CN117910196A - Dynamic reliability assessment method and device for unmanned forklift running network - Google Patents
Dynamic reliability assessment method and device for unmanned forklift running network Download PDFInfo
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Abstract
The invention discloses a dynamic reliability assessment method and a device for an unmanned forklift operation network, wherein a complex network method is adopted to model the unmanned forklift operation network, and an initial network is constructed; defining occupied paths corresponding to each directed edge and occupied starting time and duration time by adopting a time window; acquiring the running state of the unmanned forklift obtained by real-time monitoring, superposing the running state on an initial network for representation, forming a vehicle-road integrated traffic network, counting the occupied time of the directed edge of the traffic network in the future fixed time from the starting moment according to a time window, and calculating the load of the directed edge of the traffic network at the starting moment; according to the traffic network risk index in the global operation range of the load calculation, the early warning is carried out according to the traffic network risk index, the dynamic time-space characteristics of the operation network can be comprehensively reflected, and the timely and accurate dynamic reliability early warning is carried out.
Description
Technical Field
The invention relates to the field of unmanned forklifts, in particular to a dynamic reliability assessment method and device for an unmanned forklifts operation network.
Background
In recent years, in the hot tide of logistics robots, unmanned forklifts are an important direction of gradual rise, the market scale is increased by 16 times in the past four years, and the future development space is huge. Unmanned fork truck has fused fork truck and AGV technique, mainly falls into antedisplacement formula, tray transport formula and heap high formula (including the balanced heavy) at present, and the vast majority adopts laser SLAM to navigate, and the operational route is comparatively nimble, and is also more complicated than traditional grid roadmap. Compared with a common AGV, the unmanned forklift has wider application range, higher operation intensity and good performance in the scenes of production line transportation, high-level storage, off-warehouse delivery and the like; in addition, the dynamic characteristics of the two are also greatly different, the unmanned forklift needs a larger safety distance when running due to the influence of size, load and the like, and the fork arm is in unilateral operation, so that the direction of the forklift body is often required to be adjusted through an additional turning path.
The road is a basic element of unmanned forklift dispatching, but the research is still not deep enough at present. The prior art mainly focuses on unmanned forklifts, but only few related devices such as door control devices (such as rolling doors, airtight doors, air shower doors, fireproof rolling doors and the like, lifting devices (such as elevators, freight lifts, automatic loading and unloading points of lifts and the like, such as conveyor belts, roller lines, stereoscopic warehouses) and the like are involved, because the early logistics robots do not have long-distance carrying capacity of crossing workshops and floors and tend to only operate in local areas, but in recent years, along with rapid development of sensors and navigation positioning technologies, the current logistics robots have long-distance operation capacity, the operating route range is expanded increasingly, the intersection degree is increased increasingly complex, interaction with logistics peripheral devices is increased rapidly, and the operation is required to be carried out under the condition of mixing of people and machines, which means that the uncertainty of the operation network is greatly increased, and the implementation effect of a scheduling scheme is obviously influenced.
Disclosure of Invention
The technical problems mentioned above are solved. The embodiment of the application aims to provide a dynamic reliability evaluation method and device for an unmanned forklift running network, which are used for solving the technical problems mentioned in the background art section.
In a first aspect, the present invention provides a method for evaluating dynamic reliability of an unmanned forklift operating network, including the following steps:
S1, modeling an unmanned forklift operation network by adopting a complex network method, and constructing an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes;
s2, defining occupied paths corresponding to each directed edge and occupied starting time and duration time by adopting a time window;
S3, acquiring the running state of the unmanned forklift obtained through real-time monitoring, superposing the running state on an initial network for representation, forming a vehicle-road integrated traffic network, counting the occupied time of the directed edge of the traffic network in the future fixed time from the starting moment according to a time window, and calculating the load of the directed edge of the traffic network at the starting moment;
and S4, calculating a traffic network risk index in a global operation range according to the load, and carrying out early warning according to the traffic network risk index.
Preferably, the initial network is denoted as g= (V, E), where v= { V 1,v2,…,vn }, V denotes a set of operation nodes, V n denotes an nth operation node, each operation node has three-dimensional spatial coordinate attributes, e= { E 1,e2,…,em }, E denotes a set of directed edges, E m denotes an mth directed edge, a type of operation node includes an navigation point, a standby point, a charging point, or an interaction point, a distance between two operation nodes does not exceed a preset distance, and a type of path includes a unidirectional path, a bidirectional path, a path through a door control device, or a path through a lifting device.
Preferably, in step S2, when the path corresponding to the directed edge is occupied by the unmanned forklift, the length of the time window represents the passing time of the unmanned forklift; when the path corresponding to the directed edge is occupied by the planned event or the abnormal event, the length of the time window represents the duration of the planned event or the abnormal event.
Preferably, the operation state includes a position, a speed, an electric quantity, a task state, a cargo state, a fault state and a remaining path, and in step S3, the operation state is superimposed on the initial network to be represented, which specifically includes:
And associating the operation nodes or the directed edges on the initial network with the unmanned forklift through an association data table established by taking the unique identification of the unmanned forklift as a main key, and updating the operation state of the unmanned forklift in a corresponding association record in the association data table.
Preferably, in step S3, the time period that the directed edge of the traffic network is occupied in the future fixed time from the starting time is counted according to the time window, and the load of the directed edge of the traffic network at the starting time is calculated, which specifically includes:
The load L i (t) of the ith directed edge of the traffic network at the starting moment is calculated by adopting the following steps:
Wherein, T i (T) is the occupied time length of the ith directed edge e i in the future fixed time T c of the starting time T, and the occupied time length is obtained by counting through the time window registered on the ith directed edge e i, and L i (T) e [0,1].
Preferably, in step S4, a traffic network risk indicator in a global operation range is calculated according to a load, and specifically includes:
calculating a traffic network risk index F (t) in a global operation range by adopting the following steps:
Wherein G ′(t)＝(V′(t),E′ (T)) is a sub-graph formed by the starting point and the target point of all jobs in [ T, t+t c ] time and the running node and the directed edge contained in the shortest path of the starting point and the target point in the traffic network, V ′ (T) is a set of running nodes in the sub-graph, E ′ (T) is a set of directed edges in the sub-graph, E j is the j-th directed edge in the sub-graph, and M is the total number of elements in E ′ (T).
Preferably, in step S4, early warning is performed according to the traffic network risk index, which specifically includes:
and (3) executing the steps S1-S4 every interval time period to evaluate the traffic network risk index, and triggering early warning in response to determining that the traffic network risk index exceeds a preset threshold.
In a second aspect, the present invention provides a dynamic reliability assessment device for an unmanned forklift operating network, including:
The modeling module is configured to model the unmanned forklift operation network by adopting a complex network method, and construct an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes;
the time window definition module is configured to define occupied paths corresponding to each directed edge and the starting time and duration of occupied paths by adopting a time window;
the running state superposition module is configured to acquire the running state of the unmanned forklift obtained by real-time monitoring, superimpose the running state on the initial network for representation, form a traffic network with integrated vehicle and road, count the occupied duration of the directed edge of the traffic network in the future fixed time from the starting moment according to the time window, and calculate the load of the directed edge of the traffic network at the starting moment;
And the early warning module is configured to calculate a traffic network risk index in a global operation range according to the load and perform early warning according to the traffic network risk index.
In a third aspect, the present invention provides an electronic device comprising one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the implementations of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the dynamic reliability assessment method for the unmanned forklift running network, disclosed by the invention, the unmanned forklift running network is subjected to running path integrated modeling through a complex network method, so that an initial network is obtained, the occupied path corresponding to each directed edge and the occupied starting time and duration are defined in the initial network by adopting a time window, and the dynamic time-space characteristics of the running network are conveniently and comprehensively reflected.
(2) According to the method for evaluating the dynamic reliability of the unmanned forklift operation network, the operation state of the unmanned forklift is overlapped in the initial network to be expressed, so that the vehicle-road integrated traffic network is formed, and the operation state of the unmanned forklift and the interaction relation between peripheral equipment of the unmanned forklift can be dynamically monitored.
(3) According to the dynamic reliability assessment method for the unmanned forklift running network, which is provided by the invention, the occupied time length of the directed edge in the future fixed time from the time t is counted through the time window, the load of the directed edge at the time t is calculated according to the time length, the traffic network risk index is calculated according to the load, and the risk coefficient of the traffic network can be assessed periodically, so that the reliability early warning can be carried out timely, accurately and dynamically.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an exemplary device frame pattern to which an embodiment of the present application may be applied;
fig. 2 is a flow chart of a dynamic reliability evaluation method of an unmanned forklift operating network according to an embodiment of the present application;
Fig. 3 is a schematic diagram of a traffic network construction process of a dynamic reliability evaluation method of an unmanned forklift operating network according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a time window of a dynamic reliability assessment method for an unmanned forklift operating network according to an embodiment of the present application;
Fig. 5 is a schematic diagram of a dynamic reliability evaluation device of an unmanned forklift operating network according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of a computer device suitable for use in implementing an embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 illustrates an exemplary device architecture 100 of a dynamic reliability assessment method of an unmanned forklift operating network or a dynamic reliability assessment device of an unmanned forklift operating network to which embodiments of the present application may be applied.
As shown in fig. 1, the apparatus architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various applications, such as a data processing class application, a file processing class application, and the like, may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smartphones, tablets, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., software or software modules for providing distributed services) or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, such as a background data processing server processing files or data uploaded by the terminal devices 101, 102, 103. The background data processing server can process the acquired file or data to generate a processing result.
It should be noted that, the method for evaluating the dynamic reliability of the unmanned forklift operation network provided by the embodiment of the present application may be executed by the server 105, or may be executed by the terminal devices 101, 102, 103, and accordingly, the device for evaluating the dynamic reliability of the unmanned forklift operation network may be set in the server 105, or may be set in the terminal devices 101, 102, 103.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the processed data does not need to be acquired from a remote location, the above-described apparatus architecture may not include a network, but only a server or terminal device.
Fig. 2 shows a dynamic reliability evaluation method for an unmanned forklift operation network, which is provided by the embodiment of the application, and includes the following steps:
s1, modeling an unmanned forklift operation network by adopting a complex network method, and constructing an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes.
In a specific embodiment, the initial network is denoted as g= (V, E), where v= { V 1,v2,…,vn }, V denotes a set of running nodes, V n denotes an nth running node, each running node has three-dimensional spatial coordinate properties, e= { E 1,e2,…,em }, E denotes a set of directed edges, and E m denotes an mth directed edge. The type of the operation node comprises a navigation point, a standby point, a charging point or an interaction point, the distance between the two operation nodes does not exceed a preset distance, and the type of the path comprises a one-way path, a two-way path, a path through door control equipment or a path through lifting equipment.
Specifically, unlike the grid map of a common AGV, the unmanned forklift is more flexible to operate, and the unmanned forklift operating network is more like a natural network, and can be composed of various operating nodes and connecting edges (i.e. paths) between the operating nodes. Therefore, the unmanned forklift operation network can be modeled by adopting a complex network method to obtain an initial network, a set E= { E 1,e2,…,em } of directed edges can be constructed according to the connection relation between operation nodes, and the representation of four typical paths in the complex network is shown in fig. 3 (a) -3 (d). The initial network modeled in conjunction with the set of running nodes and the set of directed edges is shown in fig. 3 (e). The type of the running node covers: waypoints, standby points, charging points, interaction points (including various door control equipment, lifting equipment, and feeding and discharging points of a conveyor belt, a roller line, a stereoscopic warehouse and the like). In practice, the distance between two operation nodes is usually not more than 5 meters, so that the unmanned forklift can timely correct coordinates, and accumulated errors are reduced.
S2, defining occupied paths corresponding to each directed edge and occupied starting time and duration by adopting a time window.
In a specific embodiment, in step S2, when the path corresponding to the directed edge is occupied by the unmanned forklift passing, the length of the time window represents the passing time of the unmanned forklift; when the path corresponding to the directed edge is occupied by the planned event or the abnormal event, the length of the time window represents the duration of the planned event or the abnormal event.
Specifically, in addition to basic information such as length and radians, each directional edge has a time window attribute, and referring to fig. 4, the corresponding path is occupied and the start time and duration of the occupation are indicated through the time window. When the path is occupied by the unmanned forklift, the time window length indicates the passing time of the unmanned forklift; when occupied by a schedule, the time window length indicates the time for which the schedule is to last; when the emergency is occupied by an emergency, such as an obstacle or a fork truck fault, and the recovery time cannot be confirmed, the duration is initially set to be a relatively large constant, such as 24 hours, and the time window is updated immediately after the emergency is released. Therefore, the time window with the directed edge can represent various state information and comprehensively reflect the dynamic time-space characteristics of the initial network. Therefore, a time window is adopted to describe the state information of the path corresponding to each directed edge.
S3, acquiring the running state of the unmanned forklift obtained through real-time monitoring, superposing the running state on an initial network to represent, forming a vehicle-road integrated traffic network, counting the occupied time of the directed edge of the traffic network in the future fixed time from the starting moment according to a time window, and calculating the load of the directed edge of the traffic network at the starting moment.
In a specific embodiment, the running state includes a position, a speed, an electric quantity, a task state, a cargo state, a fault state and a remaining path, and in step S3, the running state is superimposed on the initial network to be represented, which specifically includes:
And associating the operation nodes or the directed edges on the initial network with the unmanned forklift through an association data table established by taking the unique identification of the unmanned forklift as a main key, and updating the operation state of the unmanned forklift in a corresponding association record in the association data table.
In a specific embodiment, in step S3, the time period that the directed edge of the traffic network is occupied in the future fixed time from the starting time is counted according to the time window, and the load of the directed edge of the traffic network at the starting time is calculated, which specifically includes:
The load L i (t) of the ith directed edge of the traffic network at the starting moment is calculated by adopting the following steps:
Wherein, T i (T) is the occupied time length of the ith directed edge e i in the future fixed time T c of the starting time T, and the occupied time length is obtained by counting through the time window registered on the ith directed edge e i, and L i (T) e [0,1].
Specifically, the operation state of the unmanned forklift can be monitored in real time by the unmanned forklift system, and the unmanned forklift comprises: location, speed, power, task status (idle, charging, handling), cargo status, fault status (off-route, anti-collision trigger, etc.), remnant path, etc. And (3) superposing dynamic data such as the running state of the unmanned forklift and the like on the initial network to represent, and forming a vehicle-road integrated traffic network, as shown in fig. 3 (f). Specifically, the operation nodes or the directed edges on the initial network are associated with the unmanned forklift through the association data table taking the unmanned forklift ID as a main key, namely when the unmanned forklift is positioned at a new operation node or a directed edge, corresponding association records in the association data table are updated. 3 (g) -3 (i) respectively plot the degree distribution, the shortest path length distribution, and the cluster coefficient distribution of the traffic network, it can be found that in the traffic network, the degree of the running node is concentrated near 4 due to more bidirectional paths, and the traffic network does not have a 'no-scale' characteristic; and the clustering coefficient is very small and does not have the characteristic of 'small world'.
On the basis, in order to measure the reliability of the traffic network, the load of the ith directed edge E i E at the moment t is defined. T i (T) is the length of time that the directed edge e i is occupied within a fixed future time T c (e.g., 10 minutes) of the starting time T, and can be counted through a time window registered on the ith directed edge e i, the higher the load on a directed edge, the higher the potential risk that the directed edge exists.
And S4, calculating a traffic network risk index in a global operation range according to the load, and carrying out early warning according to the traffic network risk index.
In a specific embodiment, step S4 specifically includes:
calculating a traffic network risk index F (t) in a global operation range by adopting the following steps:
Wherein G ′(t)＝(V′(t),E′ (T)) is a sub-graph formed by the starting point and the target point of all jobs and the running node and the directed edge included in the shortest path of the starting point and the target point in the time [ T, t+t c ] in the traffic network, V ′ (T) is a set of running nodes in the sub-graph, E ′ (T) is a set of directed edges in the sub-graph, E j is the j-th directed edge in the sub-graph, and M is the total number of elements in E ′ (T), where the shortest path of the starting point and the target point can be calculated by using the Floyd algorithm or Dijkstra algorithm, and will not be repeated here.
In a specific embodiment, the early warning in step S4 according to the traffic network risk indicator specifically includes:
and (3) executing the steps S1-S4 every interval time period to evaluate the traffic network risk index, and triggering early warning in response to determining that the traffic network risk index exceeds a preset threshold.
In particular, most of the traffic network is only stopped nodes, since there is no logistics service between all the operating nodes in different time periods. Therefore, the traffic network risk index is evaluated every certain period, and when the traffic network risk index exceeds a preset threshold value, the preset threshold value can be set to be 0.5, and early warning is triggered.
With further reference to fig. 5, as an implementation of the method shown in the foregoing drawings, the present application provides an embodiment of a dynamic reliability evaluation apparatus for an unmanned forklift operating network, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
The embodiment of the application provides a dynamic reliability evaluation device of an unmanned forklift operation network, which comprises the following components:
The modeling module 1 is configured to model an unmanned forklift operation network by adopting a complex network method, and construct an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes;
A time window definition module 2 configured to define, using a time window, a time window at which each path corresponding to the directed edge is occupied and a start time and duration of the occupancy;
The running state superposition module 3 is configured to acquire the running state of the unmanned forklift obtained by real-time monitoring, superpose the running state on the initial network to represent, form a traffic network with integrated vehicle and road, count the occupied duration of the directed edge of the traffic network in the future fixed time from the starting moment according to the time window, and calculate the load of the directed edge of the traffic network at the starting moment;
And the early warning module 4 is configured to calculate a traffic network risk index in a global operation range according to the load and perform early warning according to the traffic network risk index.
Referring now to fig. 6, there is illustrated a schematic diagram of a computer apparatus 600 suitable for use in an electronic device (e.g., a server or terminal device as illustrated in fig. 1) for implementing an embodiment of the present application. The electronic device shown in fig. 6 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
As shown in fig. 6, the computer apparatus 600 includes a Central Processing Unit (CPU) 601 and a Graphics Processor (GPU) 602, which can perform various appropriate actions and processes according to programs stored in a Read Only Memory (ROM) 603 or programs loaded from a storage section 609 into a Random Access Memory (RAM) 604. In the RAM604, various programs and data required for the operation of the apparatus 600 are also stored. The CPU 601, GPU602, ROM 603, and RAM604 are connected to each other through a bus 605. An input/output (I/O) interface 606 is also connected to the bus 605.
The following components are connected to the I/O interface 606: an input portion 607 including a keyboard, a mouse, and the like; an output portion 608 including a speaker, such as a Liquid Crystal Display (LCD), etc.; a storage portion 609 including a hard disk and the like; and a communication section 610 including a network interface card such as a LAN card, a modem, or the like. The communication section 610 performs communication processing via a network such as the internet. The drive 611 may also be connected to the I/O interface 606 as needed. A removable medium 612 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 611 as necessary, so that a computer program read out therefrom is mounted into the storage section 609 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 610, and/or installed from the removable medium 612. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 601 and a Graphics Processor (GPU) 602.
It should be noted that the computer readable medium according to the present application may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor apparatus, device, or means, or a combination of any of the foregoing. More specific examples of the computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. The described modules may also be provided in a processor.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: modeling an unmanned forklift operation network by adopting a complex network method, and constructing an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes; defining occupied paths corresponding to each directed edge and occupied starting time and duration time by adopting a time window; acquiring the running state of the unmanned forklift obtained by real-time monitoring, superposing the running state on an initial network for representation, forming a vehicle-road integrated traffic network, counting the occupied time of the directed edge of the traffic network in the future fixed time from the starting moment according to a time window, and calculating the load of the directed edge of the traffic network at the starting moment; and calculating a traffic network risk index in a global operation range according to the load, and carrying out early warning according to the traffic network risk index.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept described above. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. The dynamic reliability evaluation method of the unmanned forklift running network is characterized by comprising the following steps of:
s1, modeling an unmanned forklift operation network by adopting a complex network method, and constructing an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes;
S2, defining occupied paths corresponding to each directed edge and occupied starting time and duration time by adopting a time window;
S3, acquiring the running state of the unmanned forklift obtained through real-time monitoring, superposing the running state on the initial network for representation, forming a vehicle-road integrated traffic network, counting the occupied duration of the directed edge of the traffic network in the future fixed time from the starting moment according to the time window, and calculating the load of the directed edge of the traffic network at the starting moment;
And S4, calculating a traffic network risk index in a global operation range according to the load, and carrying out early warning according to the traffic network risk index.
2. The method for evaluating the dynamic reliability of the unmanned forklift operating network according to claim 1, wherein the initial network is denoted as g= (V, E), wherein v= { V 1,v2,…,vn }, V denotes a set of operating nodes, V n denotes an nth operating node, each operating node has three-dimensional spatial coordinate attributes, e= { E 1,e2,…,em }, E denotes a set of directed edges, E m denotes an mth directed edge, the type of operating node includes an waypoint, a standby point, a charging point or an interaction point, and the distance between two operating nodes does not exceed a preset distance, and the type of path includes a unidirectional path, a bidirectional path, a path through a door control device or a path through a lifting device.
3. The method for evaluating the dynamic reliability of the unmanned forklift operation network according to claim 1, wherein in the step S2, when the path corresponding to the directed edge is occupied by the unmanned forklift, the length of the time window represents the transit time of the unmanned forklift; when the path corresponding to the directed edge is occupied by the planned event or the abnormal event, the length of the time window represents the duration time of the planned event or the abnormal event.
4. The method for evaluating the dynamic reliability of the unmanned forklift operation network according to claim 1, wherein the operation states include a position, a speed, an electric quantity, a task state, a cargo state, a fault state, and a remaining path, and the step S3 of superimposing the operation states on the initial network to represent the operation states specifically includes:
And associating the operation node or the directed edge on the initial network with the unmanned forklift through an association data table established by taking the unique identification of the unmanned forklift as a main key, and updating the operation state of the unmanned forklift in a corresponding association record in the association data table.
5. The method for evaluating the dynamic reliability of the unmanned forklift operating network according to claim 1, wherein in the step S3, the time period that the directed edge of the traffic network is occupied in a fixed time from the starting time is counted according to the time window, and the load of the directed edge of the traffic network at the starting time is calculated, specifically including:
calculating the load L i (t) of the ith directed edge of the traffic network at the starting moment by adopting the following steps:
Wherein, T i (T) is the occupied time length of the ith directed edge e i in the future fixed time T c of the starting time T, and the occupied time length is obtained by counting through the time window registered on the ith directed edge e i, and L i (T) e [0,1].
6. The method for evaluating the dynamic reliability of the unmanned forklift operation network according to claim 1, wherein the calculating the traffic network risk index in the global operation range according to the load in the step S4 specifically comprises:
calculating a traffic network risk index F (t) in a global operation range by adopting the following steps:
Wherein G ′(t)＝(V′(t),E′ (T)) is a sub-graph formed by the starting point and the target point of all jobs in [ T, t+t c ] time and the running node and the directed edge contained in the shortest path of the starting point and the target point in the traffic network, V ′ (T) is a set of running nodes in the sub-graph, E ′ (T) is a set of directed edges in the sub-graph, E j is the j-th directed edge in the sub-graph, and M is the total number of elements in E ′ (T).
7. The method for evaluating the dynamic reliability of the unmanned forklift operation network according to claim 1, wherein the step S4 of pre-warning according to the traffic network risk index specifically comprises:
And (3) executing the steps S1-S4 every interval time period to evaluate the traffic network risk index, and triggering early warning in response to determining that the traffic network risk index exceeds a preset threshold.
8. A dynamic reliability assessment device for an unmanned forklift operating network, comprising:
The modeling module is configured to model an unmanned forklift operation network by adopting a complex network method, and construct an initial network, wherein the initial network comprises various operation nodes and directed edges formed by various paths among the operation nodes;
a time window definition module configured to define, by using a time window, a time when each path corresponding to the directed edge is occupied and a start time and a duration of the occupation;
The operation state superposition module is configured to acquire the operation state of the unmanned forklift obtained by real-time monitoring, superimpose the operation state on the initial network for representation, form a vehicle-road integrated traffic network, count the occupied time of the directed edge of the traffic network in the future fixed time from the starting moment according to the time window, and calculate the load of the directed edge of the traffic network at the starting moment;
And the early warning module is configured to calculate a traffic network risk index in a global operation range according to the load and perform early warning according to the traffic network risk index.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.
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