict potential value between two trajectory points is greater than the core conflict threshold, then this pair of trajectory points is marked as a core conflict. The core conflict judgment criterion is to set the core conflict threshold according to the signal obscuration degree of the trajectory point, and the core conflict threshold decreases as the obscuration degree decreases. Secondary verification: If the implicit conflict potential value is between the core conflict and the edge conflict threshold and meets the secondary verification conditions, then an edge conflict is triggered. The edge conflict is determined based on the average value of the motion credibility of the trajectory points.

6. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 4, wherein, In the proposal stage: The trajectory point nodes with high weights propose to broadcast conflict verification requests. In the verification stage: Each trajectory point node calculates a verification signature based on the local implicit conflict potential value. The verification signature is obtained by performing an encrypted hash process on the conflict potential value, motion credibility, and obscuration degree. In the confirmation stage: Collect the verification signatures of all nodes and calculate the weighted sum. If the weighted sum of the verification signatures is greater than two-thirds of the total weight, then a trusted verification block is generated, indicating that the conflict verification results of the current round have reached an agreement.

7. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 5, wherein The specific steps of S4 are as follows: S41. Extract conflict trajectory segments: Screen out all potential conflict trajectory segments from the population trajectory trust distribution map. The population trajectory trust distribution map includes the mutual relationship and edge weights between each pair of trajectory points. Extract the pairs of trajectory points with edge weights greater than the core conflict threshold to form a set of high-risk conflict segments. S42, Potential field gradient optimization: Optimize the positions of the set of high-risk conflict segments to reduce potential conflicts. By calculating the gradient of the trajectory points in the potential field, determine the optimization direction for each trajectory point. During the optimization process, each time the position of a trajectory point is adjusted, make dynamic adjustments based on the distance relationship between the current trajectory point and other trajectory points; S43, Multi-objective path planning: Use the multi-objective path planning method to optimize the positions of the trajectory points. Through the constrained Newton-Raphson algorithm, calculate the optimized set of trajectory points to minimize the deviation between the original trajectory and the optimized trajectory, and consider the potential conflict potential energy value; S44, Cooperative trajectory generation: Perform spatio-temporal interpolation on the optimized trajectory and the original trajectory to generate the final set of cooperative trajectories.

8. The method for analyzing and processing mobile trajectory big data based on Beidou positioning according to claim 7, wherein The extraction of the conflict trajectory segment includes screening from the credible distribution map of the group trajectories the trajectory point pairs associated with the edges that satisfy to form a set of high-risk conflict segments , where is the edge weight between trajectory point and trajectory point , is the core conflict threshold represents the set of high-risk conflict segments represents an element of the conflict segment and belongs to the set , and each conflict segment consists of a set of trajectory points and and the timestamp .

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