A mine vehicle scheduling method, device and equipment and storage medium

Abstract

The application discloses a mine vehicle scheduling method and device, equipment and storage medium. Through the vehicle networking platform, electronic fence data is constructed and delivered to the intelligent terminal, and the intelligent terminal locally stores the fence data, so that data support is provided for the local fence matching and state judgment of the intelligent terminal, and the real-time dependence of the intelligent terminal on the fence data of the vehicle networking platform is eliminated. The intelligent terminal collects the first vehicle state information and performs local edge calculation to obtain an edge calculation result, and meanwhile, related data is uploaded to the vehicle networking platform, so that the scheduling state judgment calculation is sunk to the local, the real-time dependence on the wireless network and the vehicle networking platform is effectively reduced, and the real-time performance of the scheduling state judgment is improved. The vehicle networking platform performs rectification and verification on the edge calculation result, so that the local calculation result of the terminal can be effectively verified and corrected, the terminal misjudgment problem is solved, and the accuracy of the mine vehicle scheduling state judgment is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle dispatching, and more particularly to a method, apparatus, equipment, and storage medium for dispatching mining vehicles. Background Technology

[0002] With the intelligent development of mining, the efficiency of mine vehicle dispatching has a significant impact on production efficiency. Mine sites have multiple operating areas and complex operating environments, making it urgent to achieve precise linkage between vehicle location and operating status to improve the real-time performance and accuracy of dispatching and meet the intelligent dispatching needs of large-scale mine transportation.

[0003] Existing mine transportation scheduling systems mainly adopt three types of technical solutions. Among them, pure cloud-based centralized computing scheduling is the mainstream approach. In this solution, the vehicle-mounted terminal only serves as a data collection and reporting device, and the cloud server uniformly performs fence matching and scheduling status judgment. The scheduling results are then sent down from the cloud. There are also manual assignment of digging and card matching methods, as well as scheduling solutions that only support a single terminal type. Different types of terminals need to deploy independent scheduling systems, and there is a lack of cross-verification mechanism between the terminal and the cloud.

[0004] In existing cloud-based technologies, scheduling status determination relies entirely on the cloud. However, due to the latency, bandwidth fluctuations, and poor signal coverage of wireless networks in mining areas, data upload delays can easily lead to delayed, missed, or incorrect status assessments. Scheduling completely halts when the network is interrupted. Furthermore, the lack of a unified scheduling framework across different terminal types means that even if some terminals possess edge computing capabilities, there is no cloud-based cross-validation and correction mechanism for their calculation results. This leaves calculation errors in scenarios such as location drift and fenced-edge areas unresolved, severely impacting the overall efficiency and accuracy of mine vehicle scheduling. Summary of the Invention

[0005] This invention provides a method, device, equipment, and storage medium for dispatching mining vehicles. By constructing an edge-cloud collaborative mining vehicle dispatching architecture, realizing automatic matching of excavators and trucks, building a unified dispatching framework for heterogeneous terminals, and designing a multi-level electronic fence incremental update mechanism, it solves the technical problems of poor real-time status judgment, weak adaptability of excavator-truck matching, lack of intra-group information perception capability at the vehicle end, and lack of a unified dispatching framework for different terminal types in traditional mining transportation dispatching systems.

[0006] According to one aspect of the present invention, a mining vehicle dispatching method is provided, applied to a mining vehicle dispatching system, comprising: an intelligent terminal and a vehicle networking platform, the method comprising: Through the vehicle-to-everything (V2X) platform, electronic fence data is constructed and distributed to smart terminals; The system collects the status information of the first vehicle through a smart terminal, performs local edge computing based on the status information of the first vehicle and the electronic fence data, obtains the edge computing results, and sends the status information of the first vehicle and the edge computing results to the vehicle network platform respectively. The first cloud computing result is obtained based on the first vehicle status information through the vehicle network platform, and the edge computing result is corrected and verified based on the first cloud computing result.

[0007] Optionally, constructing electronic fence data and distributing it to smart terminals includes: collecting real-time operation data; preprocessing the real-time operation data to obtain effective operation data; obtaining the operation turning radius and safe operation distance in the mining area; combining the excavator's operation turning radius and safe operation distance parameters in the mining area to expand the safe buffer distance outward, and extracting the extreme boundary points of the operation from the effective operation data to form a set of boundary reference points for the electronic fence; using an improved convex hull algorithm to perform topological fitting on the set of boundary reference points to generate a real-time electronic fence outline; calculating the position offset values ​​of the real-time electronic fence outline and the historical electronic fence outline; obtaining a preset offset threshold; when the position offset value exceeds the offset threshold, iteratively calculating and generating updated electronic fence data based on the real-time electronic fence outline, wherein the electronic fence data includes loading area fence, loading point fence, unloading area fence, and unloading point fence; and distributing the electronic fence data to smart terminals via transmission control protocol commands.

[0008] Optionally, the first vehicle status information is collected, including: obtaining scheduling calculation requirements, determining the collection frequency based on the scheduling calculation requirements, and collecting the first vehicle status information based on the collection frequency, wherein the first vehicle status information includes positioning data, vehicle speed data, and load data.

[0009] Optionally, local edge computing is performed based on the first vehicle status information and electronic fence data to obtain edge computing results, including: determining the fence matching result based on the positioning data and electronic fence data; obtaining preset scheduling state transition rules; based on the scheduling state transition rules, using the fence matching result as a basis, and combining vehicle speed data and load data to perform state machine deduction and output the real-time scheduling status of the current vehicle; statistically analyzing the timeliness index data of the current vehicle based on the real-time scheduling status, wherein the timeliness index data includes loading queuing time, loading time, loaded transportation time, unloading queuing time and unloading time, and total single trip time; and integrating the fence matching result, real-time scheduling status, and timeliness index data into the edge computing result.

[0010] Optionally, the fence matching result is determined based on the location data and the electronic fence data, including: performing bounding box preprocessing on the electronic fence data to obtain each fence bounding box; filtering each fence bounding box based on the location data to determine the candidate fence for the current vehicle; using the ray method to determine the actual fence area where the current vehicle is located in the candidate fence, and using the actual fence area as the fence matching result.

[0011] Optionally, the edge computing results are corrected and verified based on the first cloud computing results. This includes: determining whether the first cloud computing results and the edge computing results are consistent; if so, determining that the correction verification result is that no correction is needed and directly using the edge computing results; otherwise, determining that the correction verification result is that correction is needed, performing comprehensive correction on the edge computing results based on the target dimensions, determining the final scheduling state, generating correction instructions based on the final scheduling state, and sending the correction instructions to the smart terminal. The target dimensions include signal quality dimension, fence boundary distance dimension, load change trend dimension, and historical trajectory continuity dimension.

[0012] Optionally, after correcting and verifying the edge computing results based on the first cloud computing results, the mining vehicle scheduling method further includes: receiving a correction instruction through a smart terminal and updating the edge computing results according to the correction instruction.

[0013] Optionally, the mining vehicle dispatching system further includes: a remote information processor (TBOX); and the mining vehicle dispatching method further includes: collecting second vehicle status information through the TBOX and sending the second vehicle status information to the vehicle network platform, wherein the second vehicle status information includes location data, vehicle speed data, and load data; obtaining a second cloud computing result based on the second vehicle status information through the vehicle network platform and sending the second cloud computing result to the TBOX; and directly dispatching vehicles based on the second cloud computing result through the TBOX.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to execute a mining vehicle scheduling method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement a mining vehicle scheduling method according to any embodiment of the present invention.

[0016] The technical solution of this invention constructs and distributes electronic fence data to smart terminals through a vehicle-to-everything (V2X) platform, allowing the smart terminals to store fence data locally. This provides data support for local fence matching and status judgment, eliminating the smart terminals' real-time dependence on the V2X platform's fence data. By collecting first vehicle status information and performing local edge computing on the smart terminal to obtain the edge computing results, and simultaneously uploading relevant data to the V2X platform, the scheduling status judgment calculation is decentralized to the local terminal, effectively reducing the real-time dependence on the wireless network and the V2X platform and improving the real-time performance of scheduling status judgment. The V2X platform performs correction and verification on the edge computing results, effectively verifying and correcting the terminal's local calculation results, solving the problem of terminal misjudgment and improving the accuracy of mining vehicle scheduling status judgment.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a mining vehicle scheduling method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of another mining vehicle scheduling method provided in Embodiment 1 of the present invention; Figure 3 This is a flowchart of another mining vehicle scheduling method provided in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of a mining vehicle dispatching system according to Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Example 1 Figure 1 This is a flowchart illustrating a mining vehicle dispatching method according to Embodiment 1 of the present invention. This embodiment is applicable to mining vehicle dispatching scenarios, and the method can be executed by a mining vehicle dispatching system. The mining vehicle dispatching system includes: a smart terminal, a vehicle networking platform, and a telematics box (TBOX). Figure 1 As shown, the method includes: S110: Through the vehicle-to-everything (V2X) platform, electronic fence data is constructed and sent to smart terminals.

[0023] The vehicle-to-everything (V2X) platform refers to the mining vehicle dispatching platform layer system deployed on a cloud server cluster. The V2X platform includes modules such as communication gateway services, message queue services, real-time computing services, data storage services, and dispatch management services, and possesses functions such as fence construction and distribution, cloud computing, edge-cloud correction, intra-group dispatching, and data storage. Electronic fence data refers to the multi-level geofence coordinate data constructed by the V2X platform based on the mining operation plan, including four categories: loading area fence, loading point fence, unloading area fence, and unloading point fence. The intelligent terminal refers to the all-in-one intelligent terminal mounted on the mining vehicle, integrating Global Positioning System (GPS) positioning, load sensors, edge computing processors, and a touch screen display. The intelligent terminal has functions such as data acquisition, local edge computing, screen interaction, and intra-group information reception and display, and can establish a two-way communication link with the V2X platform through the Transmission Control Protocol (TCP).

[0024] In one optional implementation, the vehicle-to-everything (V2X) platform first acquires the actual operational plan of the mine. The mine's operational plan clearly defines the loading areas, specific excavator loading points, unloading areas, and the distribution of unloading points such as spoil heaps or material piles. Based on the mine's operational plan, the V2X platform constructs a four-tiered electronic fence system, including loading area fences, loading point fences, unloading area fences, and unloading point fences. The loading area fence is a regional fence containing one or more loading point fences, the unloading area fence is a regional fence containing one or more unloading point fences, the loading point fence corresponds to the precise fence of the excavator's current operating location, and the unloading point fence corresponds to the precise fence of the spoil heap or material pile. It is understood that all fence data uses geographic coordinates as the core data format, forming a hierarchically nested electronic fence model. After completing the electronic fence data construction, the V2X platform uses transmission control protocol commands as the transmission carrier to batch-distribute the electronic fence data, containing the geographic coordinates of various fences, to the intelligent terminals mounted on the mining vehicles. The electronic fence data is directly stored locally on the intelligent terminals for subsequent use when the terminals perform local edge computing. If the fence changes due to excavator relocation or other reasons at the mining site, the platform will not retransmit all fence data. Instead, it will only send the fence change data incrementally to the affected smart terminals. After receiving the data, the smart terminals will update the corresponding fence data locally. This ensures the timeliness of the fence data and reduces communication overhead and data update delays.

[0025] Optionally, constructing electronic fence data and distributing it to smart terminals includes: collecting real-time operation data; preprocessing the real-time operation data to obtain effective operation data; obtaining the operation turning radius and safe operation distance in the mining area; combining the excavator's operation turning radius and safe operation distance parameters in the mining area to expand the safe buffer distance outward, and extracting the extreme boundary points of the operation from the effective operation data to form a set of boundary reference points for the electronic fence; using an improved convex hull algorithm to perform topological fitting on the set of boundary reference points to generate a real-time electronic fence outline; calculating the position offset values ​​of the real-time electronic fence outline and the historical electronic fence outline; obtaining a preset offset threshold; when the position offset value exceeds the offset threshold, iteratively calculating and generating updated electronic fence data based on the real-time electronic fence outline, wherein the electronic fence data includes loading area fence, loading point fence, unloading area fence, and unloading point fence; and distributing the electronic fence data to smart terminals via transmission control protocol commands.

[0026] Real-time operation data refers to the dynamic information such as latitude, longitude, and planar coordinates of the excavator collected at high frequency by positioning equipment, which is the basic data source for constructing the electronic fence. The operating turning radius refers to the distance from the center of rotation to the farthest point of the bucket during excavator operation. The safe operating distance in the mining area refers to the safe distance that must be maintained between different equipment and operating areas within the mining area; it is a rigid indicator to ensure operational safety and is used to further expand the safety buffer space beyond the excavator's operating range. The extreme boundary points of the operation refer to the outermost coordinate points of all excavator operating positions; they are the reference points for constructing the outline of the electronic fence, and the extraction accuracy determines whether the fence can completely cover all operating areas.

[0027] Specifically, the vehicle-to-everything (V2X) platform can frequently collect real-time operational data such as latitude, longitude, and planar coordinates of all excavators within the mining work team. This data is then pre-processed with filtering and noise reduction to eliminate abnormal noise caused by signal drift and obstruction interference, resulting in valid operational data. Next, preset excavator turning radius and safe working distance parameters for the mining area are retrieved. Based on these parameters, a safe buffer distance is expanded outwards. All extreme boundary points are extracted from the valid operational data, forming a set of electronic fence boundary reference points covering the loading area, loading point, unloading area, and unloading point. Then, an improved convex hull algorithm is used to perform topological fitting on the boundary reference point set, automatically connecting the outer extreme points to generate a closed, smooth, and depression-free real-time electronic fence outline. Simultaneously, the fitting weights can adaptively adjust for different operational scenarios above and below the pit, as well as operational patterns such as dense or dispersed excavator deployment. Then, the vehicle network platform will compare the real-time coordinates of the excavator with the coordinates generated by the historical fence in real time, calculate the position offset value between the real-time electronic fence outline and the historical electronic fence outline, retrieve the preset position offset trigger threshold, and when the position offset value of a single or multiple excavators exceeds the offset threshold, or when the overall operation mode of the team changes, the algorithm process will be restarted and iterative calculations will be performed based on the real-time electronic fence outline to generate new and updated electronic fence data that can completely cover all excavators. The electronic fence data constructed specifically includes loading area fence, loading point fence, unloading area fence, and unloading point fence. Finally, the complete electronic fence data will be sent to the smart terminal.

[0028] S120. Collect the first vehicle status information through the smart terminal, and perform local edge computing based on the first vehicle status information and electronic fence data to obtain the edge computing result. Then send the first vehicle status information and the edge computing result to the vehicle network platform respectively.

[0029] The first vehicle status information refers to the vehicle operation data collected in real time from the mining vehicle by the intelligent terminal. For example, the first vehicle status information may include GPS positioning data at a frequency of 1Hz, vehicle speed data, and load data at a frequency of 10Hz. Local edge computing refers to the computing method of offloading traditional cloud-based computing tasks such as fence matching and scheduling status judgment to the on-board intelligent terminal for local completion. Edge computing results refer to the full computing output obtained by the intelligent terminal after performing local edge computing. Edge computing results include vehicle fence matching results; real-time scheduling status, such as loading queue, loading in progress, and loaded driving; and timeliness indicators for each operation stage, such as loading queue time and loading time. The edge computing results are transmitted to the vehicle network platform in real time as a reference for vehicle scheduling status.

[0030] Figure 2A flowchart of another mining vehicle scheduling method is provided for Embodiment 1 of the present invention, which is executed by a smart terminal. Step S120 mainly includes the following steps S121 to S128: S121. Obtain scheduling calculation requirements and determine the collection frequency based on the scheduling calculation requirements.

[0031] Among them, the collection frequency refers to the fixed time frequency at which the intelligent terminal collects various status information of the vehicle, which is determined by the scheduling calculation requirements.

[0032] Specifically, the vehicle-to-everything (V2X) platform determines the collection frequency of various vehicle status data based on the actual scheduling and computational needs of mining vehicle dispatching. It's important to note that the scheduling and computational needs revolve around aspects of local edge computing on the terminal, such as fence matching, state machine deduction, and timeliness indicator statistics. Each aspect has different requirements for the real-time performance and accuracy of different data, thus necessitating differentiated collection frequencies. For example, to meet the basic requirements of fence matching and location determination, GPS positioning data is collected at a frequency of 1 Hz, vehicle speed data is collected synchronously with the positioning data, while load data, which is the core basis for determining whether a vehicle has completed loading and unloading, has higher requirements for accuracy and real-time performance, and therefore is collected at a frequency of 10 Hz.

[0033] S122. Collect first vehicle status information based on the collection frequency, wherein the first vehicle status information includes positioning data, vehicle speed data and load data.

[0034] Among them, location data refers to the real-time geographical coordinates of the vehicle collected by the smart terminal through its integrated GPS positioning module; vehicle speed data refers to the real-time driving speed data of the vehicle collected by the smart terminal, which is collected synchronously with the GPS positioning data and can be used as reference data for state machine deduction and assisting in judging the vehicle dispatch status; and load data refers to the real-time cargo weight data of the vehicle collected by the smart terminal through the load sensor.

[0035] Specifically, after determining the collection frequency, the smart terminal will continuously and in real time collect the vehicle's location data, speed data, and load data according to the set frequency through its integrated GPS positioning module, vehicle speed detection module, and load sensor.

[0036] S123. Determine the fence matching result based on the location data and electronic fence data.

[0037] Among them, the fence matching result refers to the result obtained by the smart terminal after calculating and matching the collected vehicle positioning data with the locally stored electronic fence data.

[0038] Specifically, the smart terminal can use the collected location data and locally stored electronic fence data to perform fence matching calculations. During execution, it first uses a bounding box method to quickly filter out the fence range to which the location point may belong, and then uses the ray casting method to make a precise judgment to determine whether the location point is inside the fence polygon. At the same time, it follows the strategy of prioritizing accurate fences, that is, the result of the loading point fence judgment takes precedence over the loading area fence and the result of the unloading point fence judgment takes precedence over the unloading area fence, and finally obtains an accurate fence matching result.

[0039] Optionally, the fence matching result is determined based on the location data and the electronic fence data, including: performing bounding box preprocessing on the electronic fence data to obtain each fence bounding box; filtering each fence bounding box based on the location data to determine the candidate fence for the current vehicle; using the ray method to determine the actual fence area where the current vehicle is located in the candidate fence, and using the actual fence area as the fence matching result.

[0040] Specifically, the smart terminal first performs bounding box preprocessing on the locally stored geofence data. For example, it generates corresponding bounding boxes for each type of geofence: loading area geofence, loading point geofence, unloading area geofence, and unloading point geofence. A bounding box is the smallest rectangle that can completely enclose the polygonal area of ​​its corresponding geofence. By constructing bounding boxes, the geographical range of each geofence can be quickly defined, preparing for subsequent rapid filtering. Then, the smart terminal retrieves real-time vehicle location data and compares the geographic coordinates of this location data with the geographic range of each geofence's bounding box. It determines whether the location point is within the bounding box of a particular geofence and directly eliminates geofences whose location points are not within their bounding boxes, retaining only those geofences whose location points are within their bounding boxes as candidate geofences for the current vehicle. Bounding box filtering significantly narrows down the range of geofences for subsequent precise matching, reducing the computational load on the smart terminal and improving the efficiency of geofence matching.

[0041] Furthermore, the intelligent terminal uses a ray-mapping method to perform precise fence matching calculations on the selected candidate fences. Specifically, a ray is emitted from the vehicle's location point in any direction, and the number of intersections between this ray and the boundary of the candidate fence polygon is counted. If the number of intersections is odd, the location point is determined to be inside the fence polygon; if the number is even, it is determined to be outside, thus determining the actual fenced area where the current vehicle is located. Finally, the intelligent terminal follows a strategy of prioritizing precise fences, that is, the loading point fence result takes precedence over the loading area fence, and the unloading point fence result takes precedence over the unloading area fence. The finally determined actual fenced area is used as the fence matching result, providing core location information for subsequent state machine deduction.

[0042] S124. Obtain the preset scheduling state transition rules.

[0043] Among them, the scheduling state transition rule refers to the complete flow logic of the scheduling state of mining vehicles set in advance in the smart terminal. It clarifies the trigger conditions and flow order between the various states of the vehicle starting from empty driving, through loading queue, loading, loaded driving, etc., and finally returning to empty driving. It is the basis for state machine deduction.

[0044] For example, the scheduling state transition rule can be: driving without load → entering the loading area → loading queue → entering the loading point → loading in progress → load suddenly increases and leaves the loading point → driving with load → entering the unloading area → unloading queue → entering the unloading point → unloading in progress → load drops to the empty value or lifting reset → driving without load.

[0045] S125. Based on the scheduling state transition rules, using the fence matching results as a basis, and combining vehicle speed data and load data, perform state machine deduction to output the real-time scheduling status of the current vehicle.

[0046] State machine deduction refers to the process by which an intelligent terminal, based on fence matching results and combined with vehicle speed and load data, dynamically judges and deduces the current dispatch status of a vehicle according to preset dispatch state transition rules. This process can accurately identify the real-time status of a vehicle at different operational stages. Real-time dispatch status refers to the specific operational state of the vehicle obtained through state machine deduction. Examples include empty driving, loading queue, loading in progress, loaded driving, unloading queue, and unloading in progress, reflecting core state information about the vehicle's operational progress.

[0047] Specifically, the intelligent terminal uses the obtained fence matching results as a basis, combined with the collected vehicle speed and load data, to execute state machine deduction according to the preset scheduling state transition rules. For example, based on the vehicle's fence location, speed changes, and load increases or decreases, it determines and outputs the vehicle's current real-time scheduling status, such as loading queue, loading in progress, and driving with a load.

[0048] S126. Based on the real-time scheduling status, statistically analyze the timeliness index data of the current vehicles.

[0049] Among them, the timeliness index data refers to the time index of each operation link of the vehicle and the overall transportation calculated by the intelligent terminal based on the timestamp of each vehicle dispatch status transfer and the real-time dispatch status statistics.

[0050] Specifically, during the state machine simulation, the intelligent terminal records the timestamp of each vehicle scheduling state transition in real time, and combines it with the current real-time scheduling status to continuously collect and calculate the timeliness index data of each operation link of the vehicle.

[0051] The timeliness indicators include loading queue time, loading time, loaded transport time, unloading queue time, unloading time, and total trip time. Loading queue time refers to the time interval from when a mining vehicle's scheduling status is determined to be "loading queue" until it changes to "loading in progress." The intelligent terminal records the timestamps of these two status transitions and calculates this time in real time, reflecting the time spent by the vehicle waiting to be loaded in the loading area. Loading time refers to the time interval from when a mining vehicle's scheduling status is determined to be "loading in progress" until a sudden increase in load is detected and the vehicle leaves the loading point, changing its status to "loaded and moving." This represents the actual time spent by the vehicle completing the loading operation at the loading point. Loaded transport time refers to the time interval from when a mining vehicle's scheduling status is determined to be "loaded and moving" until it enters the unloading area and its status changes to "unloading queue," reflecting the time spent by the vehicle transporting a heavy load from the loading point to the unloading area. Unloading queuing time refers to the time interval from when a mining vehicle's scheduling status is determined to be "unloading queuing" until the status changes to "unloading in progress," reflecting the time spent by the vehicle waiting to unload in the unloading area. Unloading time refers to the time interval from when a mining vehicle's scheduling status is determined to be "unloading in progress" until the load is detected to have dropped to the empty load value or the lifting is reset, and the status changes to "empty driving," representing the actual time taken for the vehicle to complete unloading at the unloading point. Total trip time refers to the complete time interval from when a mining vehicle's scheduling status starts from "empty driving" in a transportation cycle, through all stages including loading queuing, loading in progress, loaded driving, unloading queuing, and unloading in progress, until it returns to "empty driving" after unloading is completed, reflecting the total time taken for the vehicle to complete a transportation operation from empty departure to unloading completion.

[0052] S127. Integrate fence matching results, real-time scheduling status, and timeliness index data into edge computing results.

[0053] Specifically, the smart terminal will integrate the obtained fence matching results, the real-time scheduling status derived from the deduction, and all the timeliness index data calculated statistically to form a complete edge computing result. The edge computing result will be transmitted to the vehicle network platform in real time and will also be displayed on the touch screen of the smart terminal.

[0054] S128. Send the first vehicle status information and edge computing results to the vehicle network platform respectively.

[0055] S130. Through the vehicle network platform, the first cloud computing result is obtained based on the first vehicle status information, and the edge computing result is corrected and verified based on the first cloud computing result.

[0056] The first cloud computing result refers to the result obtained by the vehicle-to-everything (V2X) platform after receiving the first vehicle status information reported by the intelligent terminal, and simultaneously performing calculations with the same logic as the terminal's local calculations. This first cloud computing result can include fence matching results and scheduling status judgment results completed in the cloud, used for comparison with the terminal's edge computing results as benchmark data for edge-cloud collaborative correction. Correction verification refers to the edge-cloud collaborative verification and correction mechanism executed by the V2X platform for the edge computing results of the intelligent terminal. For example, the V2X platform compares the cloud computing results with the edge computing results; if they match, the edge computing results are directly accepted; if they do not match, multi-dimensional arbitration is performed from four dimensions: GPS signal quality, fence boundary distance, load change trend, and historical trajectory continuity, ultimately yielding a corrected scheduling status.

[0057] Optionally, the mining vehicle dispatching system further includes: a remote information processor (TBOX); and the mining vehicle dispatching method further includes: collecting second vehicle status information through the TBOX and sending the second vehicle status information to the vehicle network platform, wherein the second vehicle status information includes location data, vehicle speed data, and load data; obtaining a second cloud computing result based on the second vehicle status information through the vehicle network platform and sending the second cloud computing result to the TBOX; and directly dispatching vehicles based on the second cloud computing result through the TBOX.

[0058] Specifically, for mining vehicles that are not equipped with an integrated smart terminal but only have a remote information processor (TBOX) installed, the TBOX will be responsible for collecting and transmitting vehicle status information. The TBOX will collect the vehicle's positioning data, speed data, and load data in real time through its own data acquisition module, and send the second vehicle status information to the vehicle network platform in real time based on a unified TCP protocol. It does not participate in any local calculations and only acts as a pure data acquisition and transparent transmission device.

[0059] Furthermore, after receiving the second vehicle status information reported by the TBOX, the vehicle-to-everything (V2X) platform uses this data as the sole data source to independently execute complete fence matching calculations and scheduling status judgments. For example, it completes fence matching using a two-stage algorithm of fast bounding box filtering plus raycasting for precise matching. Then, it combines preset scheduling state transition rules with reference to vehicle speed and load data to complete state machine deduction, while simultaneously statistically analyzing the timeliness indicators of each stage, ultimately integrating them to form the second cloud-based computing result. The second cloud-based computing result includes the vehicle's actual fenced area, real-time scheduling status, and timeliness indicator data. The V2X platform then sends the generated second cloud-based computing result to the corresponding TBOX via TCP commands. It is known that the TBOX lacks edge computing capabilities and an interactive display interface; therefore, the TBOX directly uses the received second cloud-based computing result to complete vehicle scheduling. Relevant scheduling status information is synchronized to the platform's scheduling management system. Dispatchers can monitor the vehicle's operational status through the platform, while drivers can obtain scheduling instructions based on the second cloud-based computing result via walkie-talkies, dispatcher voice notifications, etc., and complete loading, transportation, and unloading operations according to the instructions. Furthermore, the scheduling status of TBOX vehicles will be written together with the scheduling status of all-in-one vehicle vehicles into the platform's unified real-time cache and persistent database, realizing unified management of the scheduling status of heterogeneous terminals.

[0060] The technical solution of this invention constructs and distributes electronic fence data to smart terminals through a vehicle-to-everything (V2X) platform, allowing the smart terminals to store fence data locally. This provides data support for local fence matching and status judgment, eliminating the smart terminals' real-time dependence on the V2X platform's fence data. By collecting first vehicle status information and performing local edge computing on the smart terminal to obtain the edge computing results, and simultaneously uploading relevant data to the V2X platform, the scheduling status judgment calculation is decentralized to the local terminal, effectively reducing the real-time dependence on the wireless network and the V2X platform and improving the real-time performance of scheduling status judgment. The V2X platform performs correction and verification on the edge computing results, effectively verifying and correcting the terminal's local calculation results, solving the problem of terminal misjudgment and improving the accuracy of mining vehicle scheduling status judgment.

[0061] Example 2 Figure 3 This is a flowchart of a mining vehicle scheduling method provided in Embodiment 2 of the present invention. This embodiment adds a specific process for correcting and verifying the edge computing results based on the first cloud computing result, building upon Embodiment 1. The specific content of steps S210-S220 is largely the same as steps S110-S120 in Embodiment 1, and therefore will not be repeated in this embodiment. Figure 3 As shown, the method includes: S210: Through the vehicle-to-everything (V2X) platform, electronic fence data is constructed and sent to smart terminals.

[0062] Optionally, constructing electronic fence data and distributing it to smart terminals includes: collecting real-time operation data; preprocessing the real-time operation data to obtain effective operation data; obtaining the operation turning radius and safe operation distance in the mining area; combining the excavator's operation turning radius and safe operation distance parameters in the mining area to expand the safe buffer distance outward, and extracting the extreme boundary points of the operation from the effective operation data to form a set of boundary reference points for the electronic fence; using an improved convex hull algorithm to perform topological fitting on the set of boundary reference points to generate a real-time electronic fence outline; calculating the position offset values ​​of the real-time electronic fence outline and the historical electronic fence outline; obtaining a preset offset threshold; when the position offset value exceeds the offset threshold, iteratively calculating and generating updated electronic fence data based on the real-time electronic fence outline, wherein the electronic fence data includes loading area fence, loading point fence, unloading area fence, and unloading point fence; and distributing the electronic fence data to smart terminals via transmission control protocol commands.

[0063] S220: Collect the first vehicle status information through the smart terminal, and perform local edge computing based on the first vehicle status information and electronic fence data to obtain the edge computing result. Then, send the first vehicle status information and the edge computing result to the vehicle network platform respectively.

[0064] Optionally, the first vehicle status information is collected, including: obtaining scheduling calculation requirements, determining the collection frequency based on the scheduling calculation requirements, and collecting the first vehicle status information based on the collection frequency, wherein the first vehicle status information includes positioning data, vehicle speed data, and load data.

[0065] Optionally, local edge computing is performed based on the first vehicle status information and electronic fence data to obtain edge computing results, including: determining the fence matching result based on the positioning data and electronic fence data; obtaining preset scheduling state transition rules; based on the scheduling state transition rules, using the fence matching result as a basis, and combining vehicle speed data and load data to perform state machine deduction and output the real-time scheduling status of the current vehicle; statistically analyzing the timeliness index data of the current vehicle based on the real-time scheduling status, wherein the timeliness index data includes loading queuing time, loading time, loaded transportation time, unloading queuing time and unloading time, and total single trip time; and integrating the fence matching result, real-time scheduling status, and timeliness index data into the edge computing result.

[0066] Optionally, the fence matching result is determined based on the location data and the electronic fence data, including: performing bounding box preprocessing on the electronic fence data to obtain each fence bounding box; filtering each fence bounding box based on the location data to determine the candidate fence for the current vehicle; using the ray method to determine the actual fence area where the current vehicle is located in the candidate fence, and using the actual fence area as the fence matching result.

[0067] S230: The first cloud computing result is obtained through the vehicle network platform based on the first vehicle status information.

[0068] Specifically, after receiving the edge computing results reported by the smart terminal, the vehicle-to-everything (V2X) platform will first compare the first cloud computing results, which it synchronously calculates based on the first vehicle status information, with the edge computing results.

[0069] S240. Determine whether the first cloud computing result is consistent with the edge computing result. If yes, execute S250; otherwise, execute S260.

[0070] S250. Determine that the correction verification result is that no correction is needed, and directly use the edge calculation result.

[0071] The correction verification result refers to the conclusion reached by the vehicle-to-everything (V2X) platform after comparing the first cloud computing result with the edge computing result, regarding the accuracy of the edge computing result and whether correction is needed. It is divided into two categories: no correction required and correction required. No correction required means that the core content of the first cloud computing result and the edge computing result are completely consistent; the platform determines that the edge computing result is accurate and no correction is needed. Correction required means that the core content of the first cloud computing result and the edge computing result differs; the platform determines that the edge computing result has a deviation, and correction is needed through multi-dimensional comprehensive analysis.

[0072] Specifically, if the core content of the two is found to be completely consistent after comparison, the vehicle network platform determines that the correction verification result does not need to be corrected, directly adopts the edge computing result of the intelligent terminal, takes the result as the final scheduling state of the vehicle, and writes the state into the real-time cache and persistent database.

[0073] S260. If the correction verification result is determined to be correctable, perform comprehensive correction on the edge computing result based on the target dimension, determine the final scheduling state, generate a correction instruction based on the final scheduling state, and send the correction instruction to the smart terminal.

[0074] Specifically, if the comparison reveals an inconsistency between the first cloud computing result and the edge computing result, the platform determines that the correction verification result needs to be corrected, and then initiates a multi-dimensional comprehensive correction process to comprehensively judge the vehicle status from four target dimensions: signal quality, fence boundary distance, load change trend, and historical trajectory continuity.

[0075] The target dimensions include signal quality, fence boundary distance, load change trend, and historical trajectory continuity. The signal quality dimension refers to the dimension based on relevant indicators of the vehicle's GPS signal for correction judgment. For example, it checks indicators such as the Horizontal Dilution of Precision (HDOP) and the number of satellites. If the GPS signal quality is low, it indicates errors in the terminal positioning data, and the cloud-based calculation results will be prioritized. The fence boundary distance dimension refers to the dimension based on the actual distance between the vehicle's current location and the electronic fence boundary for correction judgment. For example, if the vehicle is less than a set threshold from the fence boundary, a lag judgment strategy will be used to maintain the vehicle's previous scheduling state and avoid frequent state jumps. The load change trend dimension refers to the dimension based on the real-time load data change patterns collected by the vehicle's load sensor for correction judgment. For example, if the load data clearly supports a certain scheduling state judgment, the state corresponding to the load data will be used to correct edge calculation deviations. The historical trajectory continuity dimension refers to the dimension based on the vehicle's historical driving trajectory for correction judgment. For example, by tracing historical trajectories, it can be determined whether the difference between the results in the cloud and the terminal is caused by GPS drift. If the state change is caused by drift, the edge computing results will be corrected based on the continuous historical trajectories.

[0076] Optionally, after correcting and verifying the edge computing results based on the first cloud computing results, the method further includes: receiving a correction instruction via a smart terminal and updating the edge computing results according to the correction instruction.

[0077] Specifically, after the vehicle-to-everything (V2X) platform completes the correction and verification of the edge computing results and generates a correction command, the intelligent terminal receives the correction command from the platform in real time via a persistent bidirectional TCP communication link established with the platform. Upon receiving the correction command, the intelligent terminal parses the final scheduling status determined by the platform's comprehensive assessment, along with the corresponding fence matching results and timeliness indicator data correction information. Based on this, it then comprehensively updates the original edge computing results stored and displayed locally. The update may include replacing erroneous fence matching results, correcting deviations in the real-time scheduling status, and recalculating timeliness indicator data based on the final scheduling status, ensuring complete consistency between the local edge computing results and the final scheduling status determined by the platform. After the update, the intelligent terminal transmits the updated edge computing results back to the V2X platform in real time, synchronizing the terminal's and platform's status data. Simultaneously, it displays the updated vehicle scheduling status, matched excavator information, and timeliness indicators on its touchscreen display, providing the driver with accurate operational references and ensuring that subsequent local edge computing is based on accurate status data, guaranteeing the continuity and accuracy of mine vehicle scheduling status judgment.

[0078] Optionally, the mining vehicle dispatching system further includes: a remote information processor (TBOX); and the mining vehicle dispatching method further includes: collecting second vehicle status information through the TBOX and sending the second vehicle status information to the vehicle network platform, wherein the second vehicle status information includes location data, vehicle speed data, and load data; obtaining a second cloud computing result based on the second vehicle status information through the vehicle network platform and sending the second cloud computing result to the TBOX; and directly dispatching vehicles based on the second cloud computing result through the TBOX.

[0079] The technical solution of this invention determines whether the first cloud-based calculation result and the edge calculation result are consistent through a vehicle-to-everything (V2X) platform, and accordingly determines whether correction is needed or not. If they are consistent, the edge calculation result is directly adopted, which can reduce unnecessary cloud-based computational overhead and improve verification efficiency. When correction is needed, the edge calculation result is comprehensively corrected based on multiple target dimensions, which can accurately determine the actual vehicle scheduling status from multiple dimensions and effectively solve the problem of calculation misjudgment in scenarios such as positioning drift and blurred fence boundaries. Based on the final scheduling status after comprehensive correction, a correction instruction is generated and sent to the intelligent terminal, which can synchronize the accurate scheduling status to the terminal, providing accurate basis for the terminal's subsequent scheduling judgment and ensuring the accuracy of the terminal's scheduling status.

[0080] Example 3 Figure 4 This is a schematic diagram of a mining vehicle dispatching system provided in Embodiment 3 of the present invention. Figure 4 As shown, the system includes: a smart terminal 310 and a vehicle-to-everything (V2X) platform 320. The V2X platform 320 is used to construct electronic fence data and send it to the smart terminal; the smart terminal 310 is used to collect first vehicle status information, and perform local edge computing based on the first vehicle status information and the electronic fence data to obtain edge computing results, and then send the first vehicle status information and the edge computing results to the V2X platform respectively; the V2X platform 320 is used to obtain first cloud computing results based on the first vehicle status information, and perform correction and verification on the edge computing results based on the first cloud computing results.

[0081] Optionally, the vehicle-to-everything (V2X) platform 320 specifically includes: an electronic fence construction unit, used for: collecting real-time operation data; preprocessing the real-time operation data to obtain effective operation data; obtaining the operation turning radius and the safe operation distance in the mining area; combining the excavator's operation turning radius and the safe operation distance parameters in the mining area to expand the safety buffer distance outward, and extracting the extreme boundary points of the operation from the effective operation data to form a set of boundary reference points for the electronic fence; using an improved convex hull algorithm to perform topological fitting on the set of boundary reference points to generate a real-time electronic fence outline; calculating the position offset values ​​of the real-time electronic fence outline and the historical electronic fence outline; obtaining a preset offset threshold; when the position offset value exceeds the offset threshold, iteratively calculating and generating updated electronic fence data based on the real-time electronic fence outline, wherein the electronic fence data includes loading area fence, loading point fence, unloading area fence, and unloading point fence; and sending the electronic fence data to the intelligent terminal through transmission control protocol commands.

[0082] Optionally, the intelligent terminal 310 specifically includes: a status information acquisition unit, used for: acquiring scheduling calculation requirements, determining the acquisition frequency based on the scheduling calculation requirements; and acquiring first vehicle status information based on the acquisition frequency, wherein the first vehicle status information includes positioning data, vehicle speed data, and load data.

[0083] Optionally, the intelligent terminal 310 specifically includes: an edge computing result calculation unit, used for: determining the fence matching result based on positioning data and electronic fence data; obtaining preset scheduling state transition rules; based on the scheduling state transition rules, using the fence matching result as a basis, and combining vehicle speed data and load data to perform state machine deduction, and outputting the real-time scheduling status of the current vehicle; statistically analyzing the timeliness index data of the current vehicle based on the real-time scheduling status, wherein the timeliness index data includes loading queuing time, loading time, loaded transportation time, unloading queuing time and unloading time, and total single trip time; and integrating the fence matching result, real-time scheduling status, and timeliness index data into an edge computing result.

[0084] Optionally, the edge computing result calculation unit specifically includes: a fence matching result determination subunit, used for: performing bounding box preprocessing on the electronic fence data to obtain each fence bounding box; filtering each fence bounding box based on the positioning data to determine the candidate fence for the current vehicle; using the ray method to determine the actual fence area where the current vehicle is located in the candidate fence, and using the actual fence area as the fence matching result.

[0085] Optionally, the vehicle networking platform 320 specifically includes: a result correction unit, used to: determine whether the first cloud computing result and the edge computing result are consistent; if so, determine that the correction verification result is that no correction is needed and directly adopt the edge computing result; otherwise, determine that the correction verification result is that correction is needed, perform comprehensive correction on the edge computing result based on the target dimension, determine the final scheduling state, generate a correction instruction based on the final scheduling state, and send the correction instruction to the intelligent terminal. The target dimension includes signal quality dimension, fence boundary distance dimension, load change trend dimension, and historical trajectory continuity dimension.

[0086] Optionally, the smart terminal 310 further includes: a correction and update unit, used to receive a correction instruction through the smart terminal and update the edge computing result according to the correction instruction after performing correction and verification on the edge computing result based on the first cloud computing result.

[0087] Optionally, the mining vehicle dispatching system also includes a 330TBOX remote information processor. The TBOX is used to collect second vehicle status information and send it to the vehicle networking platform. The second vehicle status information includes location data, speed data, and load data. The vehicle networking platform is used to obtain second cloud computing results based on the second vehicle status information and send these results to the TBOX. The TBOX is also used to directly dispatch vehicles based on the second cloud computing results.

[0088] The technical solution of this invention constructs and distributes electronic fence data to smart terminals through a vehicle-to-everything (V2X) platform, allowing the smart terminals to store fence data locally. This provides data support for local fence matching and status judgment, eliminating the smart terminals' real-time dependence on the V2X platform's fence data. By collecting first vehicle status information and performing local edge computing on the smart terminal to obtain the edge computing results, and simultaneously uploading relevant data to the V2X platform, the scheduling status judgment calculation is decentralized to the local terminal, effectively reducing the real-time dependence on the wireless network and the V2X platform and improving the real-time performance of scheduling status judgment. The V2X platform performs correction and verification on the edge computing results, effectively verifying and correcting the terminal's local calculation results, solving the problem of terminal misjudgment and improving the accuracy of mining vehicle scheduling status judgment.

[0089] The mining vehicle scheduling system provided in this embodiment of the invention can execute the mining vehicle scheduling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0090] Example 4 Figure 5A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0091] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) or random access memory (RAM), communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. Input / output (I / O) interfaces are also connected to the bus 14.

[0092] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0093] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a mine vehicle scheduling method.

[0094] In some embodiments, a mine vehicle scheduling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the mine vehicle scheduling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a mine vehicle scheduling method by any other suitable means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0100] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0101] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for scheduling mining vehicles, characterized in that, An application is made to a mining vehicle dispatching system, the mining vehicle dispatching system comprising: an intelligent terminal and a vehicle networking platform, the mining vehicle dispatching method comprising: Through the vehicle-to-everything (V2X) platform, electronic fence data is constructed and distributed to smart terminals; The smart terminal collects first vehicle status information, performs local edge computing based on the first vehicle status information and the electronic fence data, obtains edge computing results, and sends the first vehicle status information and the edge computing results to the vehicle network platform respectively. The vehicle network platform obtains a first cloud computing result based on the first vehicle status information, and performs correction and verification on the edge computing result based on the first cloud computing result. The edge calculation results are updated according to the correction instructions received through a smart terminal. The step of correcting and verifying the edge computing result based on the first cloud computing result includes: Determine whether the first cloud computing result is consistent with the edge computing result. If so, determine that the correction verification result is not required and directly use the edge computing result. Otherwise, if the correction verification result indicates that correction is needed, the edge computing result is comprehensively corrected based on the target dimension to determine the final scheduling state. A correction instruction is generated based on the final scheduling state and sent to the smart terminal. The target dimension includes signal quality dimension, fence boundary distance dimension, load change trend dimension, and historical trajectory continuity dimension. The signal quality dimension refers to the dimension for correction determination based on relevant indicators of the vehicle's GPS signal. The fence boundary distance dimension refers to the dimension for correction determination based on the actual distance between the vehicle's current location point and the electronic fence boundary. The load change trend dimension refers to the dimension for correction determination based on the real-time load data change pattern collected by the vehicle's load sensor. The historical trajectory continuity dimension refers to the dimension for correction determination based on the vehicle's historical driving trajectory. The step of constructing electronic fence data and sending it to smart terminals includes: Collect real-time operation data and preprocess the real-time operation data to obtain effective operation data; Obtain the turning radius of the operation and the safe working distance in the mining area; Combining the excavator's turning radius and the safe working distance in the mining area, the safe buffer distance is expanded outward, and the extreme boundary points of the operation are extracted from the effective operation data to form a set of boundary reference points for the electronic fence; The convex hull algorithm is used to perform topological fitting on the set of boundary reference points to generate a real-time electronic fence outline. Calculate the positional offset values ​​of the real-time electronic fence outline and the historical electronic fence outline; A preset offset threshold is obtained. When the position offset value exceeds the offset threshold, updated electronic fence data is generated based on the real-time electronic fence outline iterative calculation. The electronic fence data includes loading area fence, loading point fence, unloading area fence, and unloading point fence. The electronic fence data is sent to the smart terminal via transmission control protocol commands.

2. The mining vehicle dispatching method according to claim 1, characterized in that, The collection of the first vehicle status information includes: Obtain scheduling computation requirements, and determine the sampling frequency based on the scheduling computation requirements; The first vehicle status information is collected based on the collection frequency, wherein the first vehicle status information includes positioning data, vehicle speed data, and load data.

3. The mining vehicle scheduling method according to claim 2, characterized in that, The step of performing local edge computing based on the first vehicle status information and the electronic fence data to obtain edge computing results includes: The fence matching result is determined based on the location data and the electronic fence data; Obtain the preset scheduling state transition rules; Based on the scheduling state transition rules, using the fence matching results as a basis, and combining the vehicle speed data and load data, a state machine deduction is performed to output the real-time scheduling status of the current vehicle. Based on the real-time scheduling status, the current vehicle's timeliness index data is statistically analyzed, wherein the timeliness index data includes loading queue time, loading time, loaded transportation time, unloading queue time and unloading time, and total trip time. The fence matching results, the real-time scheduling status, and the timeliness index data are integrated into the edge computing results.

4. The mining vehicle dispatching method according to claim 3, characterized in that, The process of determining the fence matching result based on the location data and the electronic fence data includes: The electronic fence data is preprocessed with bounding boxes to obtain each fence bounding box; Based on the location data, each of the fence bounding boxes is filtered to determine the candidate fence for the current vehicle; The ray casting method is used to determine the actual fence area where the current vehicle is located in the candidate fence, and the actual fence area is used as the fence matching result.

5. The mining vehicle dispatching method according to any one of claims 1-4, characterized in that, The mining vehicle dispatching system further includes: a remote information processor (TBOX); the mining vehicle dispatching method further includes: The second vehicle status information is collected through TBOX and sent to the vehicle network platform. The second vehicle status information includes location data, vehicle speed data and load data. The second cloud computing result is obtained based on the second vehicle status information through the vehicle network platform, and the second cloud computing result is sent to the TBOX. Vehicle scheduling is performed directly based on the results of the second cloud-based calculation via TBOX.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

7. A computer storage medium, characterized in that, The computer storage medium stores computer instructions that are used to cause a processor to execute the method of any one of claims 1-5.

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