Method for realizing path collaborative planning by using an industrial vehicle ADAS system

Abstract

The present invention relates to the technical field of path planning, and particularly relates to a method for realizing collaborative path planning by using an industrial vehicle ADAS system. The method includes: obtaining the driving information of an industrial vehicle and the road distribution of a working area within a current time period, constructing a connectivity graph based on the driving information and the road distribution, and obtaining a route passing index according to the emergency situation type of each fork node at each information collection, the number of connections of each fork node in the connectivity graph, and the number of fork nodes between each fork node and other fork nodes on each connection; clustering the nodes in the connectivity graph, and determining the degree of influence of information interruption according to the number of nodes in the cluster class to which each fork node belongs and the distribution dispersion of the route passing index between each fork node and the fork node directly associated therewith, so as to plan the path of the industrial vehicle in real time. The present invention ensures the effect of industrial vehicle path planning and improves the operation efficiency and safety of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and particularly to a method for realizing collaborative path planning by using an industrial vehicle ADAS system. Background Art

[0002] Path collaborative planning in an industrial vehicle ADAS system realizes environmental perception through multi-sensor integration and data fusion technology, obtains information by combining devices such as lidar, cameras, and GPS, and realizes dynamic acquisition, processing, fusion, and analysis of multi-modal data through intelligent agents (agents). Feature extraction, pattern recognition, and anomaly detection are carried out using large models, and local path optimization is realized. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are used to achieve information sharing and collaboration between vehicles. In a working environment with multi-vehicle collaboration, PID or MPC control algorithms are applied to accurately execute the path, and virtual simulation and field tests are combined to ensure the effectiveness and safety of the system.

[0003] Existing path planning methods generally only plan the driving path of industrial vehicles based on the distance of the path. However, in the actual industrial production process, in order to improve work efficiency, multiple industrial vehicles often cooperate to complete transportation work simultaneously, and some sections may not be able to operate safely and efficiently. Therefore, there are problems with poor planning effects when using existing path planning methods to plan the driving routes of industrial vehicles. Summary of the Invention

[0004] In order to solve the problem of poor planning effects when existing methods are used to plan the driving routes of industrial vehicles, the purpose of the present invention is to provide a method for realizing collaborative path planning by using an industrial vehicle ADAS system, and the specific technical solutions adopted are as follows:

[0005] The present invention provides a method for realizing collaborative path planning by using an industrial vehicle ADAS system, and the method includes the following steps:

[0006] Obtain the driving information of industrial vehicles and the road distribution of the working area during the current time period, and construct a connectivity graph based on the driving information and the road distribution. The nodes in the connectivity graph include the fork nodes of the road and the industrial vehicle position nodes;

[0007] Obtain the influence degree of each fork node at each information collection according to the emergency situation type of each fork node in the connectivity graph at each information collection and the number of connections of each fork node in the connectivity graph; combine the influence degree of each fork node in the connectivity graph at each information collection and the number of fork nodes between each fork node and other fork nodes on each connection line to obtain the route passing index between each fork node and other fork nodes;

[0008] Cluster the nodes in the connected graph, and determine the degree of information interruption impact of each fork node according to the number of nodes in the cluster class to which each fork node belongs and the distribution and discreteness of the route passing indexes between each fork node in the connected graph and the fork nodes directly associated with it.

[0009] Plan the path of the industrial vehicle in real time based on the degree of information interruption impact.

[0010] Preferably, the method for obtaining the impact degree of each fork node at each information collection according to the emergency situation type of each fork node in the connected graph at each information collection and the number of connections of each fork node in the connected graph includes:

[0011] Determine the corresponding preset impact coefficient based on the emergency situation type of the candidate fork node at each information collection in the current time period.

[0012] Obtain the impact degree of the candidate fork node at each information collection according to the number of connections of the candidate fork node in the connected graph and the preset impact coefficient.

[0013] The candidate fork node is any fork node in the connected graph.

[0014] Preferably, the method for obtaining the impact degree of the candidate fork node at each information collection according to the number of connections of the candidate fork node in the connected graph and the preset impact coefficient includes:

[0015] Determine the normalized result of the product of the number of connections of the candidate fork node in the connected graph and the preset impact coefficient corresponding to the candidate fork node at each information collection in the current time period as the impact degree of the candidate fork node at each information collection.

[0016] Preferably, the method for obtaining the route passing index between each fork node and other fork nodes by combining the impact degree of each fork node in the connected graph at each information collection and the number of fork nodes between each fork node and other fork nodes on each connection includes:

[0017] Calculate the first sum value of the impact degrees of the first fork node at all information collections in the current time period; count the total number of all fork nodes between the first fork node and the second fork node on all connections in the connected graph.

[0018] Obtain the route passing index between the first fork node and the second fork node according to the first sum value and the total number.

[0019] The first fork node is any fork node in the connected graph, and the second fork node is any fork node in the connected graph that is not the first fork node and is on the same line as the first fork node.

[0020] Preferably, based on the first sum value and the total quantity, obtaining a route passing index between the first fork node and the second fork node includes:

[0021] Calculating a first product between the first sum value and the total quantity, and taking the negative correlation normalization result of the first product as the route passing index between the first fork node and the second fork node.

[0022] Preferably, a connected graph clustering algorithm is used to cluster all nodes in the connected graph.

[0023] Preferably, determining the information interruption influence degree of each fork node according to the number of nodes in the cluster class to which each fork node belongs and the distribution dispersion of the route passing index between each fork node in the connected graph and the fork node directly associated with it includes:

[0024] Calculating the dispersion degree of the route passing index between the candidate fork node and all fork nodes directly associated with it;

[0025] According to the number of nodes in the cluster class to which the candidate fork node belongs and the dispersion degree, obtaining the information interruption influence degree of the candidate fork node, where the number of nodes in the cluster class to which the candidate fork node belongs is positively correlated with the information interruption influence degree, and the dispersion degree is negatively correlated with the information interruption influence degree.

[0026] Preferably, obtaining the dispersion degree includes: determining the standard deviation of the route passing index between the candidate fork node and all fork nodes directly associated with it as the dispersion degree.

[0027] Preferably, the real-time path planning of the industrial vehicle based on the information interruption influence degree includes:

[0028] Taking the information interruption influence degree as the weight of the path node, and dividing the priority of all path planning according to the large model into the first level, the second level, and the third level;

[0029] When at the first level, taking the corresponding path as the preferred path, and the industrial vehicle performs mobile operations;

[0030] When at the second level, the industrial vehicle decelerates on the corresponding path or selects an alternative path;

[0031] When at the third level, taking braking measures on the industrial vehicle.

[0032] Preferably, constructing a connectivity graph based on the driving information and the road distribution includes:

[0033] Taking the positions of the industrial vehicle at each information collection during the current time period and the intersections of the roads in the working areas passed by the industrial vehicle as nodes in the connectivity graph, and connecting the corresponding nodes in the connectivity graph according to the paths traveled by all industrial vehicles during the current time period to obtain the connectivity graph.

[0034] The present invention has at least the following beneficial effects:

[0035] The present invention first constructs a connectivity graph corresponding to the current time period according to the driving information of the industrial vehicle and the road distribution in the working area during the current time period. Then, according to the emergency situation type of each intersection node at each information collection in the connectivity graph and the number of connection lines of each intersection node in the connectivity graph, the influence degree of the intersection node at each information collection is evaluated. Combining the influence degree of each intersection node at each information collection and the number of intersection nodes between each intersection node and other intersection nodes on each connection line, the passability between different road intersections is evaluated to obtain a route passability index. The larger the route passability index, the higher the passability of the road corresponding to the two intersections. The nodes in the connectivity graph are clustered. According to the number of nodes in the cluster to which each intersection node belongs and the distribution dispersion of the route passability index between each intersection node in the connectivity graph and the intersection nodes directly associated with it, the information interruption influence degree of each intersection node is obtained, and then the path of the industrial vehicle is planned. Through comprehensive analysis of multi-modal data, the system can generate a more optimized path planning scheme, reduce path deviation, and improve the operation efficiency and safety of the vehicle. The present invention collects the driving data of the industrial vehicle in real time to ensure that the vehicle can quickly respond to environmental changes and status updates, so as to make more accurate and timely decisions in path planning. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of 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 following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is a flowchart of a method for realizing path collaborative planning by using an industrial vehicle ADAS system provided by an embodiment of the present invention;

[0038] Figure 2The block diagram of a system for realizing path collaborative planning by using an industrial vehicle ADAS system provided by an embodiment of the present invention. Detailed implementation manners

[0039] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the method for realizing path collaborative planning by using an industrial vehicle ADAS system according to the present invention as follows.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0041] The following specifically describes the specific solution of the method for realizing path collaborative planning by using an industrial vehicle ADAS system provided by the present invention with reference to the accompanying drawings.

[0042] Embodiment of the method for realizing path collaborative planning by using an industrial vehicle ADAS system:

[0043] The specific scenario targeted by this embodiment is: in the path planning of industrial vehicles, in order to ensure work efficiency, the driving path or driving speed of industrial vehicles is adjusted in real time according to the actual situation of each path.

[0044] This embodiment proposes a method for realizing path collaborative planning by using an industrial vehicle ADAS system. As Figure 1 shown, the method for realizing path collaborative planning by using an industrial vehicle ADAS system in this embodiment includes the following steps:

[0045] Step S1, obtain the driving information of the industrial vehicle and the road distribution of the working area within the current time period, and construct a connectivity graph based on the driving information and the road distribution. The nodes in the connectivity graph include the fork nodes of the road and the industrial vehicle position nodes.

[0046] In the industrial environment of industrial vehicles, in order to improve the perception ability of the ADAS system for complex driving environments, it is necessary to optimize sensor data fusion, strengthen multi-source data synchronization and real-time processing. By adopting time series analysis and prediction models, the system can more accurately detect and evaluate the complex driving environment conditions. Combining with the lightweight deep learning model of edge computing, the adaptability and real-time performance of the system are enhanced, and the multi-level risk assessment and optimized decision-making planning can improve the collision risk prediction ability of the system.

[0047] In the path planning of industrial vehicles, edge computing is first required to select suitable edge computing devices, such as industrial computers and embedded systems, which have sufficient computing power to process complex models and real-time data. Industrial vehicles are equipped with a variety of sensors to collect environmental information in real time. Cameras are used to capture images for visual recognition and object detection; lidar provides detailed three-dimensional spatial data to help identify the shape and precise location of objects. These sensor data are locally processed and initially fused by the edge computing device to reduce data transmission latency.

[0048] Using a large model intelligent agent, the edge computing device can analyze and fuse multiple sensor data to improve the accuracy of environmental perception and the reliability of decision-making. The processed information is fed back to the vehicle's control system in real time with low latency, supporting key operations such as path planning, obstacle avoidance, and speed adjustment, ensuring the vehicle operates efficiently and safely in dynamic and complex environments.

[0049] Combine the cameras set at key positions on the industrial vehicle to obtain the image data collected by the cameras; the image data obtained is the complex driving environment information perceived at the position where the current lidar is located; after obtaining the image, mark the obstacles according to the contours of the obstacles scanned by the lidar, and the image acquisition device integrated with the lidar can also obtain the obstacles; and judge the influence coefficient of the industrial vehicle operation on abnormal environment types and mark it with a preset influence coefficient.

[0050] The operation route data of industrial vehicles are also processed. In the digital map, unique identifiers such as "R1" or "J1" are assigned to each route and fork for easy system identification and scheduling. Use paint or reflective stickers to mark the route and fork numbers on the ground for easy identification by the vehicle-mounted vision system of industrial vehicles. Install RFID tags at routes and forks, and the vehicle reads these tags to update position information. Install QR codes or barcodes at key positions, and the vehicle camera scans them to obtain position labels. Use computer vision technology to identify the labels in the image, such as reading the sign text through OCR. Deploy wireless sensor nodes at route nodes and forks to provide unique identifiers to support navigation.

[0051] In this embodiment, the road distribution in the working area is first obtained, where the road is a road on which industrial vehicles can work. Then, the driving information of all industrial vehicles in the working area during the current time period is collected. The current time period is a set of all historical moments whose time interval from the current moment is less than or equal to a preset duration. In this embodiment, the preset duration is 10 minutes. In specific applications, the implementer can set it according to specific circumstances. The driving information includes the positions of all vehicles during the driving process at each collection moment. In this embodiment, the driving information is collected once every second. In specific applications, the implementer can set the collection frequency of the driving information according to specific circumstances.

[0052] So far, this embodiment has collected the driving information of industrial vehicles and the road distribution information of the working area during the current time period. Since the working area of industrial vehicles usually adopts a linear or grid layout, the routes that industrial vehicles can pass through in the working area are usually fixed to ensure that industrial vehicles and other equipment run along a predetermined path. Therefore, the fork of each road is known. The positions of industrial vehicles at each information collection during the current time period and the forks of the roads in the working area passed by the industrial vehicles are used as nodes in the connectivity graph. The corresponding nodes in the connectivity graph are connected according to the paths traveled by all industrial vehicles during the current time period to obtain the connectivity graph, and the unique identifiers of the nodes and the connections, such as "R1" or "J1", remain unchanged. Then, each fork node has at least two connections. For path collaborative planning, the effective planning of its path should be based on the operating environment of industrial vehicles. The possible range of passability impacts at different positions is different. The construction method of the connectivity graph is a prior art and will not be elaborated here.

[0053] Step S2, obtain the influence degree of each fork node at each information collection in the connectivity graph according to the emergency situation type of each fork node at each information collection in the connectivity graph and the number of connections of each fork node in the connectivity graph; combine the influence degree of each fork node at each information collection in the connectivity graph and the number of fork nodes between each fork node and other fork nodes on each connection line to obtain the route passability index between each fork node and other fork nodes.

[0054] In the actual operating environment of industrial vehicles, the extraction of environmental information relies on a variety of sensors (such as LiDAR, cameras, RFID readers, etc.) carried by moving industrial vehicles for collection to keep the route and fork information up-to-date. It has obvious intermittent information extraction characteristics. During its driving process, an industrial vehicle will obtain the identification information of its current position by scanning ground marks, two-dimensional codes or RFID tags, etc. These position information and environmental data are uploaded to the central system or local computing unit in real time through wireless communication technologies (such as Wi-Fi, Bluetooth). The central system will analyze and process the received data to update the route and fork information in the digital map. This includes identifying any possible changes, such as the occurrence of abnormal driving conditions, route changes or new path selections. In this way, the system can quickly correct any inaccurate or outdated map data. The updated information is fed back to the industrial vehicle in real time, enabling it to dynamically adjust the navigation path to improve driving efficiency and avoid potential dangers. Therefore, the accuracy of the path cooperation planning method for industrial vehicles is mainly affected by the accuracy of information update.

[0055] Since the storage area usually adopts a straight or grid layout, the routes in the working area are usually fixed to ensure that industrial vehicles and other equipment run along the predetermined paths. The navigation paths of industrial vehicles are relatively regular. For the path selection in the path planning process of industrial vehicles, it more depends on the route forks and the relevant connectivity between paths, that is, its impact on the possible passing conditions of multiple small vehicles on the route. It should be analyzed more from the structural influence changes of the grid distribution of the navigation path network. In fact, it is considered from the changes in path-like traffic flow. The path planning actually mainly manifests as the specific consideration of the passability of paths and forks.

[0056] In this embodiment, first, the influence degree of each fork node in the connectivity graph at each information collection time will be evaluated according to the emergency situation type of each fork node at each information collection time and the number of connecting lines of each fork node in the connectivity graph.

[0057] Specifically, this embodiment takes a fork node in the connectivity graph as an example for illustration, and the method provided in this embodiment can be used to process other fork nodes.

[0058] Any fork node in the connectivity graph is denoted as a candidate fork node. Based on the emergency situation type of the candidate fork node at each information collection time in the current time period, the preset influence coefficient corresponding to the emergency situation type is determined; the preset influence coefficient is set artificially by the staff according to the emergency situation type. The more urgent the emergency situation type is, the greater its preset influence coefficient is, and the specific value is set according to the specific situation.

[0059] The normalization result of the product of the number of connections of the candidate fork node in the connected graph and the preset influence coefficient corresponding to each information collection of the candidate fork node within the current time period is determined as the influence degree of the candidate fork node at each information collection.

[0060] In this embodiment, a specific calculation formula for the influence degree is given. The influence degree of the i-th fork node in the connected graph at the j-th information collection can be expressed as:

[0061]

[0062] Among them, represents the influence degree of the i-th fork node at the j-th information collection, represents the number of connections of the i-th fork node in the connected graph, represents the preset influence coefficient corresponding to the i-th fork node at the j-th information collection within the current time period.

[0063] represents the influence of the type of emergency occurring on the surrounding routes of the location where the i-th fork node is located. The larger this product is, the greater the influence range of the i-th fork node on the nearby route planning, that is, the greater the influence degree of the i-th fork node at the j-th information collection.

[0064] Due to the regular characteristics of the navigation path of industrial vehicles and the unique characteristics of the connectivity between nodes, that is, industrial vehicles can only update information through the set regular paths, the correlation between two nodes on the connected graph is manifested as the passability corresponding to the node connection. Therefore, the navigation path of industrial vehicles mainly depends on the connection characteristics between nodes, that is, the fewer the number of nodes between nodes, the stronger the passability on its path. Based on this, in this embodiment, next, according to the influence degree of each fork node at each information collection and the number of fork nodes between each fork node and other fork nodes on each connection line, the route passability index between each fork node and other fork nodes is calculated. The route passability index is used to reflect the passability of the path.

[0065] Next, this embodiment will take two fork nodes in the connected graph as an example for illustration.

[0066] Specifically, any fork node in the connected graph is denoted as the first fork node, and the second fork node is any fork node that is not the first fork node and is on the same line as the first fork node; calculate the first sum value of the influence degrees of the first fork node during all information collections in the current time period; count the total number of all fork nodes between the first fork node and the second fork node on all lines in the connected graph. Calculate the first product between the first sum value and the total number, and use the negative correlation normalization result of the first product as the route passing index between the first fork node and the second fork node.

[0067] In this embodiment, a specific calculation formula for the route passing index is given. The route passing index between the i-th fork node and the k-th fork node can be expressed as:

[0068]

[0069] Where, represents the route passing index between the i-th fork node and the k-th fork node, represents the influence degree of the i-th fork node during the j-th information collection. J represents the number of times the information of the i-th fork node is collected in the current time period, that is, the number of times the industrial vehicle passes through the i-th fork node in the current time period; represents the total number of all fork nodes between the i-th fork node and the k-th fork node on all lines in the connected graph. exp( ) represents the exponential function with the natural constant as the base.

[0070] represents the first sum value, represents the first product. This product represents the structural correlation relationship between the i-th fork node and the k-th fork node and is used for subsequent planning impact analysis. When the influence degree of the i-th fork node in the current time period is smaller and the first product is also smaller, the route between the i-th fork node and the k-th fork node is more suitable for passing, that is, the route passing index between the i-th fork node and the k-th fork node is larger.

[0071] By using the above method, the route passing index between each fork node and other fork nodes can be obtained.

[0072] Step S3: Cluster the nodes in the connected graph, and determine the information interruption influence degree of each fork node according to the number of nodes in the cluster to which each fork node belongs and the distribution and discreteness of the route passing indexes between each fork node in the connected graph and the fork nodes directly associated with it.

[0073] When using the industrial vehicle ADAS system to achieve path collaborative planning for industrial vehicles, the priority of the industrial vehicle driving path planning has a certain relationship with the positions of other industrial vehicles. It is necessary to reduce possible collision situations in terms of area. However, during the driving process of industrial vehicles, by scanning ground marks, two-dimensional codes, RFID tags, etc., the identification information of the current position is obtained to keep the route and fork information up-to-date, and it has obvious intermittent information extraction characteristics. Corresponding to the connected graph, it is the structural manifestation of the connected graph, that is, only when there are industrial vehicles obtaining road condition information on the forks and routes at the data collection moment, there is information data on the corresponding nodes of the connected graph.

[0074] Use the connected graph clustering algorithm to cluster all nodes in the connected graph corresponding to the current time period to obtain multiple clusters. Each cluster is regarded as the regional influence range of the route passing condition in this local area. Then, for each node, the more nodes in the cluster to which it belongs during the collection period and the stronger the change characteristics, the greater the degree of intermittent influence of its node information.

[0075] Still taking the candidate fork node as an example for illustration. Specifically, the standard deviation of the route passing indices between the candidate fork node and all fork nodes directly associated with it is used as the degree of dispersion of the route passing indices between the candidate fork node and all fork nodes directly associated with it. The larger the standard deviation, the more discrete the distribution of the route passing indices, that is, the greater the degree of dispersion. According to the number of nodes in the cluster to which the candidate fork node belongs and the degree of dispersion, the degree of intermittent influence of the candidate fork node's information is obtained. The number of nodes in the cluster to which the candidate fork node belongs has a positive correlation with the degree of intermittent influence of the information, and the degree of dispersion has a negative correlation with the degree of intermittent influence of the information.

[0076] Among them, the positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by the actual application. The negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtractive relationship, a divisive relationship, etc., which is determined by the actual application.

[0077] In this embodiment, a specific calculation formula for the degree of intermittent influence of information is given. The degree of intermittent influence of the i-th fork node in the connected graph can be expressed as:

[0078]

[0079] Among them, represents the degree of intermittent influence of the i-th fork node in the connected graph, Indicates the number of nodes in the cluster to which the \(i\)-th fork node belongs. Indicates the standard deviation of the route passing index between the \(i\)-th fork node and all fork nodes directly associated with it in the connected graph, that is, the degree of dispersion; \(norm()\) represents the normalization function. Indicates a preset first adjustment parameter.

[0080] It should be noted that for any fork node, the fork nodes directly associated with it are on the same line as this fork node, and there are no other fork nodes between these two fork nodes.

[0081] Introducing the preset first adjustment parameter in the calculation formula of the information interruption influence degree is to prevent the denominator from being 0. In this embodiment, the preset first adjustment parameter is 0.01. In specific applications, the implementer can set it according to specific circumstances. Used to reflect the influence degree of node information under the influence of information interruption. The larger this value is, the greater the influence degree of information interruption of the \(i\)-th fork node in the connected graph.

[0082] By adopting the above method, the information interruption influence degree of each fork node can be obtained.

[0083] Step S4, based on the information interruption influence degree, plan the path of the industrial vehicle in real time.

[0084] In this embodiment, for the information interruption influence degree of each fork node in step S3, the information interruption influence degree is used as the weight of the path node, and then according to the large model, all path planning priorities are divided into the first level, the second level, and the third level. The first level is the low level, the second level is the medium level, and the third level is the high level. The specific division standard can be divided by setting thresholds, which will not be elaborated here; further, according to the path planning priority, the planned path of the industrial vehicle is adjusted in real time. When it is at the first level, it means that there is no influence situation on the path to be planned, and the corresponding path can be used as the preferred path for the industrial vehicle to move and operate; when it is at the second level, it means that there may be an emergency situation affecting a local area on this path, and it is recommended to slow down or select an alternative route; when it is at the third level, the industrial vehicle must immediately take braking to stop and wait until the path is passable, and then re-plan the driving route. The ultimate goal is to ensure that in a complex industrial environment, the industrial vehicle can operate safely and efficiently, and effectively improve the path planning accuracy and safety of the industrial vehicle ADAS system, and the obtained planned path is safer and more efficient.

[0085] In this embodiment, first, a connectivity graph corresponding to the current time period is constructed based on the driving information of the industrial vehicle and the road distribution in the working area during the current time period. Then, according to the emergency situation type of each fork node in the connectivity graph during each information collection and the number of connections of each fork node in the connectivity graph, the influence degree of the fork node during each information collection is evaluated. Combining the influence degree of each fork node during each information collection and the number of fork nodes between each fork node and other fork nodes on each connection line, the passability between different road forks is evaluated to obtain a route passability index. The larger the route passability index, the higher the passability of the road between the corresponding two fork nodes. Clustering the nodes in the connectivity graph, according to the number of nodes in the cluster class to which each fork node belongs and the distribution dispersion of the route passability index between each fork node in the connectivity graph and the fork node directly associated with it, the information interruption influence degree of each fork node is obtained, and then the path of the industrial vehicle is planned. Through comprehensive analysis of multi-modal data, the system can generate a more optimized path planning scheme, reduce path deviation, and improve the operation efficiency and safety of the vehicle. In this embodiment, the driving data of the industrial vehicle during driving is collected in real time to ensure that the vehicle can quickly respond to environmental changes and status updates, so as to make more accurate and timely decisions in path planning. Through intelligent agents, dynamic collection, processing, fusion, and analysis of multi-modal data and dynamic data are realized, and large models are used for feature extraction, pattern recognition, and anomaly detection. Using the trained large model for efficient fusion of multi-modal data and data analysis and prediction to improve the accuracy of path collaborative planning of industrial vehicles.

[0086] System embodiment for realizing path collaborative planning by using the industrial vehicle ADAS system:

[0087] Refer to Figure 2 , which shows a structural block diagram of a system for realizing path collaborative planning by using the industrial vehicle ADAS system provided by an embodiment of the present invention. The system may include a data collection module, a first calculation module, a second calculation module, and a path planning module.

[0088] Among them, the data collection module is used to obtain the driving information of the industrial vehicle and the road distribution in the working area during the current time period, and construct a connectivity graph based on the driving information and the road distribution. The nodes in the connectivity graph include the fork nodes of the road and the industrial vehicle position nodes;

[0089] A first calculation module, configured to obtain the influence degree of each fork node in the connected graph during each information collection according to the emergency situation type of each fork node in the connected graph during each information collection and the number of connections of each fork node in the connected graph; combine the influence degree of each fork node in the connected graph during each information collection, and the number of fork nodes between each fork node and other fork nodes on each connection line, to obtain the route passing index between each fork node and other fork nodes;

[0090] A second calculation module, configured to cluster the nodes in the connected graph, and determine the information interruption influence degree of each fork node according to the number of nodes in the cluster class to which each fork node belongs and the distribution and dispersion of the route passing indexes between each fork node in the connected graph and the fork nodes directly associated with it;

[0091] A path planning module, configured to plan the path of the industrial vehicle in real time based on the information interruption influence degree.

[0092] It should be understood that Figure 2 The structural block diagram and modules of a system for realizing collaborative path planning by using an industrial vehicle ADAS system shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented by using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented by using computer-executable instructions and / or included in the processor control code. For example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can be implemented not only by a hardware circuit of a programmable hardware device such as a very large scale integrated circuit or a gate array, a semiconductor such as a logic chip or a transistor, or a field programmable gate array or a programmable logic device, but also by software executed by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0093] For more details about the above-mentioned various modules, reference can be made to other positions in this specification, and details will not be elaborated here.

[0094] In other embodiments, there is also provided a device for implementing path collaborative planning by using an industrial vehicle ADAS system, including a memory and a processor. The memory is used to store executable program codes, and the processor is used to call and run the executable program codes from the memory, so that the device executes the above method for implementing path collaborative planning by using an industrial vehicle ADAS system. The device may specifically be a chip, a component or a module. The chip may include a processor and a memory connected thereto. Among them, the memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the method for implementing path collaborative planning by using an industrial vehicle ADAS system provided in the above embodiments.

[0095] In other embodiments, there is also provided a computer program product. When the computer program product runs on a computer, it causes the computer to execute the above related steps to implement the method for implementing path collaborative planning by using an industrial vehicle ADAS system provided in the above embodiments.

[0096] In other embodiments, there is also provided a computer-readable storage medium. The computer-readable storage medium stores computer program codes. When the computer program codes run on a computer, it causes the computer to execute the above related method steps to implement the method for implementing path collaborative planning by using an industrial vehicle ADAS system provided in the above embodiments.

[0097] Among them, the provided system, electronic device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be elaborated here.

[0098] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for realizing path collaborative planning using an industrial vehicle ADAS system, characterized in that: The method comprises the following steps: Acquire the driving information of the industrial vehicle and the road distribution of the working area in the current time period, and construct a connected graph based on the driving information and the road distribution, wherein the nodes in the connected graph include the fork nodes of the road and the position nodes of the industrial vehicle; Obtaining the influence of each fork node at each information collection according to the emergency situation type of each fork node in the connected graph at each information collection and the number of links of each fork node in the connected graph; obtaining the route traffic index between each fork node and other fork nodes by combining the influence of each fork node at each information collection in the connected graph and the number of fork nodes between each fork node and other fork nodes on each link; Clustering the nodes in the connectivity graph, and determining the degree of information discontinuity impact of each fork node according to the number of nodes in the cluster to which each fork node belongs and the distribution discreteness of the route traffic index between each fork node and the fork nodes directly associated with it in the connectivity graph; Plan the paths of industrial vehicles in real time based on the impact of information discontinuities; The influence degree of each fork node in each information collection is obtained, including: Based on the emergency situation type of the candidate fork node at each information collection in the current time period, determine the corresponding preset impact coefficient; According to the number of links of the candidate fork node in the connectivity graph and the preset influence coefficient, the influence degree of the candidate fork node at each information collection is obtained; The candidate fork node is any fork node in the connected graph.

2. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: The step of obtaining the influence degree of the candidate fork node at each information collection according to the number of links of the candidate fork node in the connectivity graph and the preset influence coefficient includes: The normalized result of the product of the number of links of the candidate fork node in the connectivity graph and the preset influence coefficient corresponding to each information collection of the candidate fork node in the current time period is determined as the influence degree of the candidate fork node at each information collection.

3. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: The method combines the influence degree of each fork node in the connectivity graph at each information collection and the number of fork nodes between each fork node and other fork nodes on each connection line to obtain the route traffic index between each fork node and other fork nodes, including: Calculate the first sum of the influence of the first fork node during all information collections in the current time period; count the total number of all fork nodes between the first fork node and the second fork node on all links in the connected graph; Obtaining a route traffic index between a first fork node and a second fork node according to the first sum value and the total number; The first fork node is any fork node in the connected graph, and the second fork node is any fork node in the connected graph except the first fork node and on the same line as the first fork node.

4. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 3, characterized in that: Obtaining a route traffic index between a first fork node and a second fork node according to the first sum value and the total number, including: A first product between the first sum and the total number is calculated, and a negative correlation normalization result of the first product is used as a route traffic index between the first fork node and the second fork node.

5. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: A connected graph clustering algorithm is used to cluster all nodes in the connected graph.

6. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: The method of determining the degree of information discontinuity influence of each fork node according to the number of nodes in the cluster to which each fork node belongs and the distribution discreteness of the route traffic index between each fork node and the fork node directly associated with it in the connectivity graph comprises: Calculate the degree of dispersion of the route traffic index between the candidate fork node and all fork nodes directly associated with it; According to the number of nodes in the cluster to which the candidate fork node belongs and the degree of discreteness, the degree of influence of information discontinuity of the candidate fork node is obtained. The number of nodes in the cluster to which the candidate fork node belongs is positively correlated with the degree of influence of information discontinuity, and the degree of discreteness is negatively correlated with the degree of influence of information discontinuity.

7. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 6, characterized in that: The acquisition of the discrete degree includes: determining the standard deviation of the route traffic index between the candidate fork node and all the fork nodes directly associated with the candidate fork node as the discrete degree.

8. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: The real-time planning of the path of the industrial vehicle based on the degree of influence of information discontinuity includes: The degree of influence of the information discontinuity is used as the weight of the path node, and all path planning priorities are divided into the first level, the second level and the third level according to the large model; When at the first level, the corresponding path is used as the preferred path, and the industrial vehicle performs mobile operations; When in the second level, the industrial vehicle decelerates on the corresponding path or selects an alternative path; When in the third level, braking measures are taken on the industrial vehicle.

9. The method for realizing path collaborative planning using an industrial vehicle ADAS system according to claim 1, characterized in that: The constructing a connectivity graph based on the driving information and the road distribution includes: The positions of industrial vehicles at each time of information collection in the current time period and the forks in the working area roads passed by the industrial vehicles are taken as nodes in the connected graph. The corresponding nodes in the connected graph are connected according to the paths traveled by all industrial vehicles in the current time period to obtain a connected graph.

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