A digital mine management method and management platform based on a twin model

Abstract

The present invention provides a digital mine management method and management platform based on a twin model, belonging to the technical field of data processing. Specifically, it includes: obtaining the data volume of associated acquisition data of different changing sub-models during model update processing, and combining the model change frequency coefficients of different changing sub-models. When it is determined that the digital twin model cannot adopt the preset update strategy, based on the change situation of the associated acquisition data of different changing sub-models within different unit time periods, determining the change data of different changing sub-models within different unit time periods, and using the change data to determine the benchmark period for model update of the digital twin model. When within the benchmark period, determining the update processing strategy of the digital twin model based on the analysis results of the associated acquisition data of different changing sub-models, which improves the matching degree between the digital twin model and the mine.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital twins, and particularly relates to a digital mine management method and management platform based on a twin model. Background Art

[0002] Mines cover a large area and have a large number of mining equipment, making mine management difficult. Specifically, in order to manage the mine, in the patent application CN202410605769.X, "A Method and System for Safety Detection of Smart Mines Based on Digital Twins", by constructing a digital twin system of the mine, it is possible to display and warn workers in a visual form, thereby solving the problem of backward safety detection in existing mine mining technologies. However, there are the following technical problems:

[0003] During the construction of the digital twin model of a digital mine, as the mining data changes, the mine data will also change accordingly. Therefore, if a fixed digital twin model is used, it is often impossible to accurately reflect the real mine state, and thus it is impossible to ensure the matching between the digital twin model and the mine.

[0004] To solve the above technical problems, specifically, the present application provides a digital mine management method and management platform based on a twin model. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a digital mine management method based on a twin model is provided.

[0007] A digital mine management method based on a twin model specifically includes:

[0008] S1 Obtain sub-models in the digital twin model corresponding to the digital mine, and determine the model change frequency coefficient and the changed sub-models of different sub-models based on the analysis results of the associated collected data of different sub-models;

[0009] S2 Obtain the data volume of the associated collected data when different changed sub-models are updated, and combine the model change frequency coefficient of different changed sub-models. When it is determined that the digital twin model cannot adopt the preset update strategy, proceed to the next step;

[0010] S3 Based on the change situation of the associated collected data of different changed sub-models within different unit time periods, determine the changed data of different changed sub-models within different unit time periods, and use the changed data to determine the benchmark period for model update of the digital twin model;

[0011] S4 When within the reference period, determine the update processing strategy of the digital twin model based on the analysis results of the associated collected data of different variable sub-models.

[0012] The beneficial effects of the present invention are as follows:

[0013] By using the variable data of different variable sub-models within different unit time periods to determine the reference period for model update of the digital twin model, it not only avoids the technical problem of the large complexity of the update process caused by frequent update processing of the digital twin model, but also realizes the determination of the reference period based on the unit time period with a higher probability of change by further combining the variable data of different variable sub-models within different unit time periods, improving the reliability of the model update process.

[0014] Determine the update processing strategy of the digital twin model based on the analysis results of the associated collected data of different variable sub-models, realizing the determination of the update processing strategy from the change situation of the key collected data of different variable sub-models. It not only ensures the matching degree between the digital twin model and the real mine, but also avoids the technical problem of the large difficulty of the update process caused by frequent update processing of the digital twin model.

[0015] A further technical solution is that the sub-model includes environmental models of the mining area, processing area, and mineral storage area.

[0016] A further technical solution is that the associated collected data is determined according to the data type of the environmental monitoring data of the mine area corresponding to the sub-model.

[0017] A further technical solution is that the method for determining the model change frequency coefficient of the sub-model is as follows:

[0018] Based on the analysis results of the associated collected data corresponding to the sub-model, determine the change situation of the associated collected data of the sub-model on different dates;

[0019] According to the change situation and a preset change amount threshold, determine the dates that require model update and use them as model change dates;

[0020] Determine the model change frequency coefficient of the sub-model through the proportion of the number of the model change dates in the dates.

[0021] A further technical solution is that the variable sub-model is a sub-model with a model change frequency coefficient greater than a preset frequency coefficient threshold.

[0022] A further technical solution is that the method for determining the update processing strategy of the digital twin model is as follows:

[0023] Analyze the results of collecting data associated with different variable sub - models, and combine with a preset variable threshold to determine the variable sub - models that need to be updated. Take the variable sub - models that need to be updated as matching deviation sub - models, and determine the model matching deviation coefficient of the digital twin model based on the proportion of the number of the matching deviation sub - models.

[0024] Determine the variation deviation coefficients of the collected data associated with different variable sub - models based on the deviation amount and the ratio to the preset variable threshold of the variation amount of the collected data associated with different variable sub - models, and use the average value of the variation deviation coefficients to determine the average value of the variation deviation coefficients.

[0025] Determine the model update requirement coefficient based on the ratio of the model matching deviation coefficient of the digital twin model to the average value of the variation deviation coefficients, and use the model update requirement coefficient to determine the update processing strategy of the digital twin model.

[0026] A further technical solution lies in using the model update requirement coefficient to determine the update processing strategy of the digital twin model, specifically including:

[0027] When the model update requirement coefficient is greater than the preset update requirement coefficient threshold, it is determined that the update processing strategy of the digital twin model is to perform all - around update processing.

[0028] When the model update requirement coefficient is not greater than the preset update requirement coefficient threshold, it is determined that the update processing strategy of the digital twin model is to update the matching deviation sub - models.

[0029] In a second aspect, the present invention provides a management platform, which adopts the above - mentioned digital mine management method based on a twin model, specifically including:

[0030] A data collection module and a digital twin model update module;

[0031] Among them, the data collection module is responsible for collecting and processing the data associated with different sub - models in the digital twin model.

[0032] The digital twin model update module is responsible for updating the digital twin model according to the update processing strategy.

[0033] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the specification and the drawings.

[0034] To make the above - mentioned objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By referring to the drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more apparent;

[0036] Figure 1 is a flowchart of a digital mine management method based on a twin model;

[0037] Figure 2 is a flowchart of a method for determining the model change frequency coefficient of a sub-model;

[0038] Figure 3 is a flowchart for determining that the digital twin model cannot adopt a preset update strategy;

[0039] Figure 4 is a flowchart of a method for determining the benchmark period of model update of a digital twin model;

[0040] Figure 5 is a framework diagram of a management platform. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0042] During the construction of the digital twin model of a digital mine, as the mining data changes, different indicators in the environmental data of the mine will also change accordingly. Therefore, if a fixed digital twin model is used, it is often impossible to accurately reflect the real mine state, thus unable to ensure the matching of the digital twin model with the mine, and further unable to ensure the reliability of mine management.

[0043] Model change frequency coefficient of the sub-model: Determine the duration for which the model needs to be updated on different dates according to the change situation of the associated acquisition data of the sub-model on different dates and a preset change amount threshold, and use the ratio of the duration for which the model needs to be updated on different dates to the preset duration to determine the model change frequency on different dates. Determine the model change frequency coefficient of the sub-model through the average value of the model change frequencies on different dates. When the model change frequency coefficient is greater than 0.6, it is determined that the sub-model is a variable sub-model.

[0044] Determine that the digital twin model cannot adopt the preset update strategy: Based on the model change frequency coefficient of the changing sub-model, determine the changing sub-model with the largest model change frequency coefficient, and use it as the reference sub-model. Based on the model change frequency coefficient of the reference sub-model, determine the corresponding reference update period of the digital twin model under the model change frequency coefficient. Determine the total data processing volume per unit time based on the reference update period and the total amount of updated data, and use the total data processing volume per unit time to determine whether the digital twin model can adopt the preset update strategy.

[0045] When the total data processing volume per unit time is not within the preset data volume range, it is determined that the digital twin model cannot adopt the preset update strategy.

[0046] Reference period: Based on the changed data of different changing sub-models in different unit time lengths, determine the changed data of different changing sub-models in different unit time lengths on different dates. Based on the changed data of different changing sub-models in different unit time lengths, determine the number of changing sub-models that need to be updated in different unit time lengths on different dates. Determine the average number of updated models in different unit time lengths through the average value of the number of changing sub-models that need to be updated in different unit time lengths on different dates.

[0047] The reference period for model update of the digital twin model is the shortest unit time length when the average number of updated models is greater than the preset update model quantity threshold.

[0048] Update processing strategy of the digital twin model: Based on the analysis results of the associated acquisition data of different changing sub-models, and combined with the preset change amount threshold, determine the changing sub-models that need to be updated. Use the changing sub-models that need to be updated as the matching deviation sub-models. Determine the model matching deviation coefficient of the digital twin model through the proportion of the number of matching deviation sub-models. Determine the update processing strategy of the digital twin model according to the model matching deviation coefficient of the digital twin model. When they do not match, determine that the update processing strategy of the digital twin model is to perform all update processing. When they match, the update processing strategy of the digital twin model is to update the matching deviation sub-models. Embodiment

[0049] To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, the first aspect is provided. The present invention provides a digital mine management method based on a twin model, specifically including:

[0050] S1 Obtain the sub-models in the digital twin model corresponding to the digital mine. Based on the analysis results of the associated acquisition data of different sub-models, determine the model change frequency coefficients and changing sub-models of different sub-models;

[0051] Furthermore, the sub-model includes environmental models of the mining area, the processing area, and the mineral storage area.

[0052] Specifically, the associated acquisition data is determined according to the data type of the environmental monitoring data of the mine area corresponding to the sub-model.

[0053] It can be understood that, as Figure 2 shown, the method for determining the model change frequency coefficient of the sub-model is as follows:

[0054] Based on the analysis results of the associated acquisition data corresponding to the sub-model, determine the change situation of the associated acquisition data of the sub-model on different dates;

[0055] According to the change situation and the preset change amount threshold, determine the dates when the model needs to be updated, and use them as the model change dates;

[0056] Determine the model change frequency coefficient of the sub-model by the proportion of the number of model change dates in the dates.

[0057] Furthermore, the changed sub-model is a sub-model whose model change frequency coefficient is greater than the preset frequency coefficient threshold.

[0058] In another embodiment, the method for determining the model change frequency coefficient of the sub-model is as follows:

[0059] Based on the analysis results of the associated acquisition data corresponding to the sub-model, determine the change situation of the associated acquisition data of the sub-model on different dates;

[0060] According to the change situation and the preset change amount threshold, determine the duration for which the model needs to be updated on different dates, and use the ratio of the duration for which the model needs to be updated on different dates to the preset duration to determine the model change frequency on different dates;

[0061] Determine the model change frequency coefficient of the sub-model by the average value of the model change frequencies on different dates.

[0062] It should be noted that the preset duration is 24 hours.

[0063] Optionally, the method for determining the model change frequency coefficient of the sub-model is as follows:

[0064] S11 Based on the analysis results of the associated acquisition data corresponding to the sub-model, determine the change situation of the associated acquisition data of the sub-model on different dates, and according to the change situation and the preset change amount threshold, determine the dates when the model needs to be updated, and use them as the model change dates;

[0065] Optionally, the above step S11 includes the following content:

[0066] S111 determines the change situation of the associated acquisition data of the sub-model on different dates based on the analysis result of the associated acquisition data corresponding to the sub-model. When it is determined that there is no date for which the sub-model needs to be updated according to the change situation and the preset change amount threshold, it is determined that the sub-model does not belong to the changed sub-model. When there is a date for which the sub-model needs to be updated, it is used as the model change date and proceeds to step S112;

[0067] S112 obtains the number of model change dates. When the number of model change dates is greater than the preset number threshold of change dates, it is determined that the sub-model belongs to the changed sub-model. When the number of model change dates is not greater than the preset number threshold of change dates, it proceeds to step S113;

[0068] S113 determines the proportion of the number of change dates in different divided time periods based on the proportion of the number of model change dates in different divided time periods. When the proportion of the number of change dates in different divided time periods is within the preset proportion interval, it is determined that the sub-model does not belong to the changed sub-model. When there is a divided time period in which the proportion of the number of change dates is not within the preset proportion interval, it enters step S114;

[0069] S114 When the number of divided time periods in which the proportion of the number of change dates is not within the preset proportion interval is greater than the preset number of divided time periods, it is determined that the sub-model belongs to the changed sub-model. When the number of divided time periods in which the proportion of the number of change dates is not within the preset proportion interval is not greater than the preset number of divided time periods, it proceeds to step S12.

[0070] S12 determines the model change frequency of different model change dates based on the number of times of update processing required in different model change dates and the update duration of different update processing times;

[0071] Optionally, the above step S12 includes the following content:

[0072] S121 determines the total number of times the sub-model needs to be updated based on the number of times of update processing required in different model change dates, and uses it as the total number of model updates. When the total number of model updates is not greater than the preset update number threshold, it proceeds to step S122. When the total number of model updates is greater than the preset update number threshold, it proceeds to step S123;

[0073] When the number of times of update processing required in different model change dates is within the preset update times range, it is determined that the sub-model does not belong to the changed sub-model. When there is a model change date in which the number of times of update processing required is not within the preset update times range, go to step S123;

[0074] S123 Determine the model change frequency of different model change dates based on the number of times of update processing required in different model change dates and the update duration of different update processing times. When there is a model change date with a model change frequency greater than the preset frequency threshold, go to step S124. When there is no model change date with a model change frequency greater than the preset frequency threshold, go to step S13;

[0075] S124 When the number of model change dates with a model change frequency greater than the preset frequency threshold is greater than the preset change date quantity threshold, it is determined that the sub-model belongs to the changed sub-model. When the number of model change dates with a model change frequency greater than the preset frequency threshold is not greater than the preset change date quantity threshold, go to step S13.

[0076] S13 Determine the model change frequency coefficient of the sub-model based on the proportion of the number of the model change dates and the model change frequency of different model change dates.

[0077] Further, the method for determining the model change frequency of the model change date is as follows:

[0078] Determine the update weight coefficient of different update processing times based on the update duration of different update processing times;

[0079] Determine the model change frequency of the model change date by the sum of the update weight coefficients of different update processing times.

[0080] It should be noted that the model change frequency coefficient of the sub-model is determined by the product of the average value of the model change frequencies of different model change dates and the proportion of the number of the model change dates.

[0081] S2 Obtain the data volume of the associated collected data when different changed sub-models perform model update processing, and combine the model change frequency coefficients of different changed sub-models. When it is determined that the digital twin model cannot adopt the preset update strategy, enter the next step;

[0082] Specifically, as Figure 3 shown, determining that the digital twin model cannot adopt the preset update strategy specifically includes:

[0083] Based on the model change frequency coefficient of the change sub-model, determine the change sub-model with the largest model change frequency coefficient, and use it as the reference sub-model;

[0084] According to the difference between the model change frequency coefficients of different change sub-models and the model change frequency coefficient of the reference sub-model, determine the change sub-model whose difference is greater than the preset difference threshold, and use it as the unchanged sub-model;

[0085] Determine the total updated data based on the amount of data collected in association with different change sub-models during model update processing, and determine whether the digital twin model can adopt the preset update strategy in combination with the proportion of the number of unchanged sub-models.

[0086] Further, determining whether the digital twin model can adopt the preset update strategy in combination with the proportion of the number of unchanged sub-models specifically includes:

[0087] Use the proportion of the number of unchanged sub-models to determine the update data volume threshold corresponding to the proportion, and use the update data volume threshold and the total updated data to determine whether the digital twin model can adopt the preset update strategy.

[0088] In another embodiment, determining that the digital twin model cannot adopt the preset update strategy specifically includes:

[0089] Based on the model change frequency coefficient of the change sub-model, determine the change sub-model with the largest model change frequency coefficient, and use it as the reference sub-model;

[0090] Determine the reference update period of the digital twin model based on the model change frequency coefficient of the reference sub-model;

[0091] Determine the total updated data based on the amount of data collected in association with different change sub-models during model update processing, and determine whether the digital twin model can adopt the preset update strategy in combination with the reference update period.

[0092] Further, determining whether the complexity of the update processing of the digital twin model meets the requirements in combination with the reference update period specifically includes:

[0093] Determine the total data processing volume per unit time based on the reference update period and the total updated data, and use the total data processing volume per unit time to determine whether the digital twin model can adopt the preset update strategy.

[0094] It should be noted that the preset update strategy is to perform overall update processing on the digital twin model when there are sub-models that need to be updated.

[0095] Optionally, it is determined that the digital twin model cannot adopt a preset update strategy, specifically including:

[0096] S21 determines the total amount of updated data based on the amount of collected data associated with different change sub-models during model update processing. Based on the model change frequency coefficient of the change sub-model, the change sub-model with the largest model change frequency coefficient is determined and used as the reference sub-model;

[0097] S22 determines the update processing difficulty coefficient of different change sub-models based on the difference between the model change frequency coefficient of different change sub-models and the model change frequency coefficient of the reference sub-model, and combines the amount of collected data associated with different change sub-models during model update processing;

[0098] S23 determines the comprehensive processing difficulty coefficient based on the update processing difficulty coefficients of different change sub-models, and determines whether the digital twin model can adopt a preset update strategy based on the comprehensive processing difficulty coefficient.

[0099] Furthermore, the update processing difficulty coefficient of the change sub-model is determined based on the difference and the preset difficulty coefficient corresponding to the amount of collected data associated with the change sub-model during model update processing.

[0100] Specifically, the comprehensive processing difficulty coefficient is determined based on the weighted sum of the update processing difficulty coefficients of different change sub-models.

[0101] S3 determines the change data of different change sub-models within different unit time periods based on the change situation of the collected data associated with different change sub-models within different unit time periods, and uses the change data to determine the reference period for model update of the digital twin model;

[0102] Specifically, as Figure 4 shown, the method for determining the reference period for model update of the digital twin model is:

[0103] Based on the change data of different change sub-models within different unit time periods, determine the change data of different change sub-models within different unit time periods on different dates;

[0104] Based on the change data of different change sub-models within different unit time periods, determine the number of change sub-models that need to be updated within different unit time periods on different dates;

[0105] Determine the average number of updated models within different unit time intervals by calculating the average value of the number of changing sub-models that require model updates within different unit time intervals on different dates, and use the average number of updated models to determine the benchmark period for model updates of the digital twin model.

[0106] Furthermore, the benchmark period for model updates of the digital twin model is the shortest unit time interval in which the average number of updated models is greater than the preset threshold for the number of updated models.

[0107] It should be noted that the unit time interval includes multiple time intervals between 1 hour and 1 day, and specifically, 1 hour is used for cutting and dividing different unit time intervals.

[0108] In another embodiment, the method for determining the benchmark period for model updates of the digital twin model is as follows:

[0109] Based on the change data of different changing sub-models within different unit time intervals, determine the change data of different changing sub-models within the unit time interval on different dates. Based on the change data of different changing sub-models within the unit time interval, determine the changing sub-models that require model updates within the unit time interval on different dates, and use them as updated sub-models;

[0110] When the number of dates with updated sub-models within the unit time interval is less than the preset number of dates, it is determined that the unit time interval does not belong to the benchmark period for model updates of the digital twin model;

[0111] When the number of dates with updated sub-models within the unit time interval is not less than the preset number of dates:

[0112] Based on the number of updated sub-models within the unit time interval on different dates, when determining the dates where the number of non-updated sub-models is greater than the preset threshold for the number of sub-models, it is determined that the unit time interval does not belong to the benchmark period for model updates of the digital twin model;

[0113] When there are dates where the number of updated sub-models is greater than the preset threshold for the number of sub-models:

[0114] Use the dates where the number of updated sub-models is greater than the preset threshold for the number of sub-models as the selected changing dates. When the proportion of the number of selected changing dates is less than the preset proportion of the number of dates, it is determined that the unit time interval does not belong to the benchmark period for model updates of the digital twin model;

[0115] When the proportion of the number of selected changing dates is not less than the preset proportion of the number of dates:

[0116] Determine the model update frequency coefficient within the unit time period based on the number of updated sub-models within the unit time period on different dates, and use the model update frequency coefficient to determine the benchmark period for model updates of the digital twin model.

[0117] S4 When within the benchmark period, determine the update processing strategy of the digital twin model based on the analysis results of the associated acquisition data of different variable sub-models.

[0118] Specifically, the method for determining the update processing strategy of the digital twin model is as follows:

[0119] Based on the analysis results of the associated acquisition data of different variable sub-models, and in combination with a preset variable threshold, determine the variable sub-models that need to be updated, take the variable sub-models that need to be updated as matching deviation sub-models, and determine the model matching deviation coefficient of the digital twin model through the proportion of the number of the matching deviation sub-models;

[0120] Determine the variation deviation coefficient of the associated acquisition data of different variable sub-models based on the deviation amount between the variation amount of the associated acquisition data of different variable sub-models and the preset variable threshold and the ratio to the preset variable threshold, and use the average value of the variation deviation coefficients to determine the average variation deviation coefficient;

[0121] Determine the model update requirement coefficient based on the ratio of the model matching deviation coefficient of the digital twin model to the average variation deviation coefficient, and use the model update requirement coefficient to determine the update processing strategy of the digital twin model.

[0122] Further, using the model update requirement coefficient to determine the update processing strategy of the digital twin model specifically includes:

[0123] When the model update requirement coefficient is greater than the preset update requirement coefficient threshold, then determine that the update processing strategy of the digital twin model is to perform all update processing;

[0124] When the model update requirement coefficient is not greater than the preset update requirement coefficient threshold, then determine that the update processing strategy of the digital twin model is to perform update processing on the matching deviation sub-models. Embodiment

[0125] Second aspect, as Figure 5 shown, the present invention provides a management platform, which adopts the above-mentioned digital mine management method based on a twin model, and specifically includes:

[0126] A data acquisition module, a digital twin model update module;

[0127] The data acquisition module is responsible for collecting and processing the data associated with different sub-models in the digital twin model;

[0128] The digital twin model update module is responsible for updating the digital twin model according to the update processing strategy.

[0129] Optionally, the above step S21 includes the following content:

[0130] S211 determines the total amount of updated data based on the amount of data associated with the different changing sub-models during the model update process. When the total amount of updated data is greater than the preset total amount, it is determined that the digital twin model cannot adopt the preset update strategy. When the total amount of updated data is not greater than the preset total amount of data, it proceeds to step S212;

[0131] S212 determines the preset frequency coefficient threshold using the total amount of updated data and the preset mapping function. Based on the model change frequency coefficients of the different changing sub-models, when it is determined that there is no changing sub-model with a model change frequency coefficient greater than the preset frequency coefficient threshold, it is determined that the digital twin model can adopt the preset update strategy. When there is a changing sub-model with a model frequency coefficient greater than the preset frequency coefficient threshold, it proceeds to step S213;

[0132] S213 takes the changing sub-models with model frequency coefficients greater than the preset frequency coefficient threshold as the screened changing sub-models. When the number of the screened changing sub-models does not meet the requirements, it is determined that the digital twin model can adopt the preset update strategy. When the number of the screened changing sub-models meets the requirements, it proceeds to step S214;

[0133] S214 when there is a screened changing sub-model with a deviation amount between the model frequency coefficient and the preset frequency coefficient threshold not within the preset deviation range, it is determined that the digital twin model cannot adopt the preset update strategy. When there is no screened changing sub-model with a deviation amount between the model frequency coefficient and the preset frequency coefficient threshold not within the preset deviation range, it proceeds to step S22;

[0134] Optionally, the above step S22 includes the following content:

[0135] S221 determines whether there is a changing sub-model with a difference in the model change frequency coefficient between the different changing sub-models and the model change frequency coefficient of the reference sub-model not within the preset difference interval. If so, it proceeds to the next step. If not, it is determined that the digital twin model can adopt the preset update strategy;

[0136] S222 regards the variation sub - models whose differences are not within the preset difference range as the frequent - coefficient deviation sub - models. When the number of the frequent - coefficient deviation sub - models does not meet the requirements, it is determined. When the number of the frequent - coefficient deviation sub - models meets the requirements, go to step S223;

[0137] S223 determines the total data processing volume per unit time based on the reference update period and the total update data volume. When the total data processing volume per unit time is greater than the preset data volume threshold, go to step S224. When the total data processing volume per unit time is not greater than the preset data volume threshold, it is determined that the digital twin model can adopt the preset update strategy;

[0138] S224 determines the update - processing difficulty coefficients of different variation sub - models by using the differences between the model variation frequent coefficients of different variation sub - models and the model variation frequent coefficient of the reference sub - model, and the data volume of the associated collected data during model update processing. When the number of variation sub - models whose update - processing difficulty coefficients do not meet the requirements is greater than the preset number of sub - models, it is determined that the digital twin model cannot adopt the preset update strategy. When the number of variation sub - models whose update - processing difficulty coefficients do not meet the requirements is not greater than the preset number of sub - models, go to step S23.

[0139] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non - volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content.

[0140] The above - mentioned describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multi - tasking and parallel processing are also possible or may be advantageous.

[0141] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A digital mine management method based on a twin model, characterized in that Specifically include: Obtain sub-models in the digital twin model corresponding to the digital mine, and determine the model change frequency coefficients and changed sub-models of different sub-models based on the analysis results of the associated collected data of different sub-models; Obtain the data volume of the associated collected data when different changed sub-models perform model update processing, and combine the model change frequency coefficients of different changed sub-models to determine that when the digital twin model cannot adopt the preset update strategy, proceed to the next step; Based on the change situations of the associated collected data of different changed sub-models within different unit time periods, determine the changed data of different changed sub-models within different unit time periods, and use the changed data to determine the benchmark period for model update of the digital twin model; When within the benchmark period, determine the update processing strategy of the digital twin model based on the analysis results of the associated collected data of different changed sub-models; The preset update strategy is that when there are sub-models that need to be updated, the digital twin model is updated as a whole; The method for determining the update processing strategy of the digital twin model is: Based on the analysis results of the associated collected data of different changed sub-models, and in combination with a preset change amount threshold, determine the changed sub-models that need to be updated, use the changed sub-models that need to be updated as matching deviation sub-models, and determine the model matching deviation coefficient of the digital twin model through the proportion of the number of matching deviation sub-models; Determine the change deviation coefficients of the associated collected data of different changed sub-models based on the deviation amounts of the change amounts of the associated collected data of different changed sub-models from the preset change amount threshold and the ratios to the preset change amount threshold, and use the average value of the change deviation coefficients to determine the average change deviation coefficient; Determine the model update demand coefficient according to the ratio of the model matching deviation coefficient of the digital twin model to the average change deviation coefficient, and use the model update demand coefficient to determine the update processing strategy of the digital twin model; Using the model update demand coefficient to determine the update processing strategy of the digital twin model specifically includes: When the model update demand coefficient is greater than the preset model update demand coefficient threshold, it is determined that the update processing strategy of the digital twin model is to perform all update processing; When the model update demand coefficient is not greater than the preset model update demand coefficient threshold, it is determined that the update processing strategy of the digital twin model is to update the matching deviation sub-models; 2. The digital mine management method based on the twin model according to claim 1, wherein, The sub-models include environmental models of mining areas, processing areas, and mineral storage areas; 3. The digital mine management method based on the twin model according to claim 1, characterized in that, The associated collected data is determined according to the data types of the environmental monitoring data of the mine areas corresponding to the sub-models; 4. The digital mine management method based on the twin model according to claim 1, characterized in that The method for determining the model change frequency coefficient of the sub-model is: Based on the analysis results of the associated collected data corresponding to the sub-model, determine the change situations of the associated collected data of the sub-model on different dates; According to the change situation and the preset change amount threshold, determine the dates that need to be updated for the model, and use them as model change dates; Determine the model change frequency coefficient of the sub-model based on the proportion of the number of the model change dates in the dates.

5. The digital mine management method based on the twin model according to claim 4, wherein, The changed sub-model is a sub-model whose model change frequency coefficient is greater than the preset frequency coefficient threshold.

6. The digital mine management method based on the twin model according to claim 1, characterized in that, Determine that the digital twin model cannot adopt the preset update strategy, specifically including: Based on the model change frequency coefficient of the changed sub-model, determine the changed sub-model with the largest model change frequency coefficient and use it as the reference sub-model; According to the difference between the model change frequency coefficients of different changed sub-models and the model change frequency coefficient of the reference sub-model, determine the changed sub-models whose differences are greater than the preset difference threshold and use them as the unchanged sub-models; Determine the total amount of updated data based on the amount of data collected in association with different changed sub-models during model update processing, and determine whether the digital twin model can adopt the preset update strategy in combination with the proportion of the number of the unchanged sub-models.

7. The digital mine management method based on the twin model according to claim 6, characterized in that, Determine whether the digital twin model can adopt the preset update strategy in combination with the proportion of the number of the unchanged sub-models, specifically including: Use the proportion of the number of the unchanged sub-models to determine the threshold of the updated data volume corresponding to the proportion, and use the threshold of the updated data volume and the total amount of updated data to determine whether the digital twin model can adopt the preset update strategy.

8. A management platform that adopts a digital mine management method based on a twin model according to any one of claims 1-7, characterized in that, Specifically including: A data acquisition module and a digital twin model update module; Among them, the data acquisition module is responsible for collecting and processing the data collected in association with different sub-models in the digital twin model; The digital twin model update module is responsible for updating the digital twin model according to the update processing strategy.

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