Mine asset collaborative management and control method and system based on digital twinning

Abstract

This invention relates to the field of management and scheduling technology, and discloses a method and system for collaborative management and control of mining assets based on digital twins. The method includes: acquiring physical assets and digital assets of the target site, simultaneously acquiring physical data of the physical assets and communication data of the digital assets in real time, associating the physical assets and digital assets to obtain a vehicle cost association management index for associating physical assets and corresponding digital assets; this solution, through the step-by-step calculation of the vehicle cost association management index, single asset load management degree, asset management coefficient, material priority scheduling sequence and collaborative management adaptability, can dynamically match the data upload requirements of physical assets with the computing power and bandwidth resources of digital assets, thereby reducing the risk of abnormal server load and ensuring collaborative management and control of mining assets.

Description

Technical Field

[0001] This invention relates to the field of management and scheduling technology, specifically to a method and system for collaborative management and control of mining assets based on digital twins. Background Technology

[0002] Currently, asset management in mines typically involves two types of assets: physical assets, such as mining equipment and transport vehicles, and digital IT assets, such as servers and network equipment. Physical assets and digital IT assets are usually managed separately and are independent of each other.

[0003] The above-mentioned control methods still have the following drawbacks: For example, for vehicles in physical assets, vehicle-related data such as location, load, and speed are all uploaded to the digital asset server. When multiple vehicles are concentrated, the data is also uploaded in a concentrated manner, which can easily cause abnormal server load. It is impossible to dynamically adapt the vehicle data upload demand to the server resource supply, which reduces the efficiency of resource scheduling and also reduces the effect of collaborative asset management. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for collaborative management and control of mining assets based on digital twins, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] Digital twin-based collaborative management and control methods for mining assets include:

[0007] Step S1: Obtain the physical assets and digital assets of the target location, and simultaneously obtain the physical data of the physical assets and the communication data of the digital assets in real time. Associate the physical assets and digital assets to obtain a fare association management index for associating physical assets and corresponding digital assets.

[0008] Step S2: Calculate the physical data and vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load.

[0009] Step S3: Combine and analyze the communication data with the single asset load management degree to generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets.

[0010] Step S4: Dynamically sort and manage physical assets according to asset management coefficients to obtain asset priority scheduling sequence, and allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence to generate collaborative management adaptability degree representing the degree of matching between physical asset data upload demand and digital asset resource supply.

[0011] Step S5: Analyze the compatibility of collaborative management to obtain the collaborative control identifier.

[0012] Furthermore, by associating physical assets with digital assets, a fare association management index is obtained to link physical assets with their corresponding digital assets, including:

[0013] The physical data is analyzed to determine the intensity of the irregularity in the movement of each vehicle, and a dynamic tendency value is generated to indicate the impact of the physical asset on the sudden upload of the digital asset.

[0014] For each digital asset, the CPU utilization and memory usage in its communication data are analyzed and calculated in conjunction with dynamic tendency values ​​to obtain a vehicle fee association management index for associating physical assets with corresponding digital assets.

[0015] Furthermore, the physical data and vehicle fare association management index are calculated to obtain the single asset load management degree, which represents the degree of impact of physical asset data uploads on digital asset load, including:

[0016] Based on physical data, the vehicle trajectory is mapped and decomposed to generate sudden disturbance values ​​that represent the intensity of interference to the network data stream during vehicle movement.

[0017] Based on the vehicle cost association management index and communication data, the changes in CPU utilization and memory usage are calculated to obtain the load impact inertia that reflects the current vehicle impact force.

[0018] Furthermore, the physical data and vehicle fare association management index are calculated to obtain the single asset load management degree, which represents the degree of impact of physical asset data uploads on digital asset load. This also includes:

[0019] Based on the sudden disturbance value, load impact inertia, and vehicle speed in the physical data, the interaction between the vehicle's current motion behavior and digital assets is analyzed to generate a single asset load management degree that represents the degree of impact of physical asset data upload on digital asset load.

[0020] Furthermore, by combining communication data with single-asset load management analysis, an asset management coefficient is generated representing the probability that centralized data uploads from physical assets will lead to abnormal digital asset loads, including:

[0021] Load analysis is performed on CPU utilization, memory usage, and data transmission latency in the communication data to generate load ripple values ​​that represent the complex state of the server.

[0022] Furthermore, by combining communication data with single-asset load management analysis, an asset management coefficient is generated representing the probability that centralized data uploads from physical assets will cause abnormal loads on digital assets. This also includes:

[0023] Based on the single asset load management degree, density analysis is performed on the location of all vehicles in the target area to generate a contribution clustering degree that represents the impact of data upload when vehicles are concentrated.

[0024] By fusing the load ripple value and contribution clustering, an asset management coefficient is generated that represents the probability of abnormal load on digital assets caused by centralized data uploads from physical assets.

[0025] Furthermore, physical assets are dynamically sorted and managed based on asset management coefficients to obtain a priority scheduling sequence. The computing power and bandwidth resources of corresponding digital assets are then allocated according to this sequence, generating a collaborative management fit degree that represents the degree of matching between physical asset data upload demands and digital asset resource supply. This includes:

[0026] The data upload interval for each vehicle is obtained, and the data upload interval, vehicle speed, and load are analyzed. Combined with the asset management coefficient, the scheduling impact urgency, which represents the potential impact intensity of the vehicle's delayed scheduling, is calculated.

[0027] By analyzing the urgency of scheduling impacts on all vehicles, a priority scheduling sequence for balancing physical and digital assets is obtained.

[0028] Furthermore, physical assets are dynamically sorted and managed based on asset management coefficients to obtain a priority scheduling sequence. The computing power and bandwidth resources of corresponding digital assets are then allocated according to this sequence. This generates a collaborative management fit degree, representing the degree of matching between physical asset data upload demands and digital asset resource supply. This also includes:

[0029] The remaining CPU core frequency and bandwidth capacity of digital assets are obtained in real time, and the remaining CPU core frequency and bandwidth capacity are matched with the asset priority scheduling sequence to obtain a resource matching value that represents the degree of matching between digital assets and vehicle demand.

[0030] The scheduling analysis is performed on the priority scheduling sequence of physical assets and the resource matching value to generate a collaborative management fit degree that represents the degree of matching between the demand for physical asset data upload and the supply of digital asset resources.

[0031] Furthermore, an analysis of the compatibility with collaborative management was conducted to obtain collaborative control identifiers, including:

[0032] The compatibility of collaborative management is calculated to obtain the collaborative fluctuation tolerance.

[0033] By comparing the collaborative fluctuation tolerance with the collaborative management adaptability, a collaborative control identifier is obtained.

[0034] Furthermore, a collaborative management and control system for mining assets based on digital twins, applied to the aforementioned management and control methods, includes:

[0035] The association analysis unit is used to acquire the physical and digital assets of the target location, and simultaneously acquire the physical data of the physical assets and the communication data of the digital assets in real time. It associates the physical and digital assets to obtain a fare association management index for associating physical assets with corresponding digital assets.

[0036] The load analysis unit is used to calculate the physical data and the vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load.

[0037] The management analysis unit is used to combine communication data with single asset load management for analysis, and generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets.

[0038] The collaborative adaptation unit is used to dynamically sort and manage physical assets according to asset management coefficients, obtain asset priority scheduling sequence, allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence, and generate a collaborative management adaptation degree that represents the degree of matching between physical asset data upload demand and digital asset resource supply.

[0039] The collaborative management unit is used to analyze the adaptability of collaborative management and obtain collaborative management identifiers.

[0040] In summary, the present invention has the following main beneficial effects:

[0041] By performing correlation analysis on physical and communication data, a vehicle-to-database (VTC) correlation management index is generated. This index accurately reflects sudden data upload shocks caused by irregular vehicle movement and calculates the single-asset load management degree. Combining this with sudden disturbance values ​​and load impact inertia analysis, the complex impact of vehicles on server load can be reflected. The resulting asset management coefficient is mainly used to dynamically assess the probability of server load anomalies and generate a priority scheduling sequence for assets based on the urgency of scheduling impacts. Simultaneously, resource matching values ​​are calculated by combining remaining CPU core frequency and bandwidth capacity, ultimately yielding a collaborative management fit degree. The closer the management fit degree is to 1, the higher the supply and demand coordination. The management fit degree is then compared with the collaborative fluctuation tolerance to generate a collaborative control identifier. This solution can match vehicle data upload requirements with server computing power and bandwidth resources in real time, reducing the risk of abnormal server load and improving the level of asset collaborative management. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the collaborative management and control method for mining assets based on digital twins according to the present invention.

[0043] Figure 2 This is a schematic diagram of the collaborative management and control system for mining assets based on digital twins, as described in this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] refer to Figure 1 and Figure 2 A collaborative management and control method for mining assets based on digital twins includes:

[0046] Step S1: Obtain the physical assets and digital assets of the target location, and simultaneously obtain the physical data of the physical assets and the communication data of the digital assets in real time. Associate the physical assets and digital assets to obtain a fare association management index for associating physical assets and corresponding digital assets.

[0047] The target location is a mine, the physical assets are vehicles used for transporting goods in the mine, and the digital assets are servers.

[0048] Physical data includes: vehicle location, load, and speed;

[0049] Communication data includes: CPU utilization, memory usage, and data transmission latency, etc.

[0050] Step S2: Calculate the physical data and vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load.

[0051] Step S3: Combine and analyze the communication data with the single asset load management degree to generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets.

[0052] Step S4: Dynamically sort and manage physical assets according to asset management coefficients to obtain asset priority scheduling sequence, and allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence to generate collaborative management adaptability degree representing the degree of matching between physical asset data upload demand and digital asset resource supply.

[0053] Step S5: Analyze the compatibility of collaborative management to obtain the collaborative control identifier.

[0054] In one embodiment, physical assets and digital assets are associated to obtain a fare association management index for associating physical assets and corresponding digital assets, including:

[0055] The physical data is analyzed to determine the intensity of the irregularity of each vehicle's movement. A dynamic tendency value is generated to indicate the physical asset's potential to cause a sudden upload impact on the digital asset. Specifically, the vehicle speed is continuously collected at a fixed sampling interval of 0.5 seconds. For each adjacent sampling point, the absolute value of the speed difference is calculated to obtain the speed modulus change. The speed modulus change is divided by the sampling interval, and the calculation result is normalized to the 0-1 range to obtain the speed change rate. Within a 10-second time window, the standard deviation of the absolute values ​​of all speed change rates is calculated to obtain the instantaneous impact fluctuation index.

[0056] Simultaneously, within the time window, the sampled vehicle speeds are arranged in the sampling order to obtain a vehicle speed sequence. The ratio of the standard deviation to the mean of the vehicle speed sequence is calculated to obtain the vehicle speed variation coefficient. The instantaneous impact fluctuation index is multiplied by the vehicle speed variation coefficient, and the calculation result is normalized to the 0-1 range to obtain a dynamic tendency value that indicates that the physical asset causes a sudden upload impact on the digital asset. The larger the dynamic tendency value, the more irregular the vehicle speed fluctuation, and the more likely the data upload is to cause a sudden impact on the digital asset.

[0057] For each digital asset, the CPU utilization and memory usage in its communication data are analyzed and calculated in conjunction with the dynamic tendency value to obtain the vehicle fare association management index used to associate physical assets with corresponding digital assets. Specifically, the CPU utilization and memory usage in the communication data are both between 0 and 1 and are dimensionless values. For each vehicle, its dynamic tendency value is directly multiplied by the server's CPU utilization and memory usage to obtain the vehicle fare association management index used to associate physical assets with corresponding digital assets. The vehicle fare association management is mainly used to reflect the potential load coordination association between vehicles and servers.

[0058] Analyzing vehicle movement data in physical assets generates dynamic tendency values, which accurately reflect the risk of sudden data upload surges caused by irregular speed fluctuations in each vehicle. This allows for the prediction of precursory characteristics of abnormal server load. By combining the dynamic tendency values ​​with the CPU utilization and memory usage of servers in digital assets, the resulting vehicle-data association management index can intuitively reflect the load coordination correlation between vehicles and servers. When multiple vehicles concentrate to cause data upload peaks, this index can dynamically identify the matching relationship between vehicles with high impact tendency and high-load servers, thereby guiding resource scheduling strategies. For example, it can prioritize allocating low-load servers to vehicles with high dynamic tendency values, or adjust the upload timing of vehicles with low impact tendency. This effectively avoids sudden impacts of physical asset data uploads on digital assets, achieving adaptive matching between vehicle data upload demand and server resource supply, and improving the resource scheduling efficiency of mine asset collaborative management.

[0059] In one embodiment, the physical data and the vehicle fare association management index are calculated to obtain a single asset load management degree, representing the degree of impact of physical asset data upload on digital asset load, including:

[0060] Based on physical data, the vehicle trajectory is mapped and decomposed to generate sudden disturbance values ​​that represent the intensity of interference to the network data stream during vehicle movement. Specifically, the three-dimensional position coordinates and load of the vehicle are continuously collected at 1-second intervals, with the load in tons. For each set of three adjacent consecutive position coordinate points, the radius of the circumcircle formed by the three points is first calculated, and the reciprocal of the circumcircle radius is normalized to the 0-1 interval to obtain the instantaneous curvature of the local path.

[0061] Obtain the vehicle's rated load, divide its actual load by the rated load to get the load ratio, multiply the instantaneous curvature by the load ratio to get the mapped trajectory feature value, arrange multiple trajectory feature values ​​according to the sampling interval to form a trajectory feature sequence, and the length of the trajectory feature sequence is the most recent 60 seconds;

[0062] The trajectory feature sequence is first-order differencing is performed to obtain the difference sequence. The sliding window method is used with a window width of 5 seconds and a sliding step of 1 second. The sum of the absolute values ​​of the difference sequence within each window is calculated to obtain the local fluctuation energy value. All local fluctuation energy values ​​are arranged in order to form a local fluctuation energy value sequence.

[0063] Calculate the arithmetic mean and standard deviation of the local fluctuation energy sequence. Set the upper threshold as the arithmetic mean plus the standard deviation and the lower threshold as the arithmetic mean minus the standard deviation. Traverse the local fluctuation energy sequence and compare each value with the upper and lower thresholds one by one. All points that are greater than or equal to the upper threshold or less than or equal to the lower threshold are regarded as abnormal fluctuation points.

[0064] The abnormal fluctuation points are arranged in chronological order. If the time interval between two adjacent points is 1 second, the two points are determined to belong to the same continuous abnormal segment. If the interval is greater than 1 second, it is determined to be the beginning of a new segment. At the same time, the minimum duration of a continuous abnormal segment is 2 seconds. A single abnormal point with a duration of less than 2 seconds is not counted in the continuous abnormal segment. In this way, several continuous abnormal segments are obtained, and each segment contains at least one abnormal fluctuation point.

[0065] For each consecutive anomalous segment, calculate the duration of the segment and the arithmetic mean of all local fluctuation energy values ​​within the segment. Multiply the duration of the segment by its average energy and normalize the result to the 0-1 interval to obtain the disturbance contribution value of the segment. Calculate the arithmetic mean of the disturbance contribution values ​​of all consecutive anomalous segments, which represents the sudden disturbance value that causes interference to the network data stream during vehicle movement. The larger the sudden disturbance value, the stronger the interference to the network data stream caused by the curvature of the vehicle trajectory and the coupling of the load.

[0066] Based on the vehicle fare association management index and communication data, the changes in CPU utilization and memory occupancy are calculated to obtain the load impact inertia reflecting the current vehicle impact force. Specifically, for each digital asset, the CPU utilization sequence and memory occupancy sequence are continuously collected at second intervals. For the current moment, the absolute value of the difference between the CPU utilization of the previous second and the current second is calculated to obtain the CPU change. At the same time, the absolute value of the difference between the memory occupancy of the previous second and the current second is calculated to obtain the memory change. The CPU change and the memory change are added together to obtain the total load change rate.

[0067] Multiply the vehicle cost association management index corresponding to the physical asset vehicles currently associated with the server by the total load change rate to obtain the instantaneous impact factor;

[0068] In the mining production environment, both vehicle driving status and server load experience random fluctuations on a second-by-second basis. For example, a vehicle's speed may change instantaneously due to minor road bumps, causing fluctuations in the size of uploaded data packets. This can generate noise in the server's CPU utilization within a single second. If the instantaneous impact factor per second is used directly for resource scheduling, it will lead to frequent adjustments and reduced stability. Therefore, the instantaneous impact factor of the most recent 10 consecutive seconds is arithmetically averaged, and the result is normalized to the 0-1 range. This yields the load impact inertia that reflects the impact force of the current vehicle. The larger the load impact inertia, the stronger the load impact force of the data uploaded by the vehicle on the server, and the more drastic the changes in server load.

[0069] In one embodiment, the calculation of the physical data and the vehicle fare association management index to obtain a single asset load management degree, representing the degree of impact of physical asset data upload on digital asset load, further includes:

[0070] Based on the sudden disturbance value, load impact inertia, and vehicle speed in the physical data, the interaction between the vehicle's current motion behavior and digital assets is analyzed to generate a single asset load management degree that represents the degree of impact of physical asset data upload on digital asset load. Specifically, after normalizing the vehicle speed to the 0-1 range, the square root of the sum of the squares of the normalized vehicle speed, sudden disturbance value, and load impact inertia is calculated to obtain the comprehensive modulus, which is mainly used to reflect the overall strength of the three indicators in the numerical space. The three normalized values ​​are sorted from smallest to largest, the median of the middle position is taken, the absolute value of the difference between each value and the median is calculated, and the three absolute values ​​are added together to obtain the total absolute deviation.

[0071] Multiplying the overall module length by the total absolute deviation and normalizing the calculation result to the 0-1 range yields the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load. The higher the single asset load management degree, the higher the dispersion of vehicle motion irregularity, impact force and current vehicle speed, and the more complex and significant the impact on digital asset load.

[0072] Based on the vehicle's three-dimensional position coordinates and load, the instantaneous curvature and load ratio are calculated to generate a trajectory feature sequence and local fluctuation energy value. Continuous abnormal segments are then extracted and accumulated to obtain sudden disturbance values. This effectively captures the network data stream interference intensity caused by the coupling of vehicle path curvature and load. Simultaneously, by combining the vehicle cost association management index with changes in CPU utilization and memory usage in communication data, the total load change rate and instantaneous impact factor are calculated, and the load impact inertia is obtained. This smooths random jitter and stably reflects the impact force of vehicles on the server. Finally, the single-asset load management degree is obtained. Based on the single-asset load management degree, high-impact vehicles can be dynamically identified, facilitating adjustments to data upload priorities or server resource allocation. This enables adaptive matching between physical asset data upload needs and digital asset resource supply, improving resource scheduling efficiency and asset collaborative management effectiveness.

[0073] In one embodiment, communication data is analyzed in conjunction with single asset load management to generate an asset management coefficient representing the probability that centralized data uploads from physical assets will cause abnormal digital asset loads, including:

[0074] Load analysis is performed on CPU utilization, memory usage, and data transmission latency in communication data to generate load ripple values ​​representing the complex state of the server. Specifically, for each digital asset, i.e., the server, CPU utilization, memory usage, and data transmission latency are continuously collected at intervals of one second. First, a sliding window with a length of 10 seconds is constructed, and the maximum value of data transmission latency within the window is found. The current latency is divided by the maximum value to obtain the normalized latency. Then, the harmonic mean of the current CPU utilization and memory usage is calculated, which is twice the product of the two numbers divided by the sum of the two numbers.

[0075] Simultaneously, the absolute difference between CPU utilization and memory usage is calculated. The absolute difference is divided by the sum of CPU utilization and memory usage to obtain the relative difference coefficient. The normalized latency is multiplied by the harmonic mean, then multiplied by the relative difference coefficient, and the calculation result is normalized to the 0-1 range to represent the load ripple value of the server in a complex state. The larger the load ripple value, the stronger the imbalance between the current CPU and memory load of the server, the relatively high data transmission latency, and the more complex the overall load fluctuation.

[0076] In one embodiment, the analysis of communication data combined with single asset load management results in the generation of an asset management coefficient representing the probability of abnormal digital asset load caused by centralized data uploads from physical assets. This also includes:

[0077] Based on the single asset load management degree, density analysis is performed on the location of all vehicles in the target area to generate a contribution clustering degree that represents the impact of data upload when vehicles are concentrated. Specifically, this includes: calculating the Euclidean distance between each pair of all vehicles and finding the minimum Euclidean distance as R. ;

[0078] In the formula, The contribution of Q to the impact of data uploads when vehicles are concentrated indicates the degree of clustering. The larger the Q value, the more likely the vehicles are clustered in space with the smallest spacing scale and have a high degree of single-asset load management, which leads to a superimposed and enhanced impact effect of data uploads. This indicates the total number of vehicles within the target location. and All are vehicle number indexes. This is the index of the vehicle numbers currently being counted. For the number index of the other vehicles being compared, excluding itself. Indicates vehicle With vehicles The Euclidean distance between them Indicates vehicle Single asset load management level, express and At this point, only those items that are not themselves and are related to the vehicle are counted. The remaining vehicles whose spacing is not greater than the global minimum distance R.

[0079] The load ripple value and contribution clustering are fused to generate an asset management coefficient representing the probability that centralized data uploads from physical assets will cause abnormal loads on digital assets. Specifically, this involves multiplying the product of the contribution clustering value (normalized to the 0-1 range) and the load ripple value by 2, dividing by the sum of the two, and normalizing the result to the 0-1 range to obtain a harmonic mean. This harmonic mean is directly used as the asset management coefficient representing the probability that centralized data uploads from physical assets will cause abnormal loads on digital assets. The asset management coefficient represents the probability estimate that centralized data uploads from physical assets will cause abnormal loads on digital assets. The larger the asset management coefficient, the more complex the current load state of the server and the higher the spatial clustering of vehicles. The greater the probability that the combined effect of these two factors will cause abnormal loads on the server, the higher the asset management coefficient will be. Each vehicle obtains its corresponding asset management coefficient based on the server to which its data is uploaded.

[0080] The CPU utilization, memory usage, and data transmission latency of each server are analyzed to obtain the load ripple value. The larger the load ripple value, the stronger the imbalance between CPU and memory load and the higher the latency, which accurately reflects the complex state of the server. The contribution aggregation degree and asset management coefficient are also calculated, which directly reflect the probability of server load anomalies. This enables adaptive matching between physical asset data upload requirements and digital asset resource supply, reduces the probability of load anomalies, and improves the stability of asset collaborative management and resource scheduling efficiency.

[0081] In one embodiment, physical assets are dynamically sorted and managed according to asset management coefficients to obtain a priority scheduling sequence. The computing power and bandwidth resources of the corresponding digital assets are then allocated according to this priority scheduling sequence. A collaborative management fit degree, representing the degree of matching between physical asset data upload demands and digital asset resource supply, is generated, including:

[0082] The data upload interval of each vehicle is obtained, and the data upload interval, vehicle speed, and load are analyzed. Combined with the asset management coefficient, the scheduling impact urgency, which represents the potential impact intensity of the vehicle's delayed scheduling, is calculated. Specifically, for each vehicle, its data upload interval, vehicle speed, load ratio, and the asset management coefficient of the server to which the vehicle belongs are ranked separately: the shorter the data upload interval, the higher the vehicle speed, the higher the load ratio and the higher the asset management coefficient, the higher the vehicle ranks. This gives each vehicle four independent ranking numbers.

[0083] Then calculate the sum of the absolute differences between the vehicle's asset management coefficient ranking and the other three rankings. This sum reflects the consistency of the vehicle's emergency dispatch characteristics. The smaller the sum, the more consistent the vehicle's short upload interval, high speed, high load ratio and its high asset management coefficient are, and the more it should be prioritized for processing.

[0084] The total sum of all vehicles is calculated, and the minimum and maximum values ​​are found. The maximum value is subtracted from the total sum of the current vehicle, and then divided by the difference between the maximum and minimum values ​​to obtain the coordination degree. The coordination degree is then subtracted from 1 to obtain the incoordination factor. At the same time, the asset management coefficient ranking is divided by the total number of vehicles to obtain the asset priority. The incoordination factor is multiplied by the asset priority to obtain the scheduling impact urgency, which represents the potential impact intensity of the vehicle's delayed scheduling. The higher the scheduling impact urgency, the stronger the impact that delayed scheduling of the vehicle may bring.

[0085] Analyze the scheduling urgency of all vehicles to obtain a priority scheduling sequence that balances physical and digital assets. Specifically, this includes: calculating the median of the scheduling urgency of all vehicles. The median is the value in the middle after sorting all urgency values ​​from smallest to largest. If the total number of vehicles is even, take the arithmetic mean of the two middle values.

[0086] All vehicles are divided into two groups: those with a scheduling impact urgency greater than the median are assigned to the high urgency group, and the rest to the low urgency group. Within the high urgency group, vehicles are arranged in descending order of scheduling impact urgency to form a high urgency sequence, prioritizing the scheduling of vehicles with the greatest potential impact intensity to quickly reduce the risk of abnormal digital asset load. Within the low urgency group, vehicles are arranged in ascending order of data upload interval to form a low urgency sequence, prioritizing the scheduling of vehicles with shorter upload intervals to avoid data backlog and sudden traffic surges due to excessively long waiting times.

[0087] By directly splicing high-urgency sequences before low-urgency sequences, a complete asset-priority scheduling sequence is formed. This asset-priority scheduling sequence takes into account both the emergency handling of high-impact vehicles and the frequency response of low-impact vehicles, achieving a dynamic balance between physical and digital assets.

[0088] In one embodiment, physical assets are dynamically sorted and managed according to asset management coefficients to obtain a priority scheduling sequence. The computing power and bandwidth resources of the corresponding digital assets are then allocated according to this priority scheduling sequence. A collaborative management fit degree, representing the degree of matching between physical asset data upload demands and digital asset resource supply, is generated. The method further includes:

[0089] The remaining CPU core frequency and bandwidth capacity of digital assets are obtained in real time, and the remaining CPU core frequency and bandwidth capacity are matched with the asset priority scheduling sequence to obtain a resource matching value that represents the degree of matching between digital assets and vehicle demand. Specifically, this includes: obtaining the maximum CPU core frequency and maximum network card bandwidth of the server in real time, dividing the remaining CPU core frequency and bandwidth capacity by their respective maximum CPU core frequency and maximum network card bandwidth to obtain two values, and calculating the square root of the product of these two values ​​to obtain the resource sufficiency index.

[0090] Secondly, for the first half of the vehicles in the priority scheduling sequence, if the total number of vehicles is odd, round up and calculate the sum of the scheduling impact urgency of these vehicles. Then divide by the sum of the scheduling impact urgency of all vehicles to obtain the demand concentration index. The demand concentration index reflects the relative demand intensity of the most urgent half of the vehicles for resources.

[0091] The resource abundance index and the demand concentration index are harmonic averaged by dividing twice the product of the resource abundance index and the demand concentration index by the sum of the resource abundance index and the demand concentration index, and the result is normalized to the range of 0-1. This yields a resource matching value that represents the degree of fit between digital assets and vehicle demand. The larger the resource matching value, the better the match between the remaining resources of digital assets and the resource demand of the most urgent vehicles, that is, the better the supply and demand coordination.

[0092] The scheduling analysis is performed on the priority scheduling sequence of materials and the resource matching value to generate a collaborative management adaptability degree that represents the degree of matching between the physical asset data upload demand and the digital asset resource supply. Specifically, for the scheduling impact urgency of each vehicle in the priority scheduling sequence, the vehicles are arranged in sequence order. Starting from the second vehicle, the ratio of the urgency of the current vehicle to the urgency of the previous vehicle is calculated. If the urgency of the previous vehicle is zero, the ratio is 1. The ratio is mainly used to reflect the degree of attenuation or fluctuation of the urgency between adjacent vehicles in the sequence. All ratios are multiplied together and then the root is taken to the power of the number of ratios to obtain the geometric mean. If the total number of vehicles in the sequence is 1, the geometric mean is directly 1.

[0093] Multiplying the geometric mean by the resource matching value directly yields the collaborative management fit degree, which represents the degree of matching between the physical asset data upload demand and the digital asset resource supply. The value ranges from 0 to 1. When the resource matching value is high and the scheduling impact urgency in the sequence decreases steadily, the collaborative management fit degree is close to 1, indicating that the physical asset upload demand and the digital asset resource supply are highly coordinated; otherwise, the opposite is true.

[0094] In one embodiment, the collaborative management adaptability is analyzed to obtain a collaborative control identifier, including:

[0095] The collaborative management adaptability is calculated to obtain the collaborative fluctuation tolerance. Specifically, it includes: using the same 10-second window, for the collaborative management adaptability collected every second in the most recent 10 seconds, the absolute difference between two adjacent collaborative management adaptability is calculated to obtain multiple differences. These differences are sorted from smallest to largest, and the median is calculated to obtain the typical fluctuation amplitude. At the same time, the difference between the maximum and minimum collaborative management adaptability within the window is calculated. The typical fluctuation amplitude is multiplied by the difference to obtain the collaborative fluctuation tolerance.

[0096] The collaborative fluctuation tolerance is compared with the collaborative management adaptability to obtain the collaborative control indicator. Specifically, if the collaborative management adaptability is greater than or equal to the collaborative fluctuation tolerance, the collaborative control indicator is qualified, indicating that the current physical asset data upload demand and digital asset resource supply are in a coordinated state. Otherwise, the collaborative control indicator is unqualified. If it is unqualified, steps S1 to S5 are repeated until the collaborative control indicator is qualified. The maximum number of repetitions is 3. If it exceeds 3 times, manual intervention is required.

[0097] For each vehicle, the ranking consistency of data upload interval, speed, load ratio, and asset management coefficient is calculated to obtain the scheduling impact urgency. Vehicles are then divided into high-urgency and low-urgency groups. The high-urgency group is prioritized for scheduling in descending order of impact intensity, while the low-urgency group is responded to in ascending order of upload interval. The resulting asset-priority scheduling sequence not only quickly reduces the risk of abnormal server load but also avoids sudden traffic caused by data backlog. At the same time, the remaining CPU core frequency and bandwidth capacity of the server are obtained. Combined with the sum of the urgency of the first half of the asset-priority scheduling sequence, the resource matching value is calculated to accurately measure the coordination degree between remaining resources and urgent needs. Finally, the collaborative management adaptability and collaborative fluctuation tolerance are calculated and compared to determine the collaborative control identifier, thereby improving resource scheduling efficiency, reducing the probability of abnormal load, and achieving efficient collaborative control of physical and digital assets.

[0098] In one embodiment, a digital twin-based collaborative management and control system for mining assets is applied to the aforementioned management and control method, including:

[0099] The association analysis unit is used to acquire the physical and digital assets of the target location, and simultaneously acquire the physical data of the physical assets and the communication data of the digital assets in real time. It associates the physical and digital assets to obtain a fare association management index for associating physical assets with corresponding digital assets.

[0100] The load analysis unit is used to calculate the physical data and the vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load.

[0101] The management analysis unit is used to combine communication data with single asset load management for analysis, and generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets.

[0102] The collaborative adaptation unit is used to dynamically sort and manage physical assets according to asset management coefficients, obtain asset priority scheduling sequence, allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence, and generate a collaborative management adaptation degree that represents the degree of matching between physical asset data upload demand and digital asset resource supply.

[0103] The collaborative management unit is used to analyze the adaptability of collaborative management and obtain collaborative management identifiers.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A collaborative management and control method for mining assets based on digital twins, characterized in that, include: Step S1: Obtain the physical assets and digital assets of the target location, and simultaneously obtain the physical data of the physical assets and the communication data of the digital assets in real time. Associate the physical assets and digital assets to obtain a fare association management index for associating physical assets and corresponding digital assets. Step S2: Calculate the physical data and vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load. Step S3: Combine and analyze the communication data with the single asset load management degree to generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets. Step S4: Dynamically sort and manage physical assets according to asset management coefficients to obtain asset priority scheduling sequence, and allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence to generate collaborative management adaptability degree representing the degree of matching between physical asset data upload demand and digital asset resource supply. Step S5: Analyze the compatibility of collaborative management to obtain the collaborative control identifier.

2. The collaborative management and control method for mine assets based on digital twins according to claim 1, characterized in that, Linking physical assets with digital assets yields a fare association management index used to link physical assets with their corresponding digital assets, including: The physical data is analyzed to determine the intensity of the irregularity in the movement of each vehicle, and a dynamic tendency value is generated to indicate the impact of the physical asset on the sudden upload of the digital asset. For each digital asset, the CPU utilization and memory usage in its communication data are analyzed and calculated in conjunction with dynamic tendency values ​​to obtain a vehicle fee association management index for associating physical assets with corresponding digital assets.

3. The collaborative management and control method for mine assets based on digital twins according to claim 2, characterized in that, The physical data and fare association management index are calculated to obtain the single asset load management degree, which represents the degree of impact of physical asset data uploads on digital asset load, including: Based on physical data, the vehicle trajectory is mapped and decomposed to generate sudden disturbance values ​​that represent the intensity of interference to the network data stream during vehicle movement. Based on the vehicle cost association management index and communication data, the changes in CPU utilization and memory usage are calculated to obtain the load impact inertia that reflects the current vehicle impact force.

4. The collaborative management and control method for mine assets based on digital twins according to claim 3, characterized in that, The physical data and fare association management index are calculated to obtain the single asset load management degree, which represents the degree of impact of physical asset data uploads on digital asset load. This also includes: Based on the sudden disturbance value, load impact inertia, and vehicle speed in the physical data, the interaction between the vehicle's current motion behavior and digital assets is analyzed to generate a single asset load management degree that represents the degree of impact of physical asset data upload on digital asset load.

5. The collaborative management and control method for mine assets based on digital twins according to claim 4, characterized in that, By combining communication data with single-asset load management analysis, an asset management coefficient is generated representing the probability that centralized data uploads from physical assets will lead to abnormal digital asset loads. This coefficient includes: Load analysis is performed on CPU utilization, memory usage, and data transmission latency in the communication data to generate load ripple values ​​that represent the complex state of the server.

6. The collaborative management and control method for mine assets based on digital twins according to claim 5, characterized in that, By combining communication data with single-asset load management analysis, an asset management coefficient is generated representing the probability that centralized data uploads from physical assets will cause abnormal load on digital assets. This also includes: Based on the single asset load management degree, density analysis is performed on the location of all vehicles in the target area to generate a contribution clustering degree that represents the impact of data upload when vehicles are concentrated. By fusing the load ripple value and contribution clustering, an asset management coefficient is generated that represents the probability of abnormal load on digital assets caused by centralized data uploads from physical assets.

7. The collaborative management and control method for mine assets based on digital twins according to claim 6, characterized in that, Physical assets are dynamically sorted and managed based on asset management coefficients to obtain a priority scheduling sequence. Computing power and bandwidth resources for corresponding digital assets are then allocated according to this sequence. This generates a collaborative management fit degree, representing the degree of matching between physical asset data upload demands and digital asset resource supply. This degree includes: The data upload interval for each vehicle is obtained, and the data upload interval, vehicle speed, and load are analyzed. Combined with the asset management coefficient, the scheduling impact urgency, which represents the potential impact intensity of the vehicle's delayed scheduling, is calculated. By analyzing the urgency of scheduling impacts on all vehicles, a priority scheduling sequence for balancing physical and digital assets is obtained.

8. The collaborative management and control method for mine assets based on digital twins according to claim 7, characterized in that, Physical assets are dynamically sorted and managed based on asset management coefficients to obtain a priority scheduling sequence. Computing power and bandwidth resources for corresponding digital assets are then allocated according to this sequence. A collaborative management fit score, representing the degree of matching between physical asset data upload demands and digital asset resource supply, is generated. This also includes: The remaining CPU core frequency and bandwidth capacity of digital assets are obtained in real time, and the remaining CPU core frequency and bandwidth capacity are matched with the asset priority scheduling sequence to obtain a resource matching value that represents the degree of matching between digital assets and vehicle demand. The scheduling analysis is performed on the priority scheduling sequence of physical assets and the resource matching value to generate a collaborative management fit degree that represents the degree of matching between the demand for physical asset data upload and the supply of digital asset resources.

9. The collaborative management and control method for mine assets based on digital twins according to claim 8, characterized in that, The compatibility of collaborative management is analyzed to obtain collaborative control identifiers, including: The compatibility of collaborative management is calculated to obtain the collaborative fluctuation tolerance. By comparing the collaborative fluctuation tolerance with the collaborative management adaptability, a collaborative control identifier is obtained.

10. A collaborative management and control system for mining assets based on digital twins, applied in the management and control method described in any one of claims 1-9, characterized in that, include: The association analysis unit is used to acquire the physical and digital assets of the target location, and simultaneously acquire the physical data of the physical assets and the communication data of the digital assets in real time. It associates the physical and digital assets to obtain a fare association management index for associating physical assets with corresponding digital assets. The load analysis unit is used to calculate the physical data and the vehicle fare association management index to obtain the single asset load management degree, which represents the degree of impact of physical asset data upload on digital asset load. The management analysis unit is used to combine communication data with single asset load management for analysis, and generate an asset management coefficient that represents the probability of abnormal digital asset load caused by centralized uploading of data from physical assets. The collaborative adaptation unit is used to dynamically sort and manage physical assets according to asset management coefficients, obtain asset priority scheduling sequence, allocate computing power and bandwidth resources of corresponding digital assets according to asset priority scheduling sequence, and generate a collaborative management adaptation degree that represents the degree of matching between physical asset data upload demand and digital asset resource supply. The collaborative management unit is used to analyze the adaptability of collaborative management and obtain collaborative management identifiers.

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