Multi-dimensional quantification-based coal mine intelligent system function hierarchy division method and system, electronic equipment and computer readable storage medium

Abstract

The invention belongs to the technical field of coal mine intellectualization, and particularly relates to a coal mine intellectualization system function hierarchy division method and system based on multi-dimensional quantification, and the method comprises the steps: carrying out the quantitative scoring based on three basic attributes of a function data dependence range, a decision influence range and a decision generation mode, and determining an initial ideal deployment hierarchy; for functions with contradictions among attributes, the functions are decoupled into a decision generation unit and a decision execution unit, ideal hierarchies are divided for the decision generation unit and the decision execution unit respectively, and a primary architecture of cloud edge collaboration is formed; all cross-level collaborative links generated after decoupling are identified, and the risk level of each link is quantified by using a risk assessment matrix from two dimensions of link failure consequence level and link real-time performance and reliability requirements; and calculating an overall collaborative risk index and risk density of the scheme, and outputting a final landing deployment scheme through a deployment decision matrix in combination with system function cohesion analysis. And standardization and computability of the division process are realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent coal mining technology, and specifically relates to a method and system for functional hierarchy division of intelligent coal mining systems based on multi-dimensional quantification. Background Technology

[0002] In recent years, the state has vigorously promoted the intelligent construction of coal mines, and has successively issued a series of policies, technical guidelines and standard documents, which have put forward clear requirements for the functions and construction level of intelligent coal mine systems. Against this background, professional subsystems such as coal mining, tunneling, transportation and safety have been comprehensively upgraded, and integrated management and control platforms have been widely used in major coal mines.

[0003] However, in current construction practices, most coal mines, when deploying integrated management and control platforms, have not fully explored their core value. They often only achieve data integration and control function aggregation for subsystems, failing to realize intelligent collaboration and decision optimization across systems. At the same time, various specialized subsystems have also expanded with numerous intelligent functions to adapt to the actual production needs of coal mines, leading to significant functional overlap and data silos between the platform and subsystems, as well as between different subsystems. This duplication of construction not only wastes resources but also results in low efficiency in inter-system collaboration, making it difficult to truly realize the overall intelligent value.

[0004] Furthermore, due to the high complexity of technical integration involving numerous different professional fields, the integrated management and control platform, various intelligent systems, and specialized subsystems present significant challenges for coal mines in top-level design and unified planning. On the other hand, equipment and software suppliers often add many proprietary advanced functions to their systems to enhance product competitiveness, further exacerbating the heterogeneity of the system architecture and the difficulty of integration. Therefore, how to scientifically and rationally plan and collaboratively design the functions of coal mine intelligent systems in a complex environment involving multiple systems, multiple professions, and multiple suppliers has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0005] The technical problem this invention aims to solve is the lack of unified and scientific functional planning and hierarchical division in the construction of existing intelligent coal mine systems.

[0006] To address this, the present invention provides a method and system for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification. This method is used to determine how to scientifically divide various functions in intelligent coal mine systems among the equipment and control layer, professional subsystem layer, integrated automation system, and integrated management and control platform layer. It clarifies the functional boundaries of each system level and can also be applied to the field of intelligent technology in non-coal mines and production enterprises. This fundamentally overcomes the uncertainty caused by relying on personal experience and subjective judgment, and provides a clear, reliable, and implementable functional hierarchy division scheme for intelligent coal mine systems.

[0007] The technical solution adopted by this invention to solve its technical problem is: A method for functional hierarchy partitioning of intelligent coal mine systems based on multi-dimensional quantification, including: Step 1: Quantitatively score the three fundamental attributes of the function: data dependency scope, decision impact scope, and decision generation mode, to determine its initial ideal deployment level; Step 2: For functions with conflicting attributes, decouple them into decision generation units and decision execution units, and assign ideal levels to each to form a preliminary cloud-edge collaborative architecture. Step 3: Identify all cross-level collaborative links generated after decoupling, and quantify the risk level of each link from two dimensions: the severity of link failure consequences and the real-time and reliability requirements of the link. Step four: Calculate the overall collaborative risk index and risk density of the solution, and combine them with the system functional cohesion analysis. Through the deployment decision matrix, output the final feasible deployment solution.

[0008] Furthermore, in step two: S2.1 Decision Execution Unit: Based on its data dependency scope and decision impact scope, it is divided into an appropriate lower level; Decision Generation Unit: Based on its decision generation mode and required data scope, it is divided into an appropriate top level. S2.2 Decoupling Function Evaluation: Decouple complex functions into two clear smaller functions. If conflicting functions are found during evaluation, continue decoupling according to S2.1 until no more conflicts are found during evaluation. S2.3 Collaborative Design: After decoupling, a collaborative relationship of training-deployment or strategy-control is formed between the decision generation unit and the execution unit. The model, strategy or parameter are distributed and data is fed back through standardized interfaces.

[0009] Furthermore, the risk assessment matrix is ​​based on two dimensions: the consequence level A of link failure and the real-time and reliability requirements of the link. It divides the risk level R of each link into acceptable risk, low risk, medium risk, high risk, and extremely high risk.

[0010] Furthermore, in step four, the overall collaborative risk index Where n is the total number of collaborative links in the scheme; R i The risk level value for the i-th link is recommended to be set according to the risk level R mentioned above, i.e., 0 is low risk, 1 is acceptable risk, 2 is medium risk, 3 is high risk, and 4 is extremely high risk; W is the weight of the i-th link, which is set by the link failure consequence level A, i.e., 1 is negligible, 2 is moderate, 3 is severe, and 4 is catastrophic.

[0011] Furthermore, the risk density , P HR = 0%, indicating an ideal situation where there are no high-risk links in the plan; 0% < P HR ≤ 20% indicates that the risk is relatively isolated and the solution can be optimized through targeted measures; P HR A density >20% indicates a high-risk warning.

[0012] Furthermore, in step four, when a high-risk density warning is detected, the deployment level is optimized based on the system functional cohesion. It is determined whether the decoupling scheme or functional cluster is a relatively independent business unit and mapped to the most suitable deployment level.

[0013] Furthermore, the cohesion optimization deployment hierarchy includes device-level cohesion, process-level cohesion, region-level cohesion, and global-level cohesion.

[0014] A functional hierarchy partitioning system for intelligent coal mine systems based on multi-dimensional quantification, comprising: The data acquisition and interface layer is used to receive and standardize various input information and import function lists.

[0015] The core computing engine layer is connected to the data acquisition and interface layer. The core computing engine layer implements the functional hierarchy division method of the intelligent coal mine system based on the data of the data acquisition and interface layer as described above. The decision support and human-computer interaction layer, together with the core computing engine layer, is used to intuitively display the calculation results of the core computing engine layer.

[0016] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the functional hierarchy division method for intelligent coal mine systems based on multi-dimensional quantization as described above.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the functional hierarchy division method for intelligent coal mine systems based on multi-dimensional quantization as described above.

[0018] The beneficial effects of this invention are: (1) Scientific decision-making: The functional deployment has been transformed from "experience-driven" to "data and model-driven", which greatly reduces subjective assumptions and improves the scientificity and consistency of system architecture design.

[0019] (2) Risk prevention: Potential risks of cross-level collaboration can be identified and quantified during the system design phase, avoiding huge modification costs and security risks caused by architectural defects during the system construction and operation and maintenance phases, thus achieving "source governance".

[0020] (3) Resource optimization: Through precise functional division and decoupling, the duplication and waste of resources are avoided. High real-time and high reliability functions are deployed on the edge, while high computing power and globally optimized functions are deployed on the cloud, thus achieving the global optimal configuration of computing and network resources.

[0021] (4) Enhanced system resilience: By promoting the deployment of highly cohesive and high-risk functional clusters in the "on-site centralized control center" mode, regional autonomy has been achieved, reducing the system's dependence on the central network and platform. Even when disconnected from the upper-level system, the core production links can still maintain stable operation, greatly improving the resilience and reliability of the entire intelligent system. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Figure 1 This is a schematic diagram of the intelligent coal mine system in this invention.

[0024] Figure 2 This is a schematic diagram of the functional hierarchy division system of the intelligent coal mine system based on multi-dimensional quantification in this invention.

[0025] Figure 3 This is a schematic diagram of the functional hierarchy division method for intelligent coal mine systems based on multi-dimensional quantification in this invention. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0027] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] A multi-dimensional quantitative functional hierarchy partitioning system for intelligent coal mine systems is proposed. The intelligent coal mine system comprises an equipment and control layer, a specialized subsystem layer, an integrated automation system layer, and a comprehensive management and control platform layer. This functional hierarchy partitioning system is used to deploy functions into these four layers.

[0030] The intelligent coal mine system adopts a layered and modular design concept. Its core architecture mainly includes: data acquisition and interface layer, core computing engine layer, and decision support and human-computer interaction layer.

[0031] The data acquisition and interface layer serves as a bridge between the system and the external environment, responsible for receiving and standardizing various input information. This layer includes a function library import interface, a system structure knowledge base interface, and a real-time / historical data monitoring interface. The function library import interface receives the input list of intelligent functions for coal mines, providing standardized templates (such as Excel, XML, or API) for users or third-party systems to import the list of intelligent functions to be evaluated and their initial descriptions. The system structure knowledge base interface interfaces with existing or pre-defined system architecture diagrams, network topology diagrams, equipment lists, etc., automatically reading or manually defining the system's hierarchical structure (equipment layer, subsystem layer, etc.) and its performance parameters (such as network bandwidth and controller performance). The real-time / historical data monitoring interface interfaces with the mine's industrial ring network, Supervisory Control and Data Acquisition (SCADA) system, or data platform to obtain data such as actual network latency and bandwidth utilization on critical links, used to calibrate risk assessment models.

[0032] The core computing engine layer integrates a vertical vectorization module, an ideal level determination module, an intelligent decoupling analysis module, a collaborative link identification and risk assessment module, and an overall scheme optimization and decision-making module, which are used to realize a functional level division method for intelligent coal mine systems based on multi-dimensional quantification.

[0033] Specifically, the functional attribute quantification module provides a standardized human-computer interface or API to guide users or the system in scoring each function based on three fundamental attributes: "data dependency scope," "decision impact scope," and "decision generation mode." The functional attribute quantification module has a built-in scoring rule library that performs logical validation on user input to ensure the standardization and consistency of the scoring.

[0034] The ideal level determination module has the built-in "ideal level determination matrix" and receives scores from the quantification module. It automatically matches and outputs the initial ideal deployment level (equipment and control layer, professional subsystem layer, integrated automation system layer, and integrated management and control platform layer) for each function.

[0035] Intelligent decoupling analysis module: Automatically detects contradictions between functional attributes (such as a low score for data influence range but a high score for decision generation mode). Provides a decoupling suggestion interface to help users break down complex functions into "decision generation units" and "decision execution units," and re-quantifies the attributes and determines the hierarchy of each unit.

[0036] The collaborative link identification and risk assessment module automatically identifies all cross-level data / instruction flows after decoupling and inherent in the system, generating a "collaborative link list" to guide users in assessing the "failure consequence level" and "real-time and reliability requirements" of each link. The collaborative link identification and risk assessment module has a built-in "risk assessment matrix" that automatically calculates the risk level (low risk, moderate risk, medium risk, high risk, and extremely high risk) of each link.

[0037] The overall solution optimization and decision-making module automatically calculates the "Overall Collaborative Risk Index (OCRI)" of the solution based on the risk level and failure consequence weight of all collaborative links through the OCRI calculation unit; and automatically calculates the "percentage of extremely high / high-risk links (P)" through the risk density analysis unit. HR The system's functional cohesion analysis unit provides guided questionnaires or models to assist users in determining the cohesion level of functional clusters (equipment level, process level, region level, global level); the deployment decision matrix engine incorporates the "risk and cohesion-based deployment decision matrix," which, based on the input OCRI and P... HR Based on the cohesion results, the system automatically outputs final deployment decision recommendations, such as: "Maintain platform-level deployment," "It is recommended to adopt the 'on-site control center' mode and move to the professional subsystem layer," and "The solution risk is unacceptable and needs to be redesigned."

[0038] The decision support and human-computer interaction layer presents the core engine's calculation results to users in an intuitive and operable format, including a visualization module and a report generation and output module. The visualization module can serve as a dashboard, outputting results such as a hierarchical division diagram, a collaborative link risk map, and a quantitative indicator dashboard. The hierarchical division diagram, presented as an architecture diagram, clearly shows the system hierarchy to which each function is ultimately assigned after analysis by the system, making functional boundaries readily apparent. The collaborative link risk map visualizes all collaborative links in a topology diagram, highlighting their risk levels with different colors (e.g., green, yellow, orange, red) to quickly locate weak points in the system architecture. The quantitative indicator dashboard dynamically displays OCRI, P... HR The values ​​of key indicators and health status.

[0039] The report generation and output module is used to output standard technical solution reports, machine-readable configuration files, etc., and can generate a standardized "Functional Hierarchy Division Scheme Report for Intelligent Coal Mine System" with one click. The content includes: function list and attribute scores, ideal hierarchy determination results, decoupling analysis process, details of collaborative link risk assessment, overall scheme OCRI analysis, and final deployment decision recommendations and reasons. It outputs machine-readable configuration files, providing a direct basis for subsequent system development, integration and deployment.

[0040] Specifically, in intelligent coal mine systems, the equipment and control layer is the foundational layer for direct interaction between the intelligent system and the physical world, acting as the "hands, feet, and reflexes." This layer is responsible for the lowest-level signal acquisition, equipment driving, and high-speed, high-reliability logic control. Its core components include PLCs (Programmable Logic Controllers), RTUs (Remote Terminal Units), intelligent sensors, actuators, frequency converters, and the underlying drivers or edge control units of intelligent devices such as inspection robots. The typical characteristics of this layer are high real-time performance, high reliability, and functional singularity. Its primary task is to ensure accurate, stable, and instantaneous control of field equipment, forming the most basic safety and control barrier for the system. The core functional boundary of this layer lies in executing predefined, deterministic control logic, and does not include complex algorithms, human-machine interfaces, or any cross-device process coordination functions.

[0041] The specialized subsystem layer is an intelligent "organ" oriented towards specific production process stages, undertaking the monitoring, automated control, and specialized optimization functions of an independent production unit (such as fully mechanized mining, hoisting, drainage, and ventilation). This layer mainly refers to subsystems with independent human-machine interfaces (HMIs) or configuration software, such as fully mechanized mining automation systems, hoisting electrical control systems, drainage monitoring systems, and power monitoring systems. Its typical characteristics are specialization, closed-loop automation within the process, and bridging of upstream and downstream processes. It achieves precise control and efficient operation of local production processes through in-depth cultivation and optimization of the process within its own stage. The functional scope of this layer is strictly limited to its specialized field, does not include cross-production process linkage logic, and does not undertake mine-wide data fusion and macro-level decision-making tasks.

[0042] The integrated automation system layer serves as the command and control center for mine production operations, acting as the coordinating "cerebellum" of the system. Its core value lies in horizontally connecting all specialized subsystems, enabling cross-system process linkage, centralized monitoring, and initial aggregation of production data. Its main functions include mine-wide "one-click start / stop," coordinated control of production systems (such as coal flow coordination), and centralized monitoring and alarm management throughout the entire process. A typical characteristic of this layer is unified integrated control and control process-driven operation. It is important to clarify that this layer primarily handles coordination and control based on preset rules; its functions do not include complex intelligent analysis model calculations, long-term strategic planning, or in-depth decision optimization.

[0043] The integrated management and control platform layer serves as the intelligent management and decision-making center of the mine, acting as the "brain" of the system and overseeing the overall situation. Its core value lies in its vertical integration, responsible for the fusion and governance of multi-source data across the entire mine, the perception of the overall operational status, the computation of intelligent analysis models, and the formulation and supervision of optimization control strategies. This layer primarily includes integrated management and control platform software, a data middleware platform, an AI algorithm platform, digital twins, and visualization dashboards. Its typical characteristics are globality, intelligence, and decision-making, aiming to achieve intelligent analysis, decision support, strategic management, and resource optimization across the entire mine's systems and business areas. As the highest decision-making level, its function is focused on analysis and optimization; it does not involve issuing direct control commands to lower-level equipment, nor does it intervene in real-time process coordination logic beyond its level.

[0044] A method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification is proposed. Its core lies in establishing an objective and computable quantitative evaluation system, and a closed-loop decision-making process from "functional attribute analysis" to "decoupling design," then to "collaborative risk assessment," ultimately forming a "robust deployment plan." This process guides the rational deployment of functions at the equipment and control layer, professional subsystem layer, integrated automation system layer, and integrated management and control platform layer. Specifically, it includes the following steps: Step 1: Based on the functional attribute quantification module and the ideal level determination module, perform functional ideal level division.

[0045] The system is quantitatively scored based on three fundamental attributes of the function: "data dependency scope", "decision impact scope" and "decision generation mode". The initial ideal deployment level is determined by a preset "ideal level judgment matrix".

[0046] Three fundamental judgment attributes related to functional division were defined and scored and quantified, and the quantification dimensions are shown in Table 1.

[0047] Table 1 Functional Attribute Evaluation Table

[0048] The scores of the three fundamental attributes of the evaluation function are used to find the level that best matches the ideal level decision matrix. The ideal level decision matrix is ​​shown in Table 2.

[0049] Table 2 Ideal Hierarchy Division Rules

[0050] Step two, decoupling complex functions, is achieved through the intelligent decoupling analysis module.

[0051] For functions with contradictory attributes, such as a function that scores 1 point for data dependency scope but 5 points for decision impact, where a decision can be made using only data from a single device (a strategic decision), these functions often physically span different levels, leading to contradictions when determining the ideal level.

[0052] Therefore, the functions are deconstructed and decoupled into "decision generation units" and "decision execution units," and ideal levels are assigned to each, forming a preliminary "cloud-edge collaboration" architecture.

[0053] Specifically: S2.1 Separate the "decision generation" and "decision execution" functions to be evaluated.

[0054] Decision execution unit: Based on its data dependency scope and decision impact scope, it is divided into an appropriate lower level (usually the equipment layer or professional subsystem layer) to ensure real-time performance and reliability.

[0055] Decision generation unit: Based on its decision generation mode and the required data range, it is divided into an appropriate top-level layer (usually the comprehensive management and control platform layer) to continuously optimize by utilizing global data and powerful computing power.

[0056] S2.2 Decoupling Function Evaluation: For decoupled functions, instead of evaluating a vague "function," evaluate two clear sub-functions. If conflicting functions emerge during evaluation, continue decoupling according to S2.1 until no further conflicts arise during the evaluation.

[0057] S2.3 Collaborative Design: After decoupling, a collaborative relationship of "training-deployment" or "policy-control" is formed between the decision generation unit and the execution unit, and the model, policy or parameter are distributed and data is fed back through standardized interfaces.

[0058] For example, the fundamental attribute analysis of "server-side video intelligent recognition of safety helmets and issuing voice alarms to the scene" is as follows: Data dependency range: 1 point (device data), the data source required for recognition is the video stream of a single camera; Decision impact range: 1 point (single device), the output of the decision is to control the local voice broadcaster to issue an alarm; Decision generation mode: 4 points (machine learning model), the core is a complex AI visual recognition algorithm.

[0059] Based on the first two attributes (highly localized data and scope of influence), it strongly points to the device and control layer, but the third attribute (complex AI model) points to the integrated management and control platform layer. The cyber-physical scope of the function deviates from its intelligent complexity, creating a contradiction. The above-mentioned ideal level determination method makes a difficult trade-off between "high real-time performance of the device layer" and "strong computing power of the platform layer".

[0060] Therefore, for this type of function, it is actually necessary to decouple it first and then make a judgment. That is, when the fundamental attribute analysis of the function reveals an inherent contradiction, the decoupling process is initiated.

[0061] Decoupling example: Minor Function A: Safety Helmet Recognition Alarm Execution Data dependency: 1 point; Decision impact: 1 point; Decision pattern: 1 point (At this point, the model has been solidified into executable code and is regarded as "hard logic").

[0062] Determination: The ideal layer is the device and control layer. It should be implemented in the camera or near-end edge computing unit.

[0063] Minor Feature B: Recognition Algorithm Optimization and Deployment Data dependency: 4 points (requires full mine violation samples); Decision impact: 2 points (affects all camera algorithms); Decision pattern: 4 points (machine learning training).

[0064] Determination: The ideal level is the integrated management and control platform layer. Model training and testing are completed on the cloud platform, and then the optimized model is pushed to edge devices.

[0065] Step 3, collaborative risk assessment, is achieved through the collaborative link identification and risk assessment module.

[0066] Identify all "cross-level collaborative links" generated after decoupling, and quantify the risk level of each link using a risk assessment matrix from two dimensions: "link failure consequence level" and "link real-time and reliability requirements".

[0067] After the functions to be evaluated are decoupled, complex functions are broken down into smaller, cross-level functions. Due to the cross-level nature of these functions, numerous implementation risks arise regarding real-time performance and collaboration. Risk assessments must be conducted on both the decoupled and inherent cross-level collaborative links to verify their engineering feasibility.

[0068] The cross-level collaborative risk quantification assessment method proposed below can be used for collaborative risk assessment after decoupling, as well as for collaborative risk assessment of functions with collaborative relationships in intelligent systems.

[0069] This method calculates a quantified "synergistic risk value" and uses a specialized diagnostic matrix to map high-risk outcomes to specific improvement measures.

[0070] S3.1 identifies collaborative links and clarifies the flow of data / instructions.

[0071] For each decoupled function, draw its collaborative link diagram.

[0072] Taking the "video intelligent recognition safety helmet" as an example, after decoupling, there are two core collaborative links: Link 1 (data uplink): Equipment and control layer (camera) ---> Video stream ---> Integrated management and control platform layer (AI server) Link 2 (downlink command): Integrated Management and Control Platform Layer (AI Server) ---> Alarm Commands ---> Device and Control Layer (Voice Broadcaster) S3.2 Quantitative Risk Assessment: For each collaborative link, it is evaluated from two decisive dimensions (the consequences of link failure and the real-time and reliability requirements of the link), and its risk level is determined using a risk assessment matrix.

[0073] Table 3. Link Failure Consequence Level (A) Assessment Table

[0074] Table 4 Evaluation Table of Link Real-Time Performance and Reliability Requirements

[0075] Table 5 Risk Assessment Matrix

[0076] Table 6. Comparison of Risk Level (R) Definitions and Handling Principles

[0077] Step four, overall solution decision-making, is achieved through the overall solution optimization and decision-making module, which includes an OCRI calculation unit, a risk density analysis unit, a system functional cohesion analysis unit, and a deployment decision matrix engine.

[0078] The overall collaborative risk index and risk density of the calculation scheme are combined with "system functional cohesion analysis" and the final feasible deployment scheme is output through the deployment decision matrix (such as maintaining the original design, strengthening the link, or adopting the on-site centralized control center mode to sink the function).

[0079] This method aims to systematically evaluate the collaborative risk level of the entire technical solution after functional decoupling and provide clear architectural decision-making guidance for high-risk solutions. It comprehensively measures the feasibility of the solution by calculating a quantitative Overall Collaboration Risk Index (OCRI) and incorporating risk density and system functional cohesion analysis, and directly outputs the optimal path.

[0080] S4.1 Calculate the Overall Collaborative Risk Index (OCRI) OCRI reflects the overall risk level of all collaborative links in the scheme, and the calculation formula is as follows:

[0081] Where n: the total number of collaborative links in the scheme; : Risk level value for the i-th link. It is recommended to set it according to the risk level (R) mentioned above, i.e., 0-low risk, 1-acceptable risk, 2-medium risk, 3-high risk, 4-extremely high risk; W: Weight of the i-th link. It is recommended to set it according to the link failure consequence level (A) mentioned above, i.e., 1-negligible, 2-moderate, 3-severe, 4-catastrophic.

[0082] The calculated OCRI values ​​are as follows: OCRI < 1.5 indicates that the overall risk is controllable and the solution is excellent; 1.5 ≤ OCRI < 2.5 indicates that the overall risk is moderate and high-risk links need to be monitored; OCRI ≥ 2.5 indicates that the overall risk is high and the solution needs major adjustments.

[0083] S4.2 Assess the risk structure (risk density) of the proposed solution. In addition to OCRI, it is also necessary to analyze the distribution of risks, i.e., risk density.

[0084] Percentage of extremely high / high-risk links ( ):

[0085] = 0%, indicating an ideal situation where there are no high-risk links in the plan; 0% < ≤ 20% indicates that the risk is relatively isolated and the solution can be optimized through targeted measures (such as reinforcing certain links); A threshold of >20% indicates a high-risk density warning. This suggests that the risk is systemic and may stem from an inadequate top-level architecture design. In such cases, a complete redesign of the architecture should be prioritized over patching individual links.

[0086] S4.3 System Functional Cohesion Analysis and Hierarchical Mapping When a high-risk density warning appears, it may stem from an unreasonable top-level architecture design. The main reason is that after decoupling, different parts of a function are assigned to different ideal levels, inevitably creating cross-level collaborative links. Some of these links, while theoretically feasible, are high-risk or even infeasible in real-world engineering environments (such as the complex network conditions of mines). Therefore, in such cases, a complete redesign of the architecture should be prioritized. After this architectural adjustment, steps two or three should be returned to re-decouple and reassess the risks of the new solution until the overall risk is reduced to an acceptable level.

[0087] This section presents a method for optimizing deployment hierarchy based on system functional cohesion. This step aims to verify the engineering feasibility of the theoretical architecture formed during the decoupling phase, assessing its feasibility and reliability under actual network and system conditions. It guides us on how to re-decouple and deploy in different ways, particularly driving architectural decisions such as "merging multiple components and sinking them as a whole to a lower level." The method is as follows: Determine whether the decoupling scheme or functional cluster is a relatively independent business unit and map it to the most appropriate deployment level.

[0088] Table 7 Cohesion Rating Table

[0089] For example, the automatic height adjustment of the coal mining machine drum (based on preset data from the coal seam top and bottom plate model, combined with the machine body attitude sensor, automatically adjusts the drum height to ensure that coal is picked up without cutting the top and bottom plates) uses data from the coal mining machine's own tilt sensor, position encoder, and preset top and bottom plate model (which has been downloaded to the edge). The controlled object is the hydraulic valve for lifting and lowering the coal mining machine drum.

[0090] All the necessary data comes from the equipment's own sensors; all control commands also act on the equipment's own actuators. The entire decision-making loop is completed within the coal mining machine, which is equipment-level cohesion. In this case, control is directly embedded in the equipment's own controller or implemented through a dedicated edge module located nearby, and should be deployed at the equipment and control layers.

[0091] For example, in the fully mechanized longwall face automation system (where the hydraulic supports automatically perform a series of actions such as retracting the side panels, lowering the supports, pulling the supports, raising the supports, and extending the side panels as the coal mining machine cuts forward), the data comes from the coal mining machine position, support pressure sensors, and support infrared signals (all located within the working face). The controlled objects are the solenoid valves of hundreds of hydraulic supports on the working face. This is a complete "coal mining-support" production link, where all data sources and actuators are closely distributed within the physical space of the working face, logically forming a high-speed, reliable collaborative closed loop, i.e., link-level cohesion. Therefore, it should be deployed at the professional subsystem layer.

[0092] For example, the intelligent collaborative operation of coal flow in mining areas (monitoring the coal flow rate and coal bunker level of all conveyor belts in the mining area, realizing "starting against the coal flow and stopping with the coal flow," and automatically adjusting the belt speed according to the load to achieve energy-saving operation) uses data sources such as the coal output of the fully mechanized mining face (coal mining subsystem), the weighing and speed of multiple conveyor belts (transportation subsystem), and the coal bunker level (coal bunker monitoring). The control objects are issuing speed adjustment suggestions to the coal mining machine (coal mining subsystem) and controlling the start, stop, and speed of multiple conveyor belts (transportation subsystem). It requires coordinating two different links, "coal mining" and "transportation," with the goal of achieving efficient and energy-saving operation of the entire mining area transportation system. It requires acquiring data across subsystems and sending coordination instructions to lower-level subsystems, which is regional cohesion. The core is "process linkage," and the ideal deployment level is the integrated automation system layer.

[0093] For example, the intelligent production scheduling system for the entire mine (based on multi-dimensional data such as geological models, equipment health status, energy costs, coal market prices, and transportation capacity, using big data and AI algorithms to formulate the optimal production plan for the next week or month and decompose it to each mining area) draws data from geological databases (geological data), equipment management systems (equipment health), financial systems (costs), sales systems (market prices), and integrated automation systems (real-time capacity). It does not directly control any equipment but influences the production plan, which is then distributed to various systems for execution, thus achieving global-level cohesion. This type of function serves the overall operation, strategic decision-making, and overall optimization of the entire mine, covering the entire mine, with a wide range of data sources and long decision-making cycles. Therefore, it should be deployed at the integrated management and control platform layer.

[0094] S4.4 Deployment Decision Matrix Based on Risk and Cohesion The above assessment results will be combined to form the final deployment decision, clearly specifying the target level and deployment model.

[0095] Table 8 Deployment Decision Matrix

[0096] It should be noted that while similar decoupling planning is used for functions during the intelligent construction of coal mines, this decoupling primarily focuses on function decomposition and computing power allocation. This results in situations where, after system completion, functions are either impractical due to network limitations and the complexity of the mining environment, or are not used due to safety and reliability concerns. Currently, a large number of functions in coal mine intelligent systems remain idle. This solution aims to address this issue, specifically addressing the following problems with the application of decoupling: (1) Coal mine network structures are complex (possibly containing multiple networks such as industrial Ethernet, Wi-Fi, 4 / 5G, and leaky communication), with limited transmission channels (winding roadways and equipment obstructions), severe electromagnetic interference, and the risk of temporary network outages due to production activities (such as blasting and roadway excavation). In this environment, real-time, high-reliability data transmission across levels (especially between surface platforms and underground equipment) is extremely fragile and high-risk. "Decoupling" in other fields often only focuses on functional decomposition and computing power allocation, lacking a quantitative assessment of the feasibility of the resulting "cross-level collaborative links" in harsh industrial environments.

[0097] (2) Coal mines have very high requirements for control safety. By deploying the decision matrix, once a high-risk (especially extremely high-risk) link is identified and the functional cluster has the characteristic of "link-level cohesion", the "on-site control center" mode is adopted to sink the function.

[0098] (3) After years of automation, informatization, and intelligentization, coal mines have accumulated a large number of subsystems from different manufacturers, with different technical standards, and built at different times. These systems have closed interfaces, proprietary protocols, and different data models. Forcing them to provide real-time data or execute instructions for a new function results in high integration complexity, huge costs, and poor stability. The decoupling approach of this invention is to respect the existing architecture, minimize cross-system interference, and reduce the coupling complexity between systems under this principle.

[0099] (4) In the early stages of intelligent coal mine construction, many design schemes remained at the theoretical level. A function drawn on the architecture diagram as "cloud" or "edge" may seem reasonable, but once implemented on-site, problems such as network latency, data loss, and failure to issue control commands in a timely manner are discovered, leading to project failure or significantly reduced effectiveness. This invention constructs a complete closed loop of "ideal partitioning → decoupled design → risk assessment → deployment decision". The core value of this closed loop is that it discovers and avoids risks that would only be exposed during the engineering implementation stage in advance during the system design stage.

[0100] Example 1 Taking the function of "intelligent video recognition of safety helmet wearing in coal mines and on-site broadcast alarm" as an example, the implementation process of this method is illustrated: (1) Ideal division: Analyzing this function, its data dependence (1 point, equipment data) and decision influence (1 point, single equipment) point to the equipment and control layer, but its decision generation mode (4 points, machine learning model) points to the comprehensive management and control platform layer, which is contradictory.

[0101] (2) Decoupling design: Decouple functions.

[0102] Decision execution unit (alarm): Data dependency 1 point, decision impact 1 point, decision pattern 1 point (fixed model). The ideal level is the equipment and control layer.

[0103] Decision generation unit (algorithm training): Data dependence 4 points (full mine sample), decision impact 2 points, decision pattern 4 points. The ideal level is the comprehensive management and control platform layer.

[0104] (3) Risk assessment: Two collaborative links were identified: “video data upload” and “alarm command issuance”. The assessment found that the “alarm command issuance” link was rated as high risk due to its high real-time and high reliability requirements and the serious consequences of failure.

[0105] (4) Scheme generation: Overall scheme OCRI and P HR All indicators showed high risk. Cohesion analysis of this functional cluster determined it to be "stage-level cohesion." Based on the decision matrix, the decision was made to deploy the "decision execution unit" of this function and its associated computations as a whole in the underground field control center (the edge computing device built into the intelligent camera) at the "professional subsystem layer." Algorithm training still occurs at the platform layer, but instead of real-time commands, periodically optimized models are issued. The new "model delivery" link has a "low" real-time requirement, reducing the risk to "acceptable." This resulted in a practical technical solution that balances intelligence and real-time performance while ensuring safety and reliability.

[0106] In summary, this application proposes a functional hierarchy quantification model based on fundamental attributes: for the first time, it proposes three dimensions as the objective basis for functional hierarchy division: "data dependency scope", "decision influence scope" and "decision generation mode", which replaces the traditional approach of relying on subjective experience and realizes the standardization and calculability of the division process.

[0107] A collaborative risk quantification assessment system for engineering feasibility: A dedicated risk assessment matrix for "cross-level collaborative links" has been created, which transforms abstract engineering risks into a combined risk value of "severity of consequences" and "real-time requirements", enabling early warning and precise location of architectural design risks.

[0108] A dynamic deployment decision-making mechanism based on risk and cohesion is proposed, which includes the "Overall Collaborative Risk Index (OCRI)" and the "Percentage of High-Risk Links (P)". HR As a solution-level evaluation indicator, it innovatively introduces the concept of "system functional cohesion" and strongly correlates it with the hierarchical architecture, forming a decision matrix that can dynamically recommend the optimal deployment mode (such as local equipment deployment, on-site control center, and regional control center) based on the risk assessment results, thus ensuring the smooth transformation of theoretical architecture into engineering practice.

[0109] Closed-loop feedback design process: The method flow of this invention constitutes a closed-loop system of design-evaluation-optimization. When the evaluation fails, the method provides a clear backtracking path and optimization strategy (such as re-decoupling or functional merging and sinking) to guide the design iteration until a robust solution is generated.

[0110] Example 2 This application provides a computer device including a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement a multi-dimensional quantization-based functional hierarchy division method for intelligent coal mine systems, as provided in the above method embodiments.

[0111] Computer equipment may constitute or include the apparatus or system provided in the embodiments of this application. Computer equipment may include one or more processors (processors may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), memory for storing data, and transmission devices for communication functions. In addition, it may include: a display, input / output interfaces (I / O interfaces), a universal serial bus (USB) port (which may be included as one of the ports in the I / O interface), a network interface, a power supply, and / or a camera.

[0112] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits can be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits can be a single, independent processing module, or integrated, in whole or in part, into any other element within a computer device (or mobile device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0113] The memory can be used to store software programs and modules of application software, such as the program instruction / data storage device corresponding to the functional hierarchy division method of a coal mine intelligent system based on multi-dimensional quantization in this embodiment of the application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-mentioned method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to computer devices via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0114] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer equipment. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0115] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows users to interact with the user interface of a computer device (or mobile device).

[0116] Example 3 This application embodiment also provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to implementing a method for classifying the functional hierarchy of an intelligent coal mine system based on multi-dimensional quantization in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method for classifying the functional hierarchy of an intelligent coal mine system based on multi-dimensional quantization provided in the above method embodiment.

[0117] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0118] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined by the scope of the claims.

Claims

1. A method for functional hierarchy division of an intelligent coal mine system based on multi-dimensional quantification, characterized in that, include, Step 1: Quantitatively score the three fundamental attributes of the function: data dependency scope, decision impact scope, and decision generation mode, to determine its initial ideal deployment level; Step 2: For functions with conflicting attributes, decouple them into decision generation units and decision execution units, and assign ideal levels to each to form a preliminary cloud-edge collaborative architecture. Step 3: Identify all cross-level collaborative links generated after decoupling, and quantify the risk level of each link from two dimensions: the severity of link failure consequences and the real-time and reliability requirements of the link. Step four: Calculate the overall collaborative risk index and risk density of the solution, and combine them with the system functional cohesion analysis. Through the deployment decision matrix, output the final feasible deployment solution.

2. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 1, characterized in that, In step two: S2.1 Decision Execution Unit: Based on its data dependency scope and decision impact scope, it is divided into an appropriate lower level; Decision Generation Unit: Based on its decision generation mode and required data scope, it is divided into an appropriate top level. S2.2 Decoupling Function Evaluation: Decouple complex functions into two clear smaller functions. If conflicting functions are found during evaluation, continue decoupling according to S2.1 until no more conflicts are found during evaluation. S2.3 Collaborative Design: After decoupling, a collaborative relationship of training-deployment or strategy-control is formed between the decision generation unit and the execution unit. The model, strategy or parameter are distributed and data is fed back through standardized interfaces.

3. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 1, characterized in that, The risk assessment matrix is ​​based on two dimensions: the consequence level A of link failure and the real-time and reliability requirements of the link. It divides the risk level R of each link into acceptable risk, low risk, medium risk, high risk and extremely high risk.

4. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 1, characterized in that, In step four, the overall collaborative risk index Where n is the total number of collaborative links in the scheme; R i The risk level value for the i-th link is recommended to be set according to the risk level R mentioned above, i.e., 0 is low risk, 1 is acceptable risk, 2 is medium risk, 3 is high risk, and 4 is extremely high risk; W is the weight of the i-th link, which is set by the link failure consequence level A, i.e., 1 is negligible, 2 is moderate, 3 is severe, and 4 is catastrophic.

5. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 1, characterized in that, In step four, the risk density , P HR = 0%, indicating an ideal scenario where there are no high-risk links in the solution; 0% < P HR ≤ 20% indicates that the risk is relatively isolated and the solution can be optimized through targeted measures; P HR A value greater than 20% indicates a high-risk density warning.

6. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 5, characterized in that, In step four, when a high-risk density warning is detected, the deployment level is optimized based on the system's functional cohesion. It is determined whether the decoupling scheme or functional cluster is a relatively independent business unit and mapped to the most suitable deployment level.

7. The method for functional hierarchy division of intelligent coal mine systems based on multi-dimensional quantification according to claim 6, characterized in that, The cohesion optimization deployment hierarchy includes device-level cohesion, process-level cohesion, region-level cohesion, and global-level cohesion.

8. A functional hierarchy division system for intelligent coal mine systems based on multi-dimensional quantification, characterized in that, include, The data acquisition and interface layer is used to receive and standardize various input information and import function lists. The core computing engine layer is connected to the data acquisition and interface layer. The core computing engine layer implements the functional hierarchy division method of the intelligent coal mine system based on the data of the data acquisition and interface layer as described in any one of claims 1-7. The decision support and human-computer interaction layer, together with the core computing engine layer, is used to intuitively display the calculation results of the core computing engine layer.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the functional hierarchy division method for intelligent coal mine systems based on multi-dimensional quantization as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the functional hierarchy division method for intelligent coal mine systems based on multi-dimensional quantization as described in any one of claims 1-7.

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