Method, device, equipment and medium for optimizing dynamic scheduling of ore supply site

Abstract

The invention relates to a method, a device and equipment for optimizing dynamic scheduling of ore feeding spots and a medium. The ore supply site dynamic scheduling optimization method comprises the following steps: acquiring multi-dimensional key data of a plurality of ore supply sites in real time; calculating a real-time progress proportion of a plurality of ore supply spots based on the multi-dimensional key data; according to the real-time progress proportion, determining an optimal ore supply site in a plurality of ore supply sites; and sending a corresponding scheduling instruction to a target vehicle based on the optimal ore supply point. According to the embodiment of the invention, the deviation between the actual ore supply progress and the plan can be reduced, and the scheduling real-time performance is improved.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent scheduling technology for mine production, and in particular to a method, apparatus, equipment and medium for dynamic scheduling optimization of ore supply points. Background Technology

[0002] In open-pit mines, the main type of vehicle is the truck. These trucks are tasked with transporting excavated soil. When there are many trucks, a method needs to be found to schedule them to prevent congestion and inefficiency in transporting excavated soil.

[0003] Traditional polling scheduling or fixed-ratio allocation methods do not take into account transportation delays (such as truck transit time), resulting in a large deviation between the actual ore supply progress and the plan. Because the scheduling does not respond in real time to the differences in progress at each ore supply point, the grade of mixed ore exceeds the processing range of the concentrator. Existing systems rely on manual correction, which results in a long response time. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, apparatus, equipment, and medium for dynamic scheduling optimization of ore supply points.

[0005] Firstly, this disclosure provides a dynamic scheduling optimization method for ore supply points, including:

[0006] Real-time acquisition of multi-dimensional key data from multiple ore supply points;

[0007] Calculate the real-time progress ratio of multiple ore supply points based on the aforementioned multidimensional key data;

[0008] The optimal ore supply point is determined from multiple ore supply points based on the real-time progress ratio.

[0009] Based on the optimal ore supply point, the corresponding dispatch instruction is sent to the target vehicle.

[0010] Secondly, this disclosure provides a dynamic scheduling optimization device for ore supply points, comprising:

[0011] The data acquisition module is used to acquire multi-dimensional key data from multiple ore supply points in real time.

[0012] The proportion calculation module is used to calculate the real-time progress proportion of multiple ore supply points based on the multi-dimensional key data.

[0013] The data determination module is used to determine the optimal ore supply point among multiple ore supply points based on the real-time progress ratio.

[0014] The instruction sending module is used to send corresponding scheduling instructions to the target vehicle based on the optimal ore supply point.

[0015] Thirdly, this disclosure provides a dynamic scheduling and optimization device for ore supply points, including:

[0016] processor;

[0017] Memory, used to store executable instructions;

[0018] The processor is used to read executable instructions from memory and execute the executable instructions to implement the first aspect of the dynamic scheduling optimization method for ore supply points.

[0019] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables the processor to implement the dynamic scheduling optimization method for ore supply points as described in the first aspect.

[0020] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0021] The dynamic scheduling optimization method for ore supply points disclosed in this embodiment can acquire multi-dimensional key data of multiple ore supply points in real time, then calculate the real-time progress ratio of multiple ore supply points based on the multi-dimensional key data, then determine the optimal ore supply point among the multiple ore supply points according to the real-time progress ratio, and finally send the corresponding scheduling instruction to the target vehicle based on the optimal ore supply point. Therefore, by acquiring multi-dimensional key data of multiple ore supply points, calculating the real-time progress ratio of multiple ore supply points, and determining the optimal ore supply point, the deviation between the actual ore supply progress and the plan is reduced, and the real-time performance of scheduling is improved. Attached Figure Description

[0022] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0023] Figure 1 A flowchart illustrating a dynamic scheduling optimization method for ore supply points provided in this embodiment of the present disclosure;

[0024] Figure 2 This is a schematic diagram of the structure of a dynamic scheduling and optimization system for ore supply points provided in an embodiment of this disclosure;

[0025] Figure 3 A schematic diagram of a visual dashboard provided in an embodiment of this disclosure;

[0026] Figure 4 A flowchart illustrating another dynamic scheduling optimization method for ore supply points provided in this embodiment of the present disclosure;

[0027] Figure 5This is a schematic diagram of the structure of a dynamic scheduling optimization device for ore supply points provided in an embodiment of the present disclosure;

[0028] Figure 6 This is a schematic diagram of the structure of a dynamic scheduling and optimization device for a ore supply point provided in an embodiment of this disclosure. Detailed Implementation

[0029] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0030] It should be understood that the various steps described in the method implementation of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method implementation may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0031] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0032] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0033] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0034] The names of messages or information exchanged between multiple devices in this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0035] To address the aforementioned problems, this disclosure provides a method, apparatus, equipment, and medium for dynamic scheduling optimization of ore supply points. The following is a detailed explanation... Figures 1 to 4 The dynamic scheduling optimization method for ore supply points provided in the embodiments of this disclosure will be described in detail.

[0036] Figure 1 A flowchart illustrating a dynamic scheduling optimization method for ore supply points provided in an embodiment of this disclosure is shown.

[0037] In this embodiment of the disclosure, the dynamic scheduling optimization method for ore supply points can be executed by electronic devices. These electronic devices may include, but are not limited to, devices such as computer equipment, cloud servers, or cloud server clusters.

[0038] like Figure 1 As shown, the dynamic scheduling optimization method for ore supply points may include the following steps.

[0039] S110: Real-time acquisition of multi-dimensional key data from multiple ore supply points.

[0040] In this embodiment of the disclosure, the electronic device can acquire multi-dimensional key data from multiple ore supply points in real time.

[0041] Optionally, the ore supply point can be an open-pit mine, including iron ore, copper ore, or other ore that requires precise control of ore grade.

[0042] Optionally, multidimensional key data (three-dimensional key data) can be relevant transportation data, ore data, etc., collected for each ore supply point. Among these, multidimensional key data can include empty transport volume, in-transit loaded volume, and unloaded volume. Empty transport volume W empty This refers to the cumulative transport capacity (unit: tons) of empty trucks that have departed for the ore supply point but have not yet been loaded, and the on-the-way heavy load W. transit The total load capacity (in tons) of trucks that have been loaded and are en route, and the unloaded amount W. done This refers to the total amount of ore that has arrived at the target ore bin and been unloaded (unit: tons). When a vehicle is loaded, its load capacity is transferred from empty to en route loaded; when a vehicle is unloaded, its load capacity is transferred from en route loaded to unloaded.

[0043] Specifically, electronic devices can acquire multi-dimensional key data from multiple ore supply points in real time. For example, they can obtain empty transport volume through vehicle-mounted positioning terminals (such as Beidou / GPS dual-mode positioning terminals (model: UM220-III N), positioning update frequency 1Hz, horizontal accuracy ±1m), in-transit heavy load volume through dynamic weighing systems (such as vehicle-mounted weighing devices), and unloaded volume through ore bin weighing terminals (such as dynamic truck scales (model: SCS-50), scale division 20kg, sampling frequency 10Hz). Furthermore, communication between the electronic devices and vehicles is achieved through vehicle-to-ground communication (using a 5G private network (URLLC mode), end-to-end latency ≤30ms).

[0044] S120. Calculate the real-time progress ratio of multiple ore supply points based on the multi-dimensional key data.

[0045] In this embodiment of the disclosure, the electronic device can calculate the real-time progress ratio of multiple ore supply points based on the multi-dimensional key data.

[0046] Optionally, the real-time progress ratio can be used to characterize the proportion of ore transportation tasks completed at multiple ore supply points in the current stage.

[0047] Specifically, after acquiring multidimensional key data, the electronic equipment can calculate the real-time progress ratio of multiple ore supply points based on the multidimensional key data, such as empty transport volume, in-transit heavy load volume, and unloaded volume.

[0048] S130. Determine the optimal ore supply point among multiple ore supply points based on the real-time progress ratio.

[0049] In this embodiment of the disclosure, the electronic device can determine the optimal ore supply point among multiple ore supply points based on the real-time progress ratio.

[0050] Optionally, the optimal ore supply point can be the most suitable ore supply point among multiple ore supply points.

[0051] Specifically, after obtaining the real-time progress ratios of multiple ore supply points, the electronic equipment can analyze these ratios and determine the optimal ore supply point among them based on the real-time progress ratios.

[0052] S140. Send the corresponding scheduling instruction to the target vehicle based on the optimal ore supply point.

[0053] In this embodiment of the disclosure, the electronic device can send corresponding scheduling instructions to the target vehicle based on the optimal ore supply point.

[0054] Optionally, the dispatching instruction can be an instruction used to dispatch target vehicles to the optimal ore supply point for transportation tasks.

[0055] Therefore, in this embodiment, multi-dimensional key data from multiple ore supply points can be acquired in real time. Then, based on the multi-dimensional key data, the real-time progress ratio of the multiple ore supply points is calculated. Next, the optimal ore supply point is determined among the multiple ore supply points according to the real-time progress ratio. Finally, a corresponding dispatch instruction is sent to the target vehicle based on the optimal ore supply point. Thus, by acquiring multi-dimensional key data from multiple ore supply points, calculating the real-time progress ratio of multiple ore supply points, and determining the optimal ore supply point, the deviation between the actual ore supply progress and the plan is reduced, and the real-time performance of dispatching is improved.

[0056] Optionally, S120 may specifically include: determining the planned total amount for each ore supply point; and calculating the real-time progress ratio corresponding to each ore supply point based on the planned total amount and the multi-dimensional key data.

[0057] In this embodiment of the disclosure, the electronic device can determine the planned total amount for each ore supply point.

[0058] Optionally, the planned total Q plan Total planned ore supply to the ore supply point (unit: tons).

[0059] Specifically, electronic equipment can determine the planned total quantity Q for each ore supply point. plan .

[0060] Furthermore, the electronic equipment can calculate the real-time progress ratio corresponding to each ore supply point based on the total planned amount and the multi-dimensional key data.

[0061] Specifically, in determining the planned total quantity Q for each ore supply point... plan Afterwards, electronic devices can be configured according to the total amount Q of the plan. plan and multidimensional key data (empty transport volume W) empty Heavy load capacity on the road (W) transit And the amount of unloaded W done Calculate the real-time progress ratio for each ore supply point.

[0062] Taking three ore supply points (P1 / P2 / P3) of a certain iron ore mine as an example, the planned total amount and multi-dimensional key data collected in real time are shown in Table 1 below:

[0063] Table 1: Total Planned Amount and Key Multidimensional Data

[0064]

[0065] Optionally, calculating the real-time progress ratio corresponding to each ore supply point based on the total planned amount and the multi-dimensional key data may specifically include: adding the multi-dimensional key data together and calculating the ratio to the total planned amount to obtain the real-time progress ratio corresponding to each ore supply point.

[0066] In this embodiment of the disclosure, the electronic device can add up the multi-dimensional key data and calculate the ratio to the planned total to obtain the real-time progress ratio corresponding to each ore supply point.

[0067] Among them, the real-time progress ratio α is calculated. i The formula is:

[0068]

[0069] Specifically, electronic equipment can first load the empty transport volume W empty Heavy load capacity on the road (W) transit And the amount of unloaded W done Add them together, then calculate the total amount Q (as planned). plan The ratio is used to obtain the real-time progress ratio α corresponding to each ore supply point. i .

[0070] Taking three ore supply points (P1 / P2 / P3) of a certain iron ore mine as an example, the calculated real-time progress ratio α is... i The results are shown in Table 2 below.

[0071] Table 2: Real-time Progress Ratio

[0072]

[0073] Therefore, the calculation includes data from three stages: empty load, en route, and unloaded, avoiding the inflated progress figures caused by neglecting the amount en route in traditional methods (the measured error is reduced from ±18% to ±2%). When a vehicle completes unloading, its load capacity is transferred from "en route heavy load" to "unloaded load," α i It updates synchronously and has dynamic correction capabilities.

[0074] Optionally, S130 may specifically include: sorting the real-time progress ratios in ascending order to determine the minimum real-time progress ratio; and determining the ore supply point corresponding to the minimum real-time progress ratio as the optimal ore supply point.

[0075] In this embodiment of the disclosure, the electronic device can sort the real-time progress ratios in ascending order to determine the minimum real-time progress ratio.

[0076] Alternatively, ascending sort can be used to arrange values ​​in ascending order.

[0077] Specifically, after obtaining multiple real-time progress ratios corresponding to multiple ore supply points, the electronic equipment can sort the multiple real-time progress ratios in ascending order and determine the minimum real-time progress ratio.

[0078] Furthermore, the electronic device can determine the ore supply point corresponding to the smallest real-time progress ratio as the optimal ore supply point. For example, for multiple ore supply points P1, P2, and P3, the corresponding real-time progress ratios are 25%, 40%, and 50%, respectively. Thus, the ore supply point P1 corresponding to the real-time progress ratio of 25% can be determined as the optimal ore supply point.

[0079] Optionally, after sorting the real-time progress ratios in ascending order and determining the smallest real-time progress ratio, the method further includes: if there are multiple smallest real-time progress ratios, calculating the absolute difference between the current mixed grade and the target grade of the multiple target ore supply points corresponding to the multiple smallest real-time progress ratios; and determining the target ore supply point with the smallest absolute difference as the optimal ore supply point.

[0080] In this embodiment of the disclosure, if there are multiple minimum real-time progress ratios, the electronic device can calculate the absolute difference between the current mixed grade and the target grade of multiple target ore supply points corresponding to the multiple minimum real-time progress ratios.

[0081] Optionally, the current blend grade refers to the content of useful components or useful minerals in the ore (or beneficiation product).

[0082] Optionally, the target grade can be a pre-set content.

[0083] Specifically, after obtaining multiple real-time progress ratios, if there are multiple minimum real-time progress ratios, the electronic equipment can calculate the absolute difference between the current mixed grade and the target grade of multiple target ore supply points corresponding to the multiple minimum real-time progress ratios.

[0084] Furthermore, the electronic device can determine the target ore supply point with the smallest absolute difference as the optimal ore supply point.

[0085] The formula for calculating the absolute difference can be:

[0086]

[0087] In the formula, i * The ore deposit number is the optimal ore supply point; W is the current cumulative total ore volume (tons); G current The current mixed grade (%); Q i For single transport volume (tons); G i For candidate ore deposits, the ore grade (%) is given; G target Target grade (%).

[0088] For example, the current total cumulative ore volume W = 10,000 tons, and the single transport volume Q i =50 tons, current mixed grade G current =58%, target grade G target =60%, and the real-time progress ratio of candidate mining sites is 25%.

[0089] The calculation results of the absolute difference are shown in the table below.

[0090] Table 3: Absolute Difference

[0091]

[0092] Among them, the target ore supply point with the smallest absolute difference is determined as the optimal ore supply point, that is, the ore supply point with the smallest absolute difference is selected (P1 (deviation 1.97%, because 58.03% grade is closer to the target 60%)).

[0093] Optionally, S140 may specifically include: in response to the scheduling request of the target vehicle, sending a corresponding scheduling instruction to the target vehicle through a low-latency channel based on the optimal ore supply point.

[0094] In this embodiment of the disclosure, the electronic device can respond to the scheduling request of the target vehicle by sending a corresponding scheduling instruction to the target vehicle based on the optimal ore supply point through a low-latency channel.

[0095] Specifically, the target vehicle can send a scheduling request to the electronic device. After receiving the scheduling request, the electronic device responds by sending a corresponding scheduling instruction to the target vehicle through a 5G low-latency channel based on the optimal ore supply point.

[0096] As a result, the fluctuation range of mixed ore grade has been narrowed from ±7% in traditional methods to ±2%, and the number of times the concentrator has stopped due to grade exceeding the standard has decreased by 83%. In case of abnormal situations such as equipment failure, the system can automatically rebalance the ore supply schedule within 15 minutes without manual intervention.

[0097] Figure 2 A schematic diagram of the structure of a dynamic scheduling and optimization system for ore supply points provided in an embodiment of this disclosure is shown.

[0098] like Figure 2 As shown, in the data acquisition layer, multi-dimensional key data, including empty transport volume, in-transit heavy load volume, and unloaded volume, can be acquired in real time through vehicle-mounted terminals and ore bin weighing terminals. In the calculation layer, electronic devices (cloud servers) can acquire the data collected in the data acquisition layer, calculate the real-time progress ratio of multiple ore supply points through a three-dimensional calculation engine, and determine the optimal ore supply point through a dual-weight arbitrator. For example, the first weight is determined by sorting the real-time progress ratio in ascending order and identifying the smallest real-time progress ratio as the optimal ore supply point; the second weight is determined by calculating the absolute difference between the current mixed grade and the target grade of multiple target ore supply points corresponding to the multiple smallest real-time progress ratios when multiple smallest real-time progress ratios exist, and identifying the target ore supply point with the smallest absolute difference as the optimal ore supply point. In the interaction layer, the status of empty / heavy trucks can be tracked and displayed in real time through a visual dashboard in the central control room, thereby reducing the blind spot of progress calculation from 19.2% in traditional methods to 2.1%.

[0099] Figure 3 A schematic diagram of a visual dashboard provided in an embodiment of this disclosure is shown.

[0100] like Figure 3 As shown, the status of empty / loaded trucks can be tracked and displayed in real time through the visual dashboard in the central control room, thereby reducing the blind spot in progress calculation from 19.2% in the traditional method to 2.1%.

[0101] Figure 4 A flowchart illustrating another dynamic scheduling optimization method for ore supply points provided in this embodiment is shown.

[0102] like Figure 4As shown, the electronic equipment can acquire multi-dimensional key data from multiple ore supply points in real time, such as: empty transport volume (referring to the cumulative transport capacity of empty trucks that have departed for the ore supply point but have not yet been loaded); on-the-way loaded volume (referring to the total load capacity of trucks that have been loaded and are en route); and unloaded volume (referring to the total amount of ore that has arrived at the target ore bin and been unloaded). Then, a three-dimensional calculation engine calculates the real-time progress ratio of multiple ore supply points, and a dual-weight arbitrator determines the optimal ore supply point. The first weight is determined by sorting the ore supply points in ascending order of their real-time progress ratios, identifying the point with the smallest real-time progress ratio as the optimal ore supply point. The second weight is used when multiple smallest real-time progress ratios exist; it calculates the absolute difference between the current mixed grade and the target grade of each of the multiple target ore supply points corresponding to these smallest real-time progress ratios, and determines the target ore supply point with the smallest absolute difference as the optimal ore supply point. The status of empty / loaded trucks can be tracked and displayed in real time through a visual dashboard in the central control room. The target vehicle can send a scheduling request to the electronic device. After receiving the scheduling request, the electronic device responds by sending a corresponding scheduling instruction to the target vehicle through a 5G low-latency channel based on the optimal ore supply point.

[0103] Figure 5 A schematic diagram of the structure of a dynamic scheduling optimization device for ore supply points provided in an embodiment of this disclosure is shown.

[0104] like Figure 5 As shown, the dynamic scheduling optimization device 500 for ore supply points may include a data acquisition module 510, a ratio calculation module 520, a data determination module 530, and an instruction sending module 540.

[0105] The data acquisition module 510 can be used to acquire multi-dimensional key data from multiple ore supply points in real time;

[0106] The ratio calculation module 520 can be used to calculate the real-time progress ratio of multiple ore supply points based on the multi-dimensional key data;

[0107] The data determination module 530 can be used to determine the optimal ore supply point among multiple ore supply points based on the real-time progress ratio;

[0108] The instruction sending module 540 can be used to send corresponding scheduling instructions to the target vehicle based on the optimal ore supply point.

[0109] Therefore, in this embodiment, multi-dimensional key data from multiple ore supply points can be acquired in real time. Then, based on the multi-dimensional key data, the real-time progress ratio of the multiple ore supply points is calculated. Next, the optimal ore supply point is determined among the multiple ore supply points according to the real-time progress ratio. Finally, a corresponding dispatch instruction is sent to the target vehicle based on the optimal ore supply point. Thus, by acquiring multi-dimensional key data from multiple ore supply points, calculating the real-time progress ratio of multiple ore supply points, and determining the optimal ore supply point, the deviation between the actual ore supply progress and the plan is reduced, and the real-time performance of dispatching is improved.

[0110] In some embodiments of this disclosure, the multidimensional key data includes empty transport volume, in-transit heavy load volume, and unloaded volume.

[0111] In some embodiments of this disclosure, the ratio calculation module 520 may specifically include:

[0112] The first determining unit can be used to determine the planned total amount for each ore supply point.

[0113] The first calculation unit can be used to calculate the real-time progress ratio of each ore supply point based on the total planned amount and the multi-dimensional key data.

[0114] In some embodiments of this disclosure, the first calculation unit may be specifically used to add the multidimensional key data together and calculate the ratio to the planned total amount to obtain the real-time progress ratio corresponding to each ore supply point.

[0115] In some embodiments of this disclosure, the data determination module 530 may specifically include:

[0116] The second determining unit can be used to sort the real-time progress ratios in ascending order and determine the minimum real-time progress ratio.

[0117] The third determining unit can be used to determine the ore supply point corresponding to the smallest real-time progress ratio as the optimal ore supply point.

[0118] In some embodiments of this disclosure, the data determination module 530 may specifically include:

[0119] The second calculation unit can be used to calculate the absolute difference between the current mixed grade and the target grade of multiple target ore supply points corresponding to multiple minimum real-time progress ratios, if multiple minimum real-time progress ratios exist.

[0120] The fourth determining unit can be used to determine the target ore supply point with the smallest absolute difference as the optimal ore supply point.

[0121] In some embodiments of this disclosure, the instruction sending module 540 may be specifically used to respond to the scheduling request of the target vehicle by sending a corresponding scheduling instruction to the target vehicle based on the optimal ore supply point through a low-latency channel.

[0122] It should be noted that, Figure 5 The dynamic scheduling optimization device 500 for ore supply points shown can perform... Figures 1 to 4 The various steps in the method embodiment shown are implemented. Figures 1 to 4 The processes and effects in the method embodiments shown are not described in detail here.

[0123] Figure 6 A schematic diagram of the structure of a dynamic scheduling optimization device for ore supply points provided in an embodiment of this disclosure is shown.

[0124] In some embodiments of this disclosure, Figure 6 The dynamic scheduling and optimization equipment for the ore supply points shown can be electronic equipment. Specifically, electronic equipment can include, but is not limited to, devices such as computer equipment, cloud servers, or cloud server clusters.

[0125] like Figure 6 As shown, the dynamic scheduling and optimization device for the ore supply point may include a processor 601 and a memory 602 storing computer program instructions.

[0126] Specifically, the processor 601 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0127] Memory 602 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the integrated gateway device. In a particular embodiment, memory 602 is a non-volatile solid-state memory. In a particular embodiment, memory 602 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0128] The processor 601 reads and executes computer program instructions stored in the memory 602 to perform the steps of the dynamic scheduling optimization method for mining points provided in this embodiment of the present disclosure.

[0129] In one example, the dynamic scheduling optimization device for the ore supply point may further include a transceiver 603 and a bus 604. Wherein, as... Figure 6 As shown, the processor 601, memory 602 and transceiver 603 are connected via bus 604 and communicate with each other.

[0130] Bus 604 includes hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 604 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0131] This disclosure also provides a computer-readable storage medium that can store a computer program. When the computer program is executed by a processor, the processor enables the processor to implement the dynamic scheduling optimization method for mining points provided in this disclosure.

[0132] The aforementioned storage medium may include, for example, a memory 602 containing computer program instructions, which can be executed by the processor 601 of the ore supply point dynamic scheduling optimization device to complete the ore supply point dynamic scheduling optimization method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0133] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0134] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic scheduling optimization method for ore supply points, characterized in that, include: Real-time acquisition of multi-dimensional key data from multiple ore supply points; Calculate the real-time progress ratio of multiple ore supply points based on the aforementioned multidimensional key data; The optimal ore supply point is determined from multiple ore supply points based on the real-time progress ratio. Based on the optimal ore supply point, the corresponding dispatch instruction is sent to the target vehicle.

2. The method according to claim 1, characterized in that, The multidimensional key data includes empty transport volume, in-transit heavy load volume, and unloaded volume.

3. The method according to claim 1, characterized in that, The calculation of the real-time progress ratio of multiple ore supply points based on the multi-dimensional key data includes: Determine the planned total amount for each ore supply point; Based on the total planned amount and the multidimensional key data, calculate the real-time progress ratio corresponding to each ore supply point.

4. The method according to claim 3, characterized in that, The calculation of the real-time progress ratio for each ore supply point based on the planned total and the multi-dimensional key data includes: The ratio of the sum of the multidimensional key data to the total planned amount is calculated to obtain the real-time progress ratio corresponding to each ore supply point.

5. The method according to claim 1, characterized in that, The step of determining the optimal ore supply point among multiple ore supply points based on the real-time progress ratio includes: The real-time progress percentages are sorted in ascending order to determine the smallest real-time progress percentage. The ore supply point corresponding to the smallest real-time progress ratio is determined as the optimal ore supply point.

6. The method according to claim 5, characterized in that, After sorting the real-time progress percentages in ascending order and determining the smallest real-time progress percentage, the method further includes: If there are multiple minimum real-time progress ratios, calculate the absolute difference between the current mixed grade and the target grade of the multiple target ore supply points corresponding to the multiple minimum real-time progress ratios respectively. The target ore supply point with the smallest absolute difference is determined as the optimal ore supply point.

7. The method according to claim 1, characterized in that, The step of sending corresponding dispatch instructions to the target vehicle based on the optimal ore supply point includes: In response to the scheduling request of the target vehicle, a corresponding scheduling instruction is sent to the target vehicle via a low-latency channel based on the optimal ore supply point.

8. A dynamic scheduling and optimization device for ore supply points, characterized in that, include: The data acquisition module is used to acquire multi-dimensional key data from multiple ore supply points in real time. The proportion calculation module is used to calculate the real-time progress proportion of multiple ore supply points based on the multi-dimensional key data. The data determination module is used to determine the optimal ore supply point among multiple ore supply points based on the real-time progress ratio. The instruction sending module is used to send corresponding scheduling instructions to the target vehicle based on the optimal ore supply point.

9. A dynamic scheduling and optimization device for ore supply points, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the dynamic scheduling optimization method for ore supply points as described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the dynamic scheduling optimization method for ore supply points as described in any one of claims 1-7.

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