Mine automation control method and system based on SCADA

Abstract

The invention relates to the technical field of mine production scheduling control, in particular to an SCADA-based mine automatic control method and system. Comprising the following steps: acquiring vehicle position, equipment operation parameters and material type data from a transportation channel and key nodes through a sensor network to obtain real-time load distribution and state indexes; calculating the passing time and the congestion probability of each transportation path by adopting a path planning algorithm, and generating a preliminary shunting option; adjusting the preliminary shunting option through an optimization algorithm to obtain a transportation path set meeting the material quality requirement and the node processing capability, judging whether a continuity constraint is met or not, and generating a shunting decision meeting the process requirement if the continuity constraint is met; if the shunting decision shows that the node is overloaded, re-routing to a standby path, and generating an updated scheduling instruction; and sending the navigation update to the transport vehicle, and adjusting the shunting decision according to the transport efficiency index. According to the invention, through real-time monitoring and dynamic adjustment, efficient and stable operation of the mine transportation system is ensured.

Description

Technical Field

[0001] This invention relates to the field of mine production scheduling and control technology, and in particular to a mine automation control method and system based on SCADA. Background Technology

[0002] With the continuous improvement of mine automation, intelligent control of mine production scheduling and transportation systems has gradually become an important direction for improving mine operational efficiency and reducing labor costs. In traditional mine transportation management, the transportation process is affected by many factors such as personnel operation, equipment condition, and environmental factors, leading to problems such as low transportation efficiency, inability to guarantee mineral quantity, and unreasonable resource allocation. Especially in the mine transportation process, the planning of transportation routes, the allocation of material flow, and the load control of each node have become key issues, and traditional manual control and experience-based decision-making methods often cannot efficiently solve these problems.

[0003] In mining transportation systems, congestion on transport routes, overloading at key points, and material type matching issues all directly impact the efficiency and quality of ore transportation. Therefore, ensuring efficient and stable operations throughout the mining transportation process, while meeting the quality requirements of ore transportation, through precise route planning, dynamic load adjustment, and continuous material matching management, has become one of the core challenges of mining automation control technology.

[0004] To address the aforementioned issues, this invention provides a SCADA-based mine automation control method and system. Through multiple technical means such as path congestion assessment, material type matching, and load balancing, it achieves real-time monitoring and intelligent scheduling during mine transportation, effectively avoiding problems such as node overload, path congestion, and material quality degradation, thereby improving the intelligence and automation level of mine production and transportation. Summary of the Invention

[0005] This invention provides a SCADA-based automated control method for mines, mainly comprising: Step S1: Obtain vehicle location, equipment operating parameters, and material type data from transportation channels and key nodes through a sensor network to obtain real-time load distribution and status indicators for each channel in the mine; Step S2: Based on the real-time load distribution and status indicators, use a path planning algorithm to calculate the travel time and congestion probability of each transportation path in the mine, and generate preliminary diversion options; Step S3: Adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, determine whether the continuity constraint is met; if it is met, generate a diversion decision that meets the process requirements. Step S4: If the routing decision display node is overloaded, a path planning algorithm is used to reroute to an alternative path and generate an updated scheduling instruction; Step S5: Based on the updated scheduling instructions, send navigation updates to the transport vehicles through the SCADA system to obtain the optimized transport efficiency index; based on the transport efficiency index, reacquire the updated data to determine whether further adjustments to the diversion decision are needed.

[0006] As a preferred embodiment of the present invention, step S1 includes: By collecting real-time vehicle location data, speed data, and equipment operating parameters in the transportation corridor through a sensor network, the real-time load distribution of each corridor is determined; based on the vehicle location data and speed data, a path congestion assessment index is calculated; based on the equipment operating parameters and material type data, a status index is generated; and based on the path congestion assessment index and the status index, a real-time load distribution model is constructed.

[0007] 3. As a preferred embodiment of the present invention, step S2 includes: Based on the real-time load distribution of the mine transportation corridor, the estimated travel time of each transportation route is calculated, and the congestion probability of each route is determined based on the status indicators of the transportation corridor. Based on the estimated travel time and congestion probability, multiple potential diversion options are generated; for these potential diversion options, material quality requirement verification is incorporated, and preliminary diversion options that meet the quality requirements are selected.

[0008] As a preferred embodiment of the present invention, step S3 involves obtaining a set of transportation paths that meet the quality requirements and node processing capabilities, including: If any of the initial diversion options has a congestion probability higher than a preset threshold, a genetic algorithm is used to optimize the material flow allocation scheme. Based on the optimization results of the genetic algorithm, an adjusted set of transportation paths is generated. Combining the processing capacity data of key nodes, the load distribution balance of the transportation path set is evaluated. Based on the load distribution balance, preliminary adjustment parameters are generated for subsequent continuity constraint checks.

[0009] As a preferred embodiment of the present invention, step S3, which involves determining whether the continuity constraint is met based on the set of transportation routes and process requirements to obtain a diversion decision, includes: Obtain material quality matching requirements from the process requirements database; determine whether the set of transportation paths meets the continuity constraint based on the material quality matching requirements; if the continuity constraint is met, generate a diversion decision that meets the quality requirements; determine the overload detection threshold based on the processing capacity data of the key nodes, and generate load balancing adjustment parameters for subsequent scheduling instruction generation.

[0010] As a preferred embodiment of the present invention, step S4 includes: Based on the diversion decision, it is determined whether any nodes exceed the overload detection threshold; if overloaded nodes exist, a path planning algorithm is used to recalculate alternative paths; based on the alternative paths, an updated set of transportation paths is generated; combined with the real-time load distribution and status indicators, the updated set of transportation paths is optimized, and a scheduling instruction is generated.

[0011] As a preferred embodiment of the present invention, step S5, obtaining the optimized transportation efficiency index, includes: According to the scheduling instructions, real-time navigation updates are sent to the transport vehicles; according to the navigation updates, the vehicle travel routes are adjusted; according to the adjusted travel routes, the overall transport efficiency index is calculated; according to the overall transport efficiency index, the effectiveness of the backup route allocation is evaluated, and local anti-congestion balance parameters are generated.

[0012] As a preferred embodiment of the present invention, step S5, determining whether further adjustments to the diversion decision are needed, includes: Based on the transportation efficiency index, vehicle locations and equipment operating parameters are re-collected through a sensor network; based on the re-collected data, real-time load distribution and status indicators are updated; based on the updated real-time load distribution, route congestion is re-assessed; based on the route congestion assessment results, it is determined whether the diversion decision needs to be adjusted, and a cyclically adjusted diversion plan is generated.

[0013] As a preferred embodiment of the present invention, step S5, determining whether the continuity constraint is satisfied to obtain the diversion decision, further includes: Based on the set of transportation routes, material type matching rules are obtained from the process requirements database; based on the material type matching rules, the continuity constraints of the set of transportation routes are verified; if the continuity constraints are not met, the material flow allocation is adjusted; based on the adjusted material flow allocation, a diversion decision that meets the quality requirements is generated.

[0014] The present invention also provides a SCADA-based mine automation control system for implementing the above method, the system comprising: The data acquisition unit is used to obtain vehicle location, equipment operating parameters and material type data from transportation channels and key nodes through sensor networks, so as to obtain the real-time load distribution and status indicators of each channel in the mine; The route planning unit is used to calculate the travel time and congestion probability of each transportation route in the mine based on the real-time load distribution and status indicators, and to generate preliminary diversion options. The diversion optimization unit is used to adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, it determines whether the continuity constraint is met, and if so, generates a diversion decision that meets the process requirements. The path routing unit is used to reroute to an alternative path using a path planning algorithm and generate an updated scheduling instruction if the diversion decision display node is overloaded. The navigation update unit is used to send navigation updates to transport vehicles through the SCADA system according to the updated scheduling instructions to obtain optimized transport efficiency indicators; and to reacquire updated data based on the transport efficiency indicators to determine whether further adjustments to the diversion decision are needed.

[0015] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention uses a sensor network to collect real-time data on vehicle locations, equipment operating parameters, and material types at key nodes along mine transportation routes. The system can acquire real-time load distribution and status indicators for each mine route, providing a foundation for subsequent route planning and load balancing. A route planning algorithm is employed to assess the travel time of each route based on real-time load distribution and route congestion probability, generating initial diversion options to ensure that route selection considers both load allocation and material quality requirements. The initial diversion options are further adjusted through an optimization algorithm to ensure that each route meets the requirements for material quality and node processing capacity. By verifying whether the routes meet continuity constraints, the quality degradation caused by mixed material transport is avoided, ensuring the continuity of material quality. If route congestion or node overload is detected, the route planning algorithm reroutes to an alternative route and generates new scheduling instructions, which are then transmitted via the wireless sensor network. The data is updated and transmitted to transport vehicles to ensure that they can adjust their routes in real time. To further improve system efficiency, transport efficiency indicators are evaluated and routes are dynamically adjusted. Real-time data collection allows the system to calculate the transport efficiency of each route and optimize material flow through a genetic algorithm to ensure balanced load at nodes and avoid overloading. The genetic algorithm iteratively optimizes the material flow allocation scheme, using the path congestion probability and continuity score as fitness functions to generate the optimal diversion decision. Through the cooperation of the above technical solutions, not only can the mine transport routes be dynamically adjusted according to real-time data to avoid congestion and overloading and optimize transport efficiency, but the continuity and quality requirements of material transport can also be guaranteed. This achieves load balance in the mine transport process, significantly improves the intelligence and automation level of mine transport, reduces the impact of emergencies on the transport system, and thus improves the overall transport efficiency and stability. Attached Figure Description

[0016] Figure 1This is a flowchart of a SCADA-based mine automation control method in an embodiment of the present invention; Figure 2 This is a structural diagram of a SCADA-based mine automation control system in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 This embodiment describes a SCADA-based mine automation control method, specifically including: Step S1: Obtain vehicle location, equipment operating parameters, and material type data from transportation channels and key nodes through a sensor network to obtain real-time load distribution and status indicators for each channel in the mine; specifically including: By collecting real-time vehicle location data, speed data, and equipment operating parameters in the transportation corridor through a sensor network, the real-time load distribution of each corridor is determined; based on the vehicle location data and speed data, a path congestion assessment index is calculated; based on the equipment operating parameters and material type data, a status index is generated; and based on the path congestion assessment index and the status index, a real-time load distribution model is constructed.

[0019] Specifically, by deploying sensor networks along mine transport routes and at key nodes, real-time vehicle location data, speed data, and equipment operating parameters are collected. The vehicle location and speed data collected by the sensors accurately determine the real-time load distribution of each transport route. This real-time load distribution reflects the density, speed, and capacity of vehicles on each transport path. Based on this data, route congestion assessment indicators are calculated. These indicators represent the traffic congestion situation of each route and specifically include the number of vehicles in the route, average speed, and vehicle spacing. If the vehicle speed is below a certain threshold and the vehicle spacing is less than a certain standard, the congestion assessment value will increase, indicating that the route is experiencing congestion. The risk of congestion is relatively high, therefore, route congestion assessment indicators can provide important references for route planning. In addition to vehicle location and speed data, equipment operating parameters and material type data are collected in real time through sensor networks. Among them, equipment operating parameters include key data such as engine speed and load, while material type data refers to the type and quality requirements of materials during transportation. Status indicators are generated from the above equipment operating parameters and material type data. The status indicators reflect the health status of equipment operation and the quality requirements of materials, ensuring that the scheduling process can take into account both the working efficiency of equipment and the transportation requirements of materials, and avoid transportation problems caused by equipment failure or material mismatch.

[0020] Based on the collected route congestion assessment indicators and status indicators, a real-time load distribution model is constructed. This model combines the congestion status of the routes with the operating status of the equipment, representing the load distribution of the channels in matrix form. Each row of the matrix represents a transportation channel or node, and each column represents the corresponding congestion indicator or status indicator. Through normalization processing, the model can standardize these indicators into a quantifiable data format, facilitating subsequent route planning algorithms. This model can not only dynamically display the load status of each channel but also update in real time based on the collected data to adapt to the constantly changing transportation environment. On this basis, the optimal route is calculated using a route planning algorithm. By evaluating the estimated travel time and congestion probability of each route, the optimal transportation route is selected. The route planning algorithm determines the optimal route based on the congestion indicators and status indicators calculated in real time by the real-time load distribution model using the collected location data, speed data, equipment operating parameters, and material type data. In this process, the specific conditions of different mining environments are dynamically considered, such as mine depth and passage width, and the parameters of the path planning algorithm are adjusted to make the path planning more in line with the actual situation. For example, in the depths of the mine, due to poor ventilation conditions, the sensitivity to the path status is increased to ensure the smooth transportation of ore.

[0021] The above-mentioned technical solution can effectively integrate various data acquired by sensor networks, including vehicle location, speed, equipment operating parameters, and material type information. It optimizes routes through path planning algorithms to ensure that the mining transportation system can dynamically respond to real-time load fluctuations, congestion risks, and material quality requirements. This maximizes transportation efficiency, balances node loads, and ensures continuous material quality. Furthermore, through continuously updated data and optimized decisions, it can adjust transportation routes in a timely manner to avoid overloading, congestion, and other problems. This improves the intelligence and automation level of mining transportation scheduling and ensures the stable operation of the mining transportation system.

[0022] Step S2: Based on the real-time load distribution and status indicators, a path planning algorithm is used to calculate the travel time and congestion probability of each transportation path in the mine, generating preliminary diversion options; specifically including: Based on the real-time load distribution of the mine transportation corridor, the estimated travel time of each transportation route is calculated, and the congestion probability of each route is determined based on the status indicators of the transportation corridor. Based on the estimated travel time and congestion probability, multiple potential diversion options are generated; for these potential diversion options, material quality requirement verification is incorporated, and preliminary diversion options that meet the quality requirements are selected.

[0023] Specifically, in this embodiment, real-time load distribution data collected through a sensor network, including vehicle location, speed, and equipment operating parameters, helps the system accurately calculate the estimated travel time for each transportation route. By combining real-time load data for each route, such as vehicle density, channel capacity, and travel speed, a cost value is calculated for each route based on the aforementioned real-time load data. Routes in high-load areas are assigned higher cost values, thereby increasing the estimated travel time for that route. In addition, the probability of route congestion is also calculated based on status indicators, including vehicle speed and material matching information. Specifically, the status indicators calculate the likelihood of congestion by monitoring vehicle speed and material type in real time. For example, when the proportion of slow-moving vehicles on a route exceeds a certain threshold, the probability of route congestion increases significantly, indicating a potential bottleneck on that route.

[0024] By calculating the estimated travel time and congestion probability using real-time load distribution and status indicators, multiple potential diversion options are generated. To ensure that these diversion options not only effectively distribute the load but also meet the material quality requirements during mine transportation, material quality requirements are used as an important verification standard. In this process, verification is first performed based on the material matching requirements in the process requirements database. For example, certain ore types may require the humidity, temperature, or other environmental parameters of the route to be within a specific range. By checking the environmental parameters of each potential route, it is ensured that these routes meet the material quality requirements. If a route does not meet the requirements, it is excluded from the initial diversion options. For situations where different ore types may be involved in the transportation process, routes are further screened based on the quality requirements of different ores. For example, for rare ores, it may be necessary to ensure that the temperature and humidity of the route are suitable for the transportation requirements of the ore. If the conditions of the route do not meet the requirements, the route will also be excluded.

[0025] Through the above technical solutions, the selected routes will form preliminary diversion options that meet quality requirements for subsequent transportation scheduling. This not only takes into account the congestion and load of the routes, but also ensures that the ore maintains the required quality during transportation, thereby improving the overall efficiency and stability of the mining transportation system. It ensures that mining transportation scheduling can avoid congestion and overloading while ensuring material quality, improves the level of intelligent management of mining transportation, and ensures the efficient completion of transportation tasks.

[0026] Step S3: Adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, determine whether the continuity constraint is met; if it is met, generate a diversion decision that meets the process requirements. In step S3, a set of transportation routes that meet the quality requirements and node processing capabilities is obtained, including: If any of the initial diversion options has a congestion probability higher than a preset threshold, a genetic algorithm is used to optimize the material flow allocation scheme. Based on the optimization results of the genetic algorithm, an adjusted set of transportation paths is generated. Combining the processing capacity data of key nodes, the load distribution balance of the transportation path set is evaluated. Based on the load distribution balance, preliminary adjustment parameters are generated for subsequent continuity constraint checks.

[0027] Specifically, in this embodiment, in order to adjust the initial diversion options through optimization algorithms and generate a set of transportation paths that meet the quality requirements and node processing capabilities of the mining transportation system, it is determined whether any of the initial diversion options have a congestion probability higher than a preset threshold. If so, a genetic algorithm is used to optimize the material flow. The genetic algorithm is an optimization search algorithm based on natural selection and genetic mechanisms, which solves the optimal problem by simulating the population evolution process. In this process, each transportation path in the initial diversion options is regarded as an initial population, and the path allocation scheme is regarded as a chromosome. Each chromosome includes information such as material type, number of vehicles, and path selection. The merits of each path are evaluated by a fitness function, which comprehensively considers multiple factors such as the path's congestion probability, estimated travel time, and material type matching degree. The algorithm selects high-quality solutions based on their fitness values. Through selection, crossover, and mutation operations, it generates a new generation of optimized path solutions. This process continues for multiple generations until it converges to an optimized solution whose congestion probability is below a preset threshold. Specifically, in the underground mine transportation scenario, assuming that the congestion probability of path B is 0.35, exceeding the set threshold of 0.3, the genetic algorithm will optimize the material flow and divert some materials from B to other paths A and C, thereby reducing the congestion of path B to 0.25, reducing vehicle waiting time, and improving transportation efficiency.

[0028] Based on the optimization results of the genetic algorithm, an adjusted set of transportation routes is generated. This set includes an updated list of routes and their corresponding material flows. To further optimize the load distribution of the transportation routes, the adjusted route set is evaluated using the processing capacity data of key nodes in the mine. The processing capacity data of key nodes includes the maximum carrying capacity and current load of each node. The load ratio of each node is obtained and calculated in real time from the sensor network. By calculating the load ratio of each node, i.e., the number of currently allocated vehicles divided by the node's maximum processing capacity, if the standard deviation of the load ratio is less than a preset threshold, it is considered that the load distribution is balanced. If the standard deviation exceeds the threshold, it indicates that the load is unbalanced and further optimization is needed. For example, assuming that after the mine transportation route is adjusted, the load ratio of node Y is 0.7 and the load ratio of node Z is 0.4, the calculated standard deviation is 0.15, which exceeds the preset threshold of 0.1, indicating that there is an unbalanced load in this area. At this time, the risk of overload is identified, parameters are adjusted in time to avoid node congestion, thereby ensuring the smooth operation of the transportation system.

[0029] To further optimize the route set and ensure the continuity and stability of mine transportation, preliminary adjustment parameters, such as diversion ratio adjustment values, are generated based on the load distribution balance. For example, when the load on a certain node is too high, adjustment parameters are generated to reduce the flow allocation to that node, thereby ensuring load balance in the mine transportation channels. In addition, based on process requirements and specific requirements of material types, such as avoiding high-humidity routes for rare ore transportation, routes that meet quality requirements are further screened to ensure the continuity of material quality during transportation. Through the above technical solutions, fine-grained scheduling of mine transportation routes can be achieved, which not only improves transportation efficiency but also effectively prevents node overloading and congestion, optimizing the stability and intelligence level of the mine transportation system.

[0030] Further, in step S3, determining whether the continuity constraint is satisfied based on the set of transportation routes and process requirements to obtain a diversion decision includes: Obtain material quality matching requirements from the process requirements database; determine whether the set of transportation paths meets the continuity constraint based on the material quality matching requirements; if the continuity constraint is met, generate a diversion decision that meets the quality requirements; determine the overload detection threshold based on the processing capacity data of the key nodes, and generate load balancing adjustment parameters.

[0031] Specifically, direct contact between different materials in transport channels or carriages can lead to the mixing of particles, dust, or residues. When high-purity and low-purity ores are transported continuously along the same route, dust from the low-purity ores may adhere to the high-purity ores, causing an overall decrease in purity. Furthermore, after transporting one type of material, vehicles or channels often retain some material particles. If another material is transported immediately afterward, "cross-contamination" will occur, affecting the quality of subsequent batches. Therefore, based on the set of transport routes and process requirements, material quality matching requirements are obtained from the process requirement database. These matching requirements include continuity constraints for the transport of different material types during mining operations. For example, the transport of rare ores may require continuity of materials along the same route to prevent mixing of material types and subsequent quality degradation. The system evaluates the set of transport routes based on the material quality matching requirements to determine whether each route meets the continuity constraints. Specifically, the material flow direction and type sequence on each path are checked one by one to ensure that the order and type matching requirements of the corresponding material types of the transport vehicles on the same path are consistent, so as to avoid problems such as the mixed transport of high-purity ore and low-purity ore, thereby ensuring that the quality of the materials is not affected.

[0032] When the set of transportation routes meets the continuity constraint, a diversion decision that meets the quality requirements is generated, determining the material allocation ratio and route selection for each route. If the set of routes does not meet the continuity constraint, the quality requirement verification process is triggered, checking for material type deviations and making appropriate adjustments. To further optimize transportation scheduling, an overload detection threshold is set based on the real-time processing capacity data of key nodes, thereby providing support for the generation of subsequent scheduling instructions. Specifically, the load information of each key node is collected in real time through a sensor network, including the node's maximum processing capacity and current load status.

[0033] For example, in a mine transportation scenario, assuming a key node has a maximum processing capacity of 10 tons / hour and a current load of 9 tons, based on a set overload detection threshold (e.g., a threshold of 80%, or 8 tons), it is determined whether the node's load exceeds the threshold. If overloaded, load balancing adjustment parameters are generated, suggesting that some subsequent materials be diverted to alternative routes. This adjustment is based on the path congestion probability calculated by the A* path planning algorithm, prioritizing alternative routes with lower congestion probabilities, thereby effectively avoiding node overload and optimizing the efficiency of the mine transportation system.

[0034] The system also dynamically adjusts its approach based on actual mine depth, path congestion, and material type compatibility. For example, for narrower mine passages, the system may set the overload detection threshold to a lower value and optimize path selection based on material properties, such as the temperature and humidity requirements of rare ores. These optimizations not only ensure the continuity and quality requirements of material transportation but also reduce the risk of sudden congestion during mine transportation, thereby improving overall transportation efficiency. Through these technical solutions, a set of transportation paths that meet quality requirements and node processing capabilities can be generated. Furthermore, through optimization using genetic algorithms and A* algorithms, the selection of transportation paths becomes more intelligent and precise, thus achieving efficient and stable operation of the mine transportation system.

[0035] Step S4: If the routing decision shows that the node is overloaded, a path planning algorithm is used to reroute to an alternative path, generating an updated scheduling instruction; specifically including: Based on the diversion decision, it is determined whether any nodes exceed the overload detection threshold; if overloaded nodes exist, a path planning algorithm is used to recalculate alternative paths; based on the alternative paths, an updated set of transportation paths is generated; combined with the real-time load distribution and status indicators, the updated set of transportation paths is optimized, and a scheduling instruction is generated.

[0036] Specifically, based on the aforementioned diversion decision, the first step is to determine whether any nodes exceed the overload detection threshold. Specifically, real-time load data for each node is collected via a sensor network. This data includes vehicle location and equipment operating parameters. The load data for each node is compared with the preset overload detection threshold. If a node's load exceeds the threshold, it is marked as an overloaded node. Then, the alternative route is recalculated using the A* path planning algorithm. The A* path planning algorithm, as a heuristic search algorithm, selects a route by evaluating the lowest-cost path from the origin to the destination. Its calculation is based on the combination of the actual cost g(n) and the estimated cost h(n) of the path. g(n) is the actual travel cost from the origin to the current node, and h(n) is the estimated cost from the current node to the destination node. In the mine transportation scenario, the cost of a path includes path length and congestion probability; paths with higher congestion will be assigned higher costs, thus optimizing path selection.

[0037] Specifically, the A* path planning algorithm prioritizes paths with a congestion probability below a set threshold, such as 50%, during path selection. This ensures that the selected backup paths effectively avoid overloaded nodes, thereby reducing delays and congestion during transportation, maintaining transportation continuity, preventing material backlog due to node overload, and improving overall transportation efficiency. Furthermore, based on the optimization results of the A* path planning algorithm, an updated set of transportation paths is generated. This set includes new backup paths and verifies the material type matching requirements of each path to ensure that the continuity of transportation paths matches material quality requirements, avoiding cross-contamination or quality degradation. The updated set of transportation paths is further optimized using real-time load distribution and status indicators to ensure load balancing and improve transportation efficiency. Specifically, real-time load distribution data includes the current load status of each node and the congestion status of the path. Status indicators reflect equipment operating parameters and material types. This data helps to accurately optimize the load balancing of paths. By comprehensively analyzing the expected travel time and congestion probability of each path, paths with lower loads and higher efficiency can be prioritized, and scheduling instructions can be generated based on the optimized paths. The scheduling instructions include specific route selection and vehicle allocation information, ensuring vehicles travel along the optimal path, thereby effectively reducing the burden on overloaded nodes and improving overall transportation efficiency. When material type deviations occur deep within the mine, the system can respond quickly, reassessing the route set with updated data and generating corresponding scheduling instructions to ensure the continuity and stability of material transportation. Furthermore, it can prioritize routes based on material quality requirements, ensuring that high-value ores receive priority access to suitable transportation routes, preventing quality damage. This comprehensive route planning and optimization strategy enhances the intelligence level of mine transportation, optimizes the allocation of mine resources, effectively avoids node overloading and congestion during transportation, and ensures the efficient and stable operation of the mine transportation system.

[0038] Step S5: Based on the updated scheduling instructions, send navigation updates to the transport vehicles through the SCADA system to obtain the optimized transport efficiency index; based on the transport efficiency index, reacquire the updated data to determine whether further adjustments to the diversion decision are needed.

[0039] In step S5, the optimized transportation efficiency index is obtained, including: According to the scheduling instructions, real-time navigation updates are sent to the transport vehicles; according to the navigation updates, the vehicle travel routes are adjusted; according to the adjusted travel routes, the overall transport efficiency index is calculated; according to the overall transport efficiency index, the effectiveness of the backup route allocation is evaluated, and local anti-congestion balance parameters are generated.

[0040] Specifically, according to the aforementioned scheduling instructions, real-time navigation updates are sent to the transport vehicles, including backup route information calculated by the A* path planning algorithm. The process first transmits navigation data containing backup route information to the transport vehicles in the mine via a wireless sensor network. This data originates from the set of paths previously rerouted using the A* path planning algorithm. By receiving the path adjustment details contained in the scheduling instructions and encoding them into a transmittable format, the data is ensured to be broadcast to the transport vehicles in real time through communication nodes within the mine. This ensures that when critical nodes in the mine transport corridor are overloaded, the scheduling instructions will trigger the sending of navigation updates, avoiding path congestion and maintaining transport continuity.

[0041] Upon receiving a navigation update, transport vehicles adjust their routes according to the new instructions. Specifically, the vehicles extract alternative route options from the received navigation update, instructing them to switch to low-congestion routes to ensure transport efficiency is not affected by congestion and maintain the continuity of material flow through route adjustment. Based on the adjusted routes, the overall transport efficiency index is further calculated. This is achieved by collecting the vehicle's location and operating parameters after the adjustment, combined with route travel time and material throughput. Travel time is based on the predicted value of the A* route planning algorithm, while material throughput considers node processing capacity. By acquiring real-time vehicle speed and load data on the adjusted routes, a weighted average method is used to calculate the overall efficiency, for example, multiplying travel time by a congestion probability factor and adding it to the material type matching degree to obtain a comprehensive efficiency index. Then, the calculated efficiency index is compared with a preset benchmark to confirm whether an efficiency improvement has been achieved. For example, in a mine transportation scenario, if the processing capacity of a certain channel node is reduced and the travel time is decreased after path adjustment, the system evaluates that the efficiency has been improved, thereby reducing waiting time and optimizing resource allocation. In addition, when the probability of channel congestion is higher than the threshold, this calculation can show that the allocation of backup paths improves the overall efficiency, indicating the effectiveness of path adjustment and providing data support for subsequent congestion prevention.

[0042] Based on the calculated overall transportation efficiency index, the allocation effect of backup routes is further evaluated, and local anti-congestion balance parameters are generated. Specifically, the congestion probability and load distribution data in the efficiency index are analyzed to quantify the contribution rate of backup routes to the overall flow and generate balance parameters for subsequent cyclical adjustments. By extracting the path congestion assessment part of the efficiency index, the utilization rate of backup routes is calculated, and the allocation effect is evaluated in combination with the material flow direction optimized by the genetic algorithm. For example, if the backup route carries 30% of the flow, the generated balance parameter value is 0.8, indicating that local anti-congestion has reached equilibrium. The parameters are adjusted in combination with real-time status indicators to cope with sudden material type matching deviations and ensure that the adjusted parameters reflect the current anti-congestion status of the mine transportation channel.

[0043] This implementation method is not only applicable to the transportation of rare ores deep in mines, but also takes into account the differences in scenarios at different mine depths. For example, in a mine tunnel at a depth of 500 meters, the material type matching requirements are incorporated into the efficiency index calculation, and high-quality paths are prioritized to further optimize the anti-congestion balance. If the node processing capacity is 4 tons / hour, the evaluation after calculating the efficiency index shows that the allocation effect reduces congestion. The generated balance parameters support cyclical decision-making and enhance the system's adaptability to sudden material type deviations. This approach can improve transportation efficiency and reduce the risk of equipment failure, thereby improving the overall efficiency of mine transportation and ensuring the local anti-congestion balance of the transportation channel.

[0044] The above technical solutions enable refined management and optimization of mine transportation routes, which not only improves transportation efficiency but also ensures material quality control and transportation continuity during the transportation process, providing strong support for mine automation control systems.

[0045] Furthermore, in step S5, determining whether further adjustments to the diversion decision are needed includes: Based on the transportation efficiency index, vehicle locations and equipment operating parameters are re-collected through a sensor network; based on the re-collected data, real-time load distribution and status indicators are updated; based on the updated real-time load distribution, route congestion is re-assessed; based on the route congestion assessment results, it is determined whether the diversion decision needs to be adjusted, and a cyclically adjusted diversion plan is generated.

[0046] Specifically, based on the aforementioned transportation efficiency indicators, vehicle locations and equipment operating parameters are re-collected through a sensor network. This data is used to update real-time load distribution and status indicators. In particular, the sensor network is deployed in underground mine transportation channels and key nodes to capture vehicle locations, equipment operating status, and load information in real time. By combining the above data, the real-time load distribution is updated, the vehicle density and material load on the current path are calculated, and new status indicators, such as the path congestion assessment value, are generated.

[0047] Based on the updated real-time load distribution, a new path congestion assessment is performed. Specifically, the A* path planning algorithm is used to calculate the estimated travel time and congestion probability of each transportation path. During the calculation, the A* path planning algorithm uses the real-time load distribution as a weight input to evaluate the path cost from the node to the target node. The path cost includes the known path travel time and the congestion probability calculated based on the current load distribution, thereby identifying which paths have a congestion probability higher than a preset threshold and promptly discovering potential congestion points.

[0048] Based on the path congestion assessment results, it is determined whether the diversion decision needs to be adjusted. If the assessment results show that the congestion probability of some paths is higher than a preset threshold, the material flow direction will be optimized through a genetic algorithm to generate a new diversion scheme. The genetic algorithm iteratively optimizes the material flow direction allocation scheme through population initialization, selection, crossover, and mutation operations. During the process, the path congestion assessment results are used as the input of the fitness function. Each material flow direction allocation scheme represents the material allocation ratio of different paths. The algorithm optimizes these allocation ratios through multiple generations of iteration until a diversion scheme with higher fitness is obtained, ensuring that path congestion is effectively controlled.

[0049] After optimization by the genetic algorithm, the generated diversion scheme will be combined with the material quality matching requirements in the process requirement database to ensure that the matching of material types will not lead to cross-contamination or quality degradation. In addition, data will be collected and updated regularly to form a cyclical adjustment mechanism. Through the above technical solutions, the transportation route can be continuously optimized according to real-time load distribution, path congestion assessment and material quality requirements to ensure load balance and material continuity in the mining transportation process. In turn, transportation efficiency can be continuously monitored and optimized in the underground mine transportation process to avoid congestion and overloading, improve transportation efficiency and ensure the continuity and stability of ore quality.

[0050] Furthermore, in step S5, determining whether the continuity constraint is satisfied to obtain the diversion decision also includes: Based on the set of transportation routes, material type matching rules are obtained from the process requirements database; based on the material type matching rules, the continuity constraints of the set of transportation routes are verified; if the continuity constraints are not met, the material flow allocation is adjusted; based on the adjusted material flow allocation, a diversion decision that meets the quality requirements is generated.

[0051] Specifically, based on the set of transportation routes, material type matching rules are first obtained from the process requirements database. These rules define the allowed continuous transportation sequences between different material types, ensuring that the material types on each route in the mine transportation can remain consistent according to process requirements, preventing quality degradation caused by alternating material types during transportation. By querying the material type identifiers of each route in the transportation route set, the corresponding matching rules are extracted from the database, and it is verified whether the transportation route set conforms to these continuity constraints. Specifically, each route in the route set is traversed, and the material types between adjacent nodes are compared chain by chain to check whether they conform to the continuous transportation sequence defined by the matching rules. For example, in the transportation of rare ore, it is necessary to ensure that the rare ore route does not intersect with the ordinary ore route to avoid mixed transportation causing quality contamination. If a material type mismatch is found in the route sequence, the route is marked as a constraint violation, and the distribution density of the violation nodes is calculated to provide data support for subsequent route adjustments.

[0052] If the continuity constraint is not met during the verification process, the material flow allocation will be adjusted to ensure the continuity of the path and the matching of material types. To this end, a genetic algorithm is used to optimize the material flow allocation scheme. Based on the principles of natural selection and genetics, the genetic algorithm finds the optimal solution by simulating the evolutionary process of a population. In this process, multiple material allocation schemes are first initialized as a population, each scheme representing the material allocation ratio for different paths. The fitness of each scheme is calculated; the fitness function comprehensively considers the path congestion probability and the material continuity score. Schemes with lower congestion probabilities and higher material type matching have higher fitness. Subsequently, through selection, crossover, and mutation operations, the material flow allocation scheme is iteratively optimized until the best allocation scheme is found, ensuring that all paths meet the continuity constraint while optimizing path selection to avoid material cross-contamination or quality degradation.

[0053] By optimizing the path set using a genetic algorithm and combining it with real-time load distribution data, the system reassesses the load situation of each path and generates diversion decisions that meet quality requirements. This process not only ensures the continuity of materials but also avoids node overload issues. During the adjustment process, the system also dynamically adjusts the material flow direction based on real-time data, such as node processing capacity and path congestion, generating diversion decision instructions. These instructions include confirmation of the path selection and material type matching for each transport vehicle, ensuring that the material type remains consistent across all paths during transportation and meets process requirements. Through the above technical solution, a complete diversion scheme is generated based on the collected real-time load data and path congestion assessment results, and each path in the mine transportation process is dynamically adjusted. Through the optimization of the genetic algorithm and the support of real-time data, the system can quickly respond to sudden changes in the mine transportation process, ensuring that the efficiency of mine transportation and the quality of materials are not affected. This effectively improves the continuity and efficiency of mine transportation, reduces transportation delays caused by node overload or material type mismatch, and further optimizes the overall performance of the mine automation control system.

[0054] This invention also provides a SCADA-based mine automation control system for implementing the above-mentioned method, such as... Figure 2 As shown, the system includes: The data acquisition unit is used to obtain vehicle location, equipment operating parameters and material type data from transportation channels and key nodes through sensor networks, so as to obtain the real-time load distribution and status indicators of each channel in the mine; The route planning unit is used to calculate the travel time and congestion probability of each transportation route in the mine based on the real-time load distribution and status indicators, and to generate preliminary diversion options. The diversion optimization unit is used to adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, it determines whether the continuity constraint is met, and if so, generates a diversion decision that meets the process requirements. The path routing unit is used to reroute to an alternative path using a path planning algorithm and generate an updated scheduling instruction if the diversion decision display node is overloaded. The navigation update unit is used to send navigation updates to transport vehicles through the SCADA system according to the updated scheduling instructions to obtain optimized transport efficiency indicators; and to reacquire updated data based on the transport efficiency indicators to determine whether further adjustments to the diversion decision are needed.

[0055] In summary, this invention uses a sensor network to collect real-time data on vehicle locations, equipment operating parameters, and material types at key nodes along mine transportation routes. The system can acquire real-time load distribution and status indicators for each mine route, providing a foundation for subsequent route planning and load balancing. A route planning algorithm is employed to assess the travel time of each route based on real-time load distribution and route congestion probability, generating initial diversion options to ensure that route selection considers both load allocation and material quality requirements. The initial diversion options are further adjusted through an optimization algorithm to ensure that each route meets the requirements of material quality and node processing capacity. Verification of route continuity constraints avoids quality degradation caused by mixed material transport, ensuring material quality continuity. If route congestion or node overload is detected, the system reroutes to an alternative route using the A* algorithm and generates new scheduling instructions. Navigation updates are transmitted to the transport vehicles via a wireless sensor network, ensuring that vehicles can adjust their routes in real time. To further improve system efficiency, transportation efficiency indicators are evaluated. The system dynamically adjusts the routes, and real-time data collection enables it to calculate the transportation efficiency of each route. A genetic algorithm optimizes material flow, ensuring balanced load at nodes and preventing overloading. The genetic algorithm iteratively optimizes material distribution schemes, using path congestion probability and continuity score as fitness functions to generate optimal diversion decisions. Through the combined use of these technologies, the system not only dynamically adjusts mine transportation routes based on real-time data to avoid congestion and overloading and optimize transportation efficiency, but also ensures the continuity and quality requirements of material transportation. This achieves load balancing during mine transportation, significantly improves the intelligence and automation level of mine transportation, reduces the impact of emergencies on the transportation system, and thus enhances overall transportation efficiency and stability.

[0056] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A mine automation control method based on SCADA, characterized in that, The method includes: Step S1: Obtain vehicle location, equipment operating parameters, and material type data from transportation channels and key nodes through a sensor network to obtain real-time load distribution and status indicators for each channel in the mine; Step S2: Based on the real-time load distribution and status indicators, use a path planning algorithm to calculate the travel time and congestion probability of each transportation path in the mine, and generate preliminary diversion options; Step S3: Adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, determine whether the continuity constraint is met; if it is met, generate a diversion decision that meets the process requirements. Step S4: If the routing decision display node is overloaded, a path planning algorithm is used to reroute to an alternative path and generate an updated scheduling instruction; Step S5: Based on the updated scheduling instructions, send navigation updates to the transport vehicles through the SCADA system to obtain the optimized transport efficiency index; based on the transport efficiency index, reacquire the updated data to determine whether further adjustments to the diversion decision are needed.

2. The method as described in claim 1, characterized in that, Step S1 includes: By collecting real-time vehicle location data, speed data, and equipment operating parameters in the transportation corridor through a sensor network, the real-time load distribution of each corridor is determined; based on the vehicle location data and speed data, path congestion assessment indicators are calculated; through the equipment operating parameters and material type data, status indicators are generated; and based on the path congestion assessment indicators and status indicators, a real-time load distribution model is constructed.

3. The method as described in claim 1, characterized in that, Step S2 includes: Based on the real-time load distribution of the mine transportation corridor, the estimated travel time of each transportation route is calculated, and the congestion probability of each route is determined based on the status indicators of the transportation corridor. Based on the estimated travel time and congestion probability, multiple potential diversion options are generated; for these potential diversion options, material quality requirement verification is incorporated, and preliminary diversion options that meet the quality requirements are selected.

4. The method as described in claim 1, characterized in that, In step S3, a set of transportation routes that meet the quality requirements and node processing capabilities is obtained, including: If any of the initial diversion options has a congestion probability higher than a preset threshold, a genetic algorithm is used to optimize the material flow allocation scheme. Based on the optimization results of the genetic algorithm, an adjusted set of transportation paths is generated. Combining the processing capacity data of key nodes, the load distribution balance of the transportation path set is evaluated. Based on the load distribution balance, preliminary adjustment parameters are generated for subsequent continuity constraint checks.

5. The method as described in claim 4, characterized in that, In step S3, determining whether the continuity constraint is met based on the set of transportation routes and process requirements to obtain a diversion decision includes: Obtain material quality matching requirements from the process requirements database; determine whether the set of transportation paths meets the continuity constraint based on the material quality matching requirements; if the continuity constraint is met, generate a diversion decision that meets the quality requirements; determine the overload detection threshold based on the processing capacity data of the key nodes, and generate load balancing adjustment parameters for subsequent scheduling instruction generation.

6. The method as described in claim 1, characterized in that, Step S4 includes: Based on the diversion decision, it is determined whether any nodes exceed the overload detection threshold; if overloaded nodes exist, a path planning algorithm is used to recalculate alternative paths; based on the alternative paths, an updated set of transportation paths is generated; combined with the real-time load distribution and status indicators, the updated set of transportation paths is optimized, and a scheduling instruction is generated.

7. The method as described in claim 1, characterized in that, In step S5, the optimized transportation efficiency index is obtained, including: According to the scheduling instructions, real-time navigation updates are sent to the transport vehicles; according to the navigation updates, the vehicle travel routes are adjusted; according to the adjusted travel routes, the overall transport efficiency index is calculated; according to the overall transport efficiency index, the effectiveness of the backup route allocation is evaluated, and local anti-congestion balance parameters are generated.

8. The method as described in claim 1, characterized in that, In step S5, it is determined whether further adjustments to the diversion decision are needed, including: Based on the transportation efficiency index, vehicle locations and equipment operating parameters are re-collected through a sensor network; based on the re-collected data, real-time load distribution and status indicators are updated; based on the updated real-time load distribution, route congestion is re-assessed; based on the route congestion assessment results, it is determined whether the diversion decision needs to be adjusted, and a cyclically adjusted diversion plan is generated.

9. The method as described in claim 1, characterized in that, Step S5, determining whether the continuity constraint is satisfied to obtain the diversion decision, also includes: Based on the set of transportation routes, material type matching rules are obtained from the process requirements database; based on the material type matching rules, the continuity constraints of the set of transportation routes are verified; if the continuity constraints are not met, the material flow allocation is adjusted; based on the adjusted material flow allocation, a diversion decision that meets the quality requirements is generated.

10. A SCADA-based mine automation control system for implementing the method as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition unit is used to obtain vehicle location, equipment operating parameters and material type data from transportation channels and key nodes through sensor networks, so as to obtain the real-time load distribution and status indicators of each channel in the mine; The route planning unit is used to calculate the travel time and congestion probability of each transportation route in the mine based on the real-time load distribution and status indicators, and to generate preliminary diversion options. The diversion optimization unit is used to adjust the initial diversion options through optimization algorithms to obtain a set of transportation paths that meet the material quality requirements and node processing capabilities; based on the set of transportation paths and process requirements, it determines whether the continuity constraint is met, and if so, generates a diversion decision that meets the process requirements. The path routing unit is used to reroute to an alternative path using a path planning algorithm and generate an updated scheduling instruction if the diversion decision display node is overloaded. The navigation update unit is used to send navigation updates to transport vehicles through the SCADA system according to the updated scheduling instructions to obtain optimized transport efficiency indicators; and to reacquire updated data based on the transport efficiency indicators to determine whether further adjustments to the diversion decision are needed.

Images