Distributed cache storage management system for coal transportation and washing data

Abstract

This invention relates to the field of data management and distributed caching technology in the coal industry, particularly a distributed cache storage management system for coal transportation and washing data. The system executes the following steps: S1, binding production data to a unique batch identifier of incoming raw coal and a coal flow characteristic tag; S2, constructing a spatiotemporally coupled cache index to complete cache data addressing mapping; S3, aggregating and storing data to corresponding cache nodes to complete differentiated cache partition deployment; S4, outputting a priority scheduling strategy; S5, outputting a cross-network synchronization control strategy; S6, dynamically adjusting the lifecycle of corresponding batch cache data and outputting lifecycle configuration rules; S7, iteratively updating to adapt to on-site production needs. When quality problems such as excessive ash content in refined coal occur, the entire process data can be traced back through batch identifiers to quickly locate abnormal links. Simultaneously, it provides a precise data foundation for subsequent cache aggregation and hierarchical scheduling, significantly improving the refined management level of coal washing production.

Description

Technical Field

[0001] This invention relates to the field of data management and distributed caching technology in the coal industry, and in particular to a distributed cache storage management system for coal transportation and washing data. Background Technology

[0002] In the context of coal transportation and washing, existing application solutions generally adopt core data processing logic such as static timestamp index mapping, fixed partition storage, access frequency-driven resource scheduling, and unified lifecycle management. This involves caching and partitioning data from the raw coal transportation process, including belt operation, coal quality testing, and feeding metering, with data from the washing process, including heavy media separation, equipment interlocking, and product quality inspection, according to data type or collection time sequence, and then synchronizing them to the backend storage.

[0003] However, this existing processing method has the following technical drawbacks in practical applications of coal transportation and washing:

[0004] On the one hand, existing technologies use static timestamps as the sole index for cached data, storing and retrieving data only according to the acquisition sequence. However, in coal transportation and washing scenarios, after raw coal undergoes ash, sulfur, and washability tests at the online belt conveyor detection point, it typically requires 3-8 minutes of belt transport, multi-stage transfer, and buffer silo caching before entering the heavy medium cyclone separator. On-site conditions such as belt slippage, feed rate fluctuations, and buffer silo position adjustments can cause a deviation of approximately ±60 seconds between the actual arrival time of the coal flow at the sorting equipment and the theoretical acquisition time.

[0005] This static timestamp index caching method cannot match the spatiotemporal displacement changes during coal transport, which will cause serious misalignment between the coal quality detection data retrieved from the cache and the actual batches of coal entering the sorting equipment. The accuracy of parameter matching for the feedforward control of heavy media separation will be greatly reduced, directly causing deviation in the density control of heavy media suspension, ultimately leading to a decrease in clean coal yield and an increase in the rate of excessive ash content in clean coal. Moreover, this problem cannot be solved by optimizing the conventional read and write speed of general cache.

[0006] On the other hand, the coal transportation and washing site has a three-tiered heterogeneous network consisting of a dedicated network for edge data collection along the transportation route, an internal industrial control network for the washing workshop, and a public network for the group's dispatch cloud platform. Network instability caused by strong electromagnetic interference in the washing workshop and signal fluctuations along the transportation route is a common occurrence on site. The existing scheduling and synchronization mechanism based on access frequency cannot match the industrial functional safety requirements and the operating characteristics of the heterogeneous network. When network fluctuations recover and the load on cache nodes suddenly increases, non-core, low-priority data with high access frequency will crowd out cache bandwidth and storage resources, causing the cache read / write latency of core hard real-time data to exceed the industrial control safety threshold. At the same time, the synchronization time difference of closed-loop control data between heterogeneous network nodes exceeds the limit, causing a mismatch between the transportation feed rate and the washing and sorting capacity, resulting in buffer bin overflow, unstable feeding of heavy medium cyclones, and even triggering equipment safety interlock malfunctions and unplanned production line shutdowns.

[0007] Based on this, a distributed cache storage management system adapted to the specific process conditions of coal transportation and washing is designed, which has both technical necessity and industrial application value. Summary of the Invention

[0008] To solve one of the aforementioned technical problems, the present invention employs a distributed cache storage management system for coal transportation and washing data, wherein the system performs the following steps:

[0009] S1. Collect production data of the entire process of coal transportation and washing, and after preprocessing, bind a unique batch identifier of raw coal entering the plant and a coal flow characteristic label to each data set, and output a labeled dataset.

[0010] S2. Receive the labeled dataset output from the previous steps, combine it with coal flow transportation conditions and spatial displacement data to construct a spatiotemporal coupled cache index, complete the cache data addressing mapping, and output the index mapping rules.

[0011] S3. Receive the index mapping rules and tagged dataset generated in the previous step, aggregate and store the transportation and washing data of the same batch of raw coal to the corresponding cache node, complete the differentiated cache partition deployment, and output the cache dataset after partition deployment.

[0012] S4. Based on the data attributes within the preceding cache partition, divide the cache read and write priorities according to the industrial functional safety level, configure the cache resource preemptive scheduling rules, and output the priority scheduling strategy.

[0013] S5. Based on priority scheduling strategy and cache partition deployment, match the heterogeneous network conditions of transportation network, washing and beneficiation network and group public network, set cross-node synchronization rules and directional pre-push mechanism for coal washing flow related data, and output cross-network synchronization control strategy.

[0014] S6. Track the entire production process progress of raw coal batches, combine priority scheduling strategies, dynamically adjust the lifecycle of corresponding batch cached data, complete cache invalidation control and compliance archiving, and output lifecycle configuration rules.

[0015] S7 monitors the on-site transportation and washing process and the running status of cache nodes in real time. Based on the preceding output rules, iteratively updates the index mapping rules, priority scheduling strategies, synchronization control strategies, and lifecycle configuration rules to adapt to on-site production needs.

[0016] Based on any of the above technical solutions, the following further optimization is made: the multi-source production data collected by the raw coal batch data tag binding module includes belt operation status data, feeding and metering data, online coal quality detection data upon arrival at the plant, vehicle transportation trajectory data, and transfer point operation data in the raw coal transportation process;

[0017] It also includes process control data for heavy media separation in the washing and beneficiation process, equipment safety interlock data, coal quality testing data, product quality inspection data, and coal slurry water treatment operation data;

[0018] The coal flow characteristic tags include coal flow source mine tag, coal quality washability grade tag, washing process type tag, data security grade tag, and data real-time grade tag. All tags form a one-to-one mapping and binding relationship with the unique batch identifier of raw coal entering the plant.

[0019] Based on any of the above technical solutions, a further optimization is made: when the coal flow spatiotemporal coupling index construction module constructs the spatiotemporal coupling cache index, it performs the following steps:

[0020] The first step is to obtain the coal quality testing point collection time, the conveying path parameters from the testing point to the feed inlet of the heavy medium cyclone, the real-time operating condition parameters of the belt conveyor, and the coal quality characteristic data for the corresponding batch of raw coal.

[0021] The second step is to calculate the matching degree between the coal quality test data and the corresponding coal batch entering the washing process by using the coal batch-cache index matching quantification formula, and use the matching degree as the core addressing weight of the cache index.

[0022] The third step is to bind the matching degree weight with the raw coal batch identifier, coal flow feature label, and coal flow predicted arrival time to generate a unique spatiotemporal coupled cache index key value, and complete the addressing mapping of cache data.

[0023] The coal flow batch-cached index matching metric formula is as follows:

[0024] (1);

[0025] In the formula:

[0026] The matching degree between coal quality test data and the corresponding batch of raw coal to be washed is the core addressing weight of the spatiotemporal coupling cache index. The value ranges from 0 to 1. The higher the matching degree, the higher the cache addressing priority.

[0027] The posterior probability of the actual arrival time of the coal flow at the sorting equipment is obtained by adapting the Bayesian probability model to the fluctuation of the coal flow transportation conditions.

[0028] The actual time it takes for the coal stream to reach the feed inlet of the heavy medium cyclone separator; : The time when coal quality data corresponding to the coal flow is collected at the online detection point of the conveyor belt; : A set of characteristic parameters for belt operation, including belt linear speed, load rate, and slippage coefficient;

[0029] Jaccard similarity coefficient between raw coal batch identifiers and coal flow characteristic tags is used to quantify the binding correlation between data and batches, and avoid cross-batch data index misalignment;

[0030] The set of unique batch identifiers for raw coal entering the plant; : The set of feature labels corresponding to coal flow data;

[0031] : Coal quality characteristic consistency correction coefficient, with a value range of 0.9~1.1. The parameter is determined based on the allowable fluctuation range of coal quality test data.

[0032] Based on any of the above technical solutions, the following optimization is made: The transportation-washing associated data aggregation and caching module strongly associates and binds the coal quality testing data, belt operation data, and feeding metering data of the same batch of raw coal with the heavy media separation process control data, feed parameter data, and product quality inspection data of the washing process, and aggregates and stores them in a dedicated partition of the same cache node; at the same time, it divides the cache into two-level partitions according to the washing process, namely the heavy media separation control partition, the equipment safety interlock partition, the transportation scheduling and control partition, and the coal quality traceability management partition, and sets up physically isolated access channels between each partition.

[0033] Based on any of the above technical solutions, the following optimization is made: When configuring the preemptive scheduling rules for cache resources in the washing and screening industrial safety hierarchical scheduling module, the priority score of each group of data in the cache partition is first calculated by the dynamic scoring formula of cache priority with hard constraints for industrial safety. Then, according to the order of scores from high to low, the allocation levels of cache bandwidth, storage resources, and read / write channels are divided, and the resource preemption permission of high-priority data is configured.

[0034] When the load on a cache node exceeds a set threshold or network bandwidth is limited, high-priority data can preempt the cache resources of low-priority data to ensure the read and write timeliness of core data.

[0035] The dynamic scoring formula for cache priority with hard constraints on industrial safety is as follows:

[0036] (2);

[0037] (2-1);

[0038] In the formula:

[0039] The read / write priority score for cached data, after being normalized by the Sigmoid function, ranges from 0 to 1. The higher the score, the higher the priority of cache resource allocation and scheduling.

[0040] The industrial functional safety level corresponding to the data is divided into 4 levels according to the Industrial Control System Functional Safety Assessment Standard, with corresponding values ​​of 4, 3, 2, and 1. The higher the safety level, the larger the value.

[0041] The process coupling degree between data and heavy media separation feedforward control, and transportation-washing capacity matching closed-loop control, is categorized according to the importance of each process control link, with a value ranging from 0 to 1. Higher coupling degrees result in larger values. Numerical positive correlation;

[0042] The real-time requirements for industrial control data are determined based on the data acquisition cycle specifications. The shorter the acquisition cycle, the higher the real-time requirements. The value range is 0~1.

[0043] , , : Weighted allocation coefficients, where The value is 0.5. The value is 0.3. The value is 0.2, and the coefficient value is determined based on the principles of industrial production control.

[0044] : Dedicated cache bandwidth reserved for the highest priority data of the equipment safety interlock; The total available bandwidth of the cache node is given by equation (2-1), which is a hard constraint for industrial security. Under no circumstances should low-priority data occupy this reserved resource.

[0045] Based on any of the above technical solutions, the following optimization is made: When the three-level heterogeneous network synchronization control module sets cross-node synchronization rules and directional pre-push mechanism, it first calculates the matching degree value between the data to be synchronized and the coal flow washing time sequence through the coal flow association data pre-push time sequence matching degree formula under heterogeneous network, and then matches the corresponding synchronization control strategy based on the matching degree value.

[0046] The formula for the time-series matching degree of pre-push coal flow correlation data under the heterogeneous network is:

[0047] (3)

[0048] In the formula:

[0049] The matching degree between the data to be pushed and the coal inflow washing time sequence, after cosine similarity normalization, takes a value of 0~1, which is used to trigger the targeted pre-push mechanism. The higher the matching degree, the higher the pre-push priority.

[0050] : Time series nodes within the time series window; The total number of nodes in the timing window, and the length of the timing window is determined according to the lead requirement of feedforward control for heavy medium sorting in GB / T35052-2018;

[0051] The progress feature vector of the coal flow arriving at the sorting equipment at time t is composed of the spatial displacement of the coal flow, the operating conditions of the belt conveyor, and the parameters of the feed bin. The core parameters come from the corresponding parameters of the spatiotemporal coupling index of the coal flow.

[0052] The time-series feature vector of the related data to be pushed at time t consists of data priority, process coupling degree, and single package data volume. The core priority parameter comes from... The calculation results;

[0053] : Transmission robustness correction coefficient for three-level heterogeneous networks, with a value range of 0 to 1. The parameter is determined based on the data transmission stability standards under different network environments.

[0054] The synchronization control strategy is as follows: when When, the cross-node related data targeted pre-push mechanism is triggered; when When, the dual-node redundancy backup synchronization mechanism is triggered; when When this happens, the local cache locking and breakpoint resume mechanism are triggered.

[0055] Based on any of the above technical solutions, the following further optimization is made: The batch full-cycle cache lifecycle management module tracks the progress of the entire production process of raw coal batches from incoming acceptance, belt conveying, washing and sorting, product quality inspection to finished product delivery. Through a batch cache lifecycle dynamic adaptation formula with traceability and compliance constraints, the dynamic lifecycle duration of the corresponding batch cache data is calculated, and the storage strategy of the cache data is adjusted synchronously. When the raw coal batch completes finished product delivery and full-process traceability archiving, the corresponding cache data invalidation cleanup and cold data archiving operations are triggered.

[0056] The dynamic adaptation formula for the batch cache lifecycle with traceability compliance constraints is as follows:

[0057] (4)

[0058] (4-1)

[0059] In the formula:

[0060] : The dynamic lifecycle duration of the corresponding raw coal batch cache data;

[0061] The basic lifecycle constant term is determined based on the retention requirements of production process data, and its value is 6.58, corresponding to a basic lifecycle of 720 hours.

[0062] : The completion progress of the entire production process of the raw coal batch, with a value range of 0 to 1. After the batch is completed, the finished product is shipped out and archived, the value is 1.

[0063] : The read / write priority score for the corresponding cached data;

[0064] The data traceability compliance level is divided into 3 levels according to the coal quality traceability management standard, with corresponding values ​​of 1, 0.5 and 0, and the core traceability data has a value of 1.

[0065] , , Life cycle adjustment factor, where The value is -1.2. The value is 0.5. The value is 0.8, and the coefficient is determined according to the coal industry production data traceability management standard. The earlier the batch progress, the higher the priority, the higher the compliance level, and the longer the life cycle.

[0066] Random error term, with a value range of ±0.05, is used to adapt to small fluctuations in on-site working conditions;

[0067] The minimum retention time for cached data is determined to be 168 hours according to the information management standards of the coal industry. Equation (4-1) is a hard constraint for compliance.

[0068] Based on any of the above technical solutions, the following optimization is made: the preemptive scheduling rules configured in the washing and screening industrial safety hierarchical scheduling module set a cache read and write latency threshold. When the read and write latency of core control data exceeds the 10ms industrial control safety threshold specified in MT / T1097-2008, the read and write pause and resource release operations of low-priority data are automatically triggered to ensure core safety and the timeliness of read and write control data.

[0069] The cache read / write latency threshold setting in this solution is based on clear industry standards. According to the response time requirements for coal mine electromechanical equipment monitoring systems, the standards clearly stipulate that the system response time for core data related to equipment safety interlocks and production closed-loop control must not exceed 10ms. Therefore, this solution sets the cache read / write latency threshold for core control data to 10ms. The scope of core control data in this solution is clearly defined, falling within the industrial functional safety level. Data with values ​​of 4 and 3 include equipment safety interlocks, emergency stop control data, heavy media sorting closed-loop control data, and transportation-washing capacity matching closed-loop control data. This range corresponds completely to the safety level classification. Those skilled in the art can directly determine the range of core control data based on the safety level of the data without additional identification and screening.

[0070] Based on any of the above technical solutions, the following further optimization is made: the working condition adaptation closed-loop optimization module monitors the belt running condition, the washing and screening equipment operating status, the signal status of the three-level heterogeneous network, and the load and operating status of the buffer nodes in real time. When changes in working conditions such as belt slippage, feed fluctuation, network signal abnormality, or buffer node overload are detected, the module automatically iteratively updates the correction parameters of the spatiotemporal coupling index, priority scheduling rules, synchronization control strategies, and lifecycle configuration to achieve real-time adaptation between the buffer management strategy and the on-site production conditions.

[0071] Based on any of the above technical solutions, a further optimization is made: the spatiotemporal coupling cache index key value generated by the coal flow spatiotemporal coupling index construction module forms a one-to-one mapping with the control cycle of the heavy medium separation feedforward control system. Each control cycle corresponds to a set of exclusive index key values, ensuring that the coal quality data retrieved by the feedforward control is completely matched with the actual coal flow entering the washing process.

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] 1. This invention uses the unique identifier of raw coal batch as the core link. Through the raw coal batch data tag binding module, it configures exclusive feature tags for multi-source heterogeneous data throughout the process. Combined with the dynamic indexing mechanism of the coal flow spatiotemporal coupling index construction module, it realizes the association and binding of the transportation data and washing data of the same raw coal batch, ensuring that the coal quality data retrieved by the heavy media separation feedforward control system is completely matched with the actual coal flow batch entering the separation equipment.

[0074] The binding mechanism of this invention provides a complete data link for batch-wide traceability. When quality problems such as excessive ash content in refined coal occur, the entire process data can be traced back through batch identifiers to quickly locate abnormal links. At the same time, it provides a precise data foundation for subsequent cache aggregation and hierarchical scheduling, significantly improving the level of refined management in coal washing and processing.

[0075] 2. The security-first cache resource management system constructed in this invention effectively avoids the production safety risks caused by the prioritization of core control data resources due to access frequency-based scheduling in existing technologies. This invention utilizes an industrial safety-level scheduling module with a priority dynamic scoring formula incorporating hard industrial safety constraints. It takes industrial functional safety level as the core influencing factor for priority scoring, while reserving no less than 30% of dedicated bandwidth for core security data and configuring resource preemption permissions for high-priority data. When the cache node load exceeds limits or network bandwidth is limited, core security and control data can preempt low-priority data resources, ensuring that its read / write latency remains below the 10ms industrial control safety threshold.

[0076] By linking three-level heterogeneous network synchronous control and batch full-cycle cache lifecycle management modules, we can achieve full-process resource control with industrial functional safety level as the core, which is in line with the coal industry production safety control principle, effectively avoids risks such as safety interlock malfunction and unplanned equipment shutdown, and ensures safe and stable production operation.

[0077] 3. This invention uses a working condition adaptation closed-loop optimization module to monitor the working conditions of the belt conveyor, washing and screening equipment, three-level heterogeneous network and cache nodes in real time. When common working condition changes occur, such as belt slippage, feed rate fluctuation, network signal abnormality, and node overload, the invention automatically iterates and updates the spatiotemporal coupling index correction parameters, priority scheduling rules, cross-network synchronization control strategies and cache lifecycle configuration, so as to achieve real-time matching between the cache system and the field working conditions without manual intervention.

[0078] Compared to existing technologies that only target local optimizations of the cache node itself, the closed-loop optimization mechanism of this invention covers the entire process of cache indexing, storage, scheduling, synchronization, and lifecycle management. Through the coordinated linkage of various modules, it ensures that the cache service is always adapted to production needs, greatly improves the operational stability and adaptability of the cache system, and reduces manual operation and maintenance costs.

[0079] 4. This invention optimizes cache storage and data transmission efficiency, and significantly improves the control accuracy and production efficiency of coal washing and processing while ensuring compliant data retention.

[0080] This invention utilizes a transportation-washing associated data aggregation and caching module, employing a same-node affinity scheduling mechanism, to aggregate and store cross-stage strongly correlated data from the same batch of raw coal into the same cache node. This reduces the number of routing jumps for cross-node correlation queries to zero, significantly reducing data retrieval latency and improving cache hit rate. Furthermore, through the time-series matching mechanism of the three-level heterogeneous network synchronization and management module, targeted pre-pushing is only performed on data that is strongly correlated with the coal inflow washing time, greatly reducing the bandwidth consumption of the three-level heterogeneous network.

[0081] Meanwhile, by using the dynamic adaptation formula of the batch full-cycle cache lifecycle management module, combined with the hard constraints of traceability and compliance, the lifecycle of cached data is dynamically adjusted. This not only meets the information management standards of the coal industry, but also releases a large amount of hot cache resources, preventing non-core data from occupying resources for a long time.

[0082] In addition, the one-to-one mapping between index key values ​​and feedforward control cycles for heavy media sorting significantly reduces the data retrieval delay of the control system, improves the response speed and stability of feedforward control, and helps to increase the yield of clean coal and reduce the ash content exceeding the standard. Attached Figure Description

[0083] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0084] Figure 1 This is a flowchart of the distributed cache storage management system for coal transportation and washing data of the present invention.

[0085] Figure 2 The flowchart illustrates the construction of a spatiotemporal coupled cache index for the coal flow spatiotemporal coupled index construction module of this invention. Detailed Implementation

[0086] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific process of the present invention is as follows: Figures 1-2 As shown in the image.

[0087] Example 1: A distributed cache storage management system for coal transportation and washing data, including a raw coal batch data tag binding module, a coal flow spatiotemporal coupling index construction module, a transportation-washing related data aggregation cache module, a washing industry safety hierarchical scheduling module, a three-level heterogeneous network synchronization control module, a batch full-cycle cache lifecycle management module, and also includes a working condition adaptation closed-loop optimization module;

[0088] The output of the preceding module serves as the core input of the subsequent module and uses an industrial communication protocol for bidirectional interaction. The working condition adaptation closed-loop optimization module interacts bidirectionally with the other six modules using an industrial communication protocol.

[0089] A distributed cache storage management method for coal transportation and washing data based on a distributed cache storage management system comprises the following steps:

[0090] S1, Raw Coal Batch Data Label Binding Module, is used to collect multi-source heterogeneous production data of the entire process of coal transportation and washing. After completing the standardized preprocessing, it binds a unique raw coal batch identifier and coal flow characteristic label to each group of data and outputs a labeled dataset.

[0091] S2, the coal flow spatiotemporal coupling index construction module, is used to receive the labeled dataset output from the previous steps, combine the coal flow transportation conditions and spatial displacement data, construct a spatiotemporal coupling cache index that is bound to the raw coal batch and matches the coal flow from the detection point to the sorting equipment, complete the cache data addressing mapping, and output the index mapping rules.

[0092] S3, Transportation-Washing Related Data Aggregation and Cache Module, is used to receive the index mapping rules and labeled datasets generated in the previous step, aggregate and store the transportation and washing strongly related data of the same batch of raw coal to the corresponding cache node, complete the differentiated cache partition deployment, and output the cache dataset after partition deployment;

[0093] S4, the safety classification and scheduling module for the washing and sorting industry, is used to classify cache read and write priorities according to the functional safety level of the washing and sorting industry based on the data attributes in the cache partition that has been deployed in the previous step, configure cache resource preemptive scheduling rules, and output priority scheduling strategies.

[0094] S5, the three-level heterogeneous network synchronization control module, is used to match the on-site heterogeneous network conditions of the transportation network, the washing and beneficiation network, and the group's public network based on the priority scheduling strategy and cache partition deployment configured in the previous step. It sets cross-node synchronization rules and a targeted pre-push mechanism for coal washing flow related data, and outputs cross-network synchronization control strategies.

[0095] S6, Batch Full-Lifecycle Cache Lifecycle Management Module, is used to track the progress of the entire production process of raw coal batches from entering the plant to leaving the finished product warehouse. Combined with the priority scheduling strategy output by the preceding sequence, it dynamically adjusts the lifecycle of the corresponding batch cache data, completes cache invalidation control and compliance archiving, and outputs lifecycle configuration rules.

[0096] S7, the working condition adaptation closed-loop optimization module, is used to monitor the on-site transportation and washing working conditions and the running status of cache nodes in real time. Based on the output rules of the preceding modules, it iteratively updates the index mapping rules, priority scheduling strategies, synchronization control strategies and lifecycle configuration rules to ensure that the cache service is stably adapted to the on-site production needs.

[0097] The execution chain of this system's modules is as follows: raw coal batch data tag binding module → coal flow spatiotemporal coupling index construction module → transportation-washing associated data aggregation and caching module → washing industry safety hierarchical scheduling module → three-level heterogeneous network synchronous control module → batch full-cycle cache lifecycle management module. The working condition adaptation closed-loop optimization module serves as the global feedback level and establishes bidirectional signal connections with the above six modules, forming a complete closed-loop architecture of forward execution and reverse optimization. The signal connections between modules adopt the OPCUA industrial Ethernet communication protocol commonly used in coal preparation plants.

[0098] Secondly, the input to the raw coal batch data tagging module in step S1 is the full-process production data of transportation and washing that can be directly obtained from the existing acquisition system of the coal preparation plant. The labeled dataset output is the sole core input of step S2. The index mapping rules output by step S2 and the labeled dataset from step S1 together serve as the core input of step S3. The partitioned cache dataset output by step S3 serves as the core input of step S4. The priority scheduling strategy output by step S4 also serves as the core input of steps S5 and S6. All rules and strategies output by steps S1-S6 serve as the input of step S7. The optimized rules output by step S7 are then used to update the corresponding modules, achieving dynamic adaptation of the entire system. Thirdly, the data standardization preprocessing in step S1 removes outlier data using the 3σ criterion, unifies dimensions using minimum-maximum normalization, and standardizes data using the JSON universal format. The operating condition and node status monitoring in step S7 uses real-time data acquisition and threshold judgment methods, and all monitoring parameters are directly obtained from the existing SCADA system of the coal preparation plant.

[0099] Existing technologies for caching optimization in coal mining scenarios only perform routine adaptations to single stages of data acquisition and transmission, never binding the caching management architecture to the entire coal transportation-washing production process. Specifically, the batch tag binding in step S1 provides a unique data association link for all subsequent modules, forming the foundation for full-process batch control; the spatiotemporal coupling index construction in step S2 directly solves the coal flow data misalignment problem caused by the static timestamp index in existing technologies, while also providing addressing basis for the associated data aggregation in step S3; the associated data aggregation caching in step S3 provides the physical basis for partitioned control of hierarchical scheduling in step S4; the safety hierarchical scheduling in step S4 also provides core priority basis for heterogeneous network synchronization in step S5 and lifecycle management in step S6; the execution results of steps S5 and S6 provide optimization targets for closed-loop optimization in step S7; the closed-loop optimization in step S7 iterates backward to update the configuration rules of all preceding modules, achieving real-time adaptation of the entire system to on-site operating conditions.

[0100] This solution achieves precise binding of coal flow data and batches entering the washing process throughout the entire coal transportation and washing process. Using the unique identifier of the raw coal batch as the core link of the entire process data, through the progressive control of the entire link from data acquisition tag binding, to spatiotemporal index construction, and then to associated data aggregation and caching, it ensures that the transport coal quality data, operating condition data, and washing process data of the same raw coal batch are always in a strongly correlated binding state. The coal quality data retrieved by the heavy media separation feedforward control system is completely matched with the actual coal flow batch entering the separation equipment. This effect cannot be achieved by general distributed caching architecture because general caching architecture only focuses on data read and write efficiency and does not design a dedicated full-link binding for the spatiotemporal displacement characteristics of coal flow in industrial scenarios. This solution implements security-priority cache resource management, avoiding the security risks of core control data resources being squeezed out due to access frequency scheduling in existing technologies. By using the industrial safety graded scheduling module as the core, and linking heterogeneous network synchronization and lifecycle management modules, a resource management system based on industrial functional safety levels is constructed. This breaks the inherent logic of frequency-priority access to general Internet caches, ensuring that core data such as equipment security interlocks and closed-loop control can occupy cache bandwidth, storage resources, and read / write channels under any operating conditions. This conforms to the principles of industrial production safety management and control, an effect that general cache architectures do not possess.

[0101] This solution achieves dynamic adaptive adaptation of the caching architecture to on-site production conditions, solving the technical problem that existing static caching configurations cannot adapt to fluctuations in on-site conditions. Through a closed-loop optimization module for condition adaptation, it monitors on-site conditions such as conveyor belt operation, network status, and cache node load in real time. When common changes in operating conditions occur, such as conveyor belt slippage, network fluctuations, or node load exceeding limits, it automatically iterates and updates the configuration rules of all preceding modules, achieving real-time matching between the caching architecture and on-site conditions without manual intervention, thus realizing dynamic adaptation. This solution also utilizes a batch full-cycle cache lifecycle management module to track the entire production process of raw coal batches from arrival at the plant to finished product delivery, dynamically adjusting the cache lifecycle of corresponding data. This ensures that the retention time of core traceability data fully complies with the information management standards of the coal industry, while preventing non-core data from occupying cache resources for extended periods.

[0102] Based on any of the above technical solutions, the following further optimization is made: the multi-source production data collected by the raw coal batch data tag binding module includes belt operation status data, feeding and metering data, online coal quality detection data upon arrival at the plant, vehicle transportation trajectory data, and transfer point operation data in the raw coal transportation process;

[0103] It also includes process control data for heavy media separation in the washing and beneficiation process, equipment safety interlock data, coal quality testing data, product quality inspection data, and coal slurry water treatment operation data;

[0104] The coal flow characteristic tags include coal flow source mine tag, coal quality washability grade tag, washing process type tag, data security grade tag, and data real-time grade tag. All tags form a one-to-one mapping and binding relationship with the unique batch identifier of raw coal entering the plant.

[0105] In this scheme, the coal flow source mine label is directly assigned a value based on the mine source information when the raw coal enters the plant; the coal quality washability grade label is divided into five levels—extremely easy to wash, easy to wash, moderately washable, difficult to wash, and extremely difficult to wash—based on the coal washability assessment method, and a corresponding value is assigned; the washing process type label is directly assigned a value based on the actual process type used by the coal preparation plant, such as heavy media separation, jigging separation, and flotation; the data security level label is divided into four levels according to the coal industry data security management standards, and a corresponding value is assigned; the data real-time level label is divided into four levels—10ms, 100ms, seconds, and minutes—and a corresponding value is assigned. Furthermore, the one-to-one mapping and binding relationship explicitly defined in this scheme is implemented using the database primary key-foreign key association method known in the art. A unique batch identifier of the raw coal entering the plant is used as the primary key, and all coal flow characteristic labels are used as foreign keys to establish a one-to-one correspondence, stored in the existing real-time database of the coal preparation plant. Those skilled in the art can directly implement this binding relationship using well-known SQL statements.

[0106] The core concept of this technical solution is unique in that it designs a dedicated data binding mechanism based on the multi-source heterogeneous data characteristics of coal transportation and washing scenarios. This mechanism uses raw coal batch identification as the core and multi-dimensional feature tags as auxiliary. The working principle is to define the scope of data collection for the entire process, covering the entire chain of data dimensions from raw coal transportation to finished product delivery. At the same time, it configures feature tags for each data set that are deeply bound to production processes, safety standards, and industry requirements. Finally, it establishes a one-to-one mapping relationship between batch identification and tags, realizing accurate classification and strong correlation binding of data throughout the entire process. This provides a precise data foundation for subsequent cache index construction, aggregated storage, and hierarchical scheduling.

[0107] This solution clearly defines the scope of data collection throughout the entire process, ensuring that no data is missed from the entire chain of raw coal batches from entry into the plant to finished product delivery, providing complete data support for subsequent batch-wide traceability. The multi-dimensional feature tags designed in this solution form a precise synergy with subsequent modules: coal quality selectivity grade tags and washing process type tags provide coal quality characteristic parameters for the construction of the spatiotemporal coupling index of the coal flow, directly improving the matching degree between the index and the washed coal flow; data security grade tags provide security level parameters for the priority division of the safety grading and scheduling module in the coal washing industry, directly supporting safety-first scheduling rules; and data real-time grade tags are used for priority scoring calculations. The time-series matching degree calculation provides real-time parameters, directly ensuring the timeliness of reading and writing core data; the coal flow source mine tag provides basic parameters for batch traceability compliance level classification, directly supporting compliance control; and the one-to-one mapping binding relationship established by this solution ensures a strong association between all tags and raw coal batch identifiers, providing a unique core link for batch-based control throughout the entire process. All technical features and upstream and downstream modules form an interlocking synergy, jointly serving the overall concept of achieving accurate binding of data throughout the entire process with raw coal batches and ensuring deep adaptation of the cache architecture and production process. The technical effect brought about by its synergy far exceeds the effect of conventional data tag binding.

[0108] This technical solution uses a unique batch identifier for incoming raw coal as the primary key. All data from the entire process of raw coal transportation, belt conveying, washing and sorting, and product quality inspection are bound to this batch identifier, ensuring that the transportation data and washing data of the same batch of raw coal can be quickly and accurately linked through the batch identifier.

[0109] This solution utilizes a multi-dimensional feature tagging system to pre-classify and pre-define data attributes, significantly improving the efficiency and accuracy of subsequent cache management. During the data acquisition and preprocessing stage, each data set is configured with multi-dimensional feature tags representing process, security, real-time performance, and compliance. Subsequent cache index construction, partitioned storage, hierarchical scheduling, synchronization control, and lifecycle management can all directly call the tag parameters to complete the corresponding rule configuration, eliminating the need for secondary attribute identification and classification. This significantly reduces the computational load on the cache system and improves the response speed of cache management. This effect is unattainable with existing conventional data binding methods because existing technologies only use tags for data retrieval and do not deeply bind tags to the entire cache management process. This design differs significantly from conventional technological thinking.

[0110] The labeling system is deeply integrated with national and industry standards in the coal industry: the classification, grading, and value assignment standards of all labels directly correspond to the current national and industry standards in the coal industry. Whether it is the production control system and scheduling system within the coal preparation plant, or the group-level management system and traceability system, data attributes can be identified through standardized labels, achieving cross-system data compatibility and interoperability. The one-to-one mapping and binding relationship of this solution provides complete data link support for subsequent full-process traceability of coal quality, completely solving the problem of the inability to quickly and accurately trace the source when coal quality is abnormal in existing technologies: when quality problems such as excessive ash content in clean coal or abnormal sorting effect occur, the batch identifier of the finished product can be used to trace back to the corresponding batch identifier of the raw coal entering the plant, and then the transportation, washing, and testing data of the entire process of that batch can be retrieved through the batch identifier to quickly locate the abnormal link and achieve full-chain reverse traceability from finished product to raw coal.

[0111] Based on any of the above technical solutions, a further optimization is made: when the coal flow spatiotemporal coupling index construction module constructs the spatiotemporal coupling cache index, it performs the following steps:

[0112] The first step is to obtain the coal quality testing point collection time, the conveying path parameters from the testing point to the feed inlet of the heavy medium cyclone, the real-time operating condition parameters of the belt conveyor, and the coal quality characteristic data for the corresponding batch of raw coal.

[0113] The second step is to calculate the matching degree between the coal quality test data and the corresponding coal batch entering the washing process by using the coal batch-cache index matching quantification formula, and use the matching degree as the core addressing weight of the cache index.

[0114] The third step is to bind the matching degree weight with the raw coal batch identifier, coal flow feature label, and coal flow predicted arrival time to generate a unique spatiotemporal coupled cache index key value, and complete the addressing mapping of cache data.

[0115] The coal flow batch-cached index matching metric formula is as follows:

[0116] (1);

[0117] In the formula:

[0118] The matching degree between coal quality test data and the corresponding batch of raw coal to be washed is the core addressing weight of the spatiotemporal coupling cache index. The value ranges from 0 to 1. The higher the matching degree, the higher the cache addressing priority.

[0119] The posterior probability of the actual arrival time of the coal flow at the sorting equipment is obtained by adapting the Bayesian probability model to the fluctuation correction of the coal flow transportation conditions. It is used for static collection of timestamps, and the parameter calculation is determined according to the condition fluctuation correction standard specified in the belt conveyor operation condition monitoring method.

[0120] The actual time it takes for the coal stream to reach the feed inlet of the heavy medium cyclone separator; : The time when coal quality data corresponding to the coal flow is collected at the online detection point of the conveyor belt; : A set of characteristic parameters for belt operation, including belt linear speed, load rate, and slippage coefficient;

[0121] Jaccard similarity coefficient between raw coal batch identifiers and coal flow characteristic tags is used to quantify the binding correlation between data and batches, and avoid cross-batch data index misalignment;

[0122] The set of unique batch identifiers for raw coal entering the plant; : The set of feature labels corresponding to coal flow data;

[0123] : Coal quality characteristic consistency correction coefficient, with a value range of 0.9~1.1. The parameter is determined based on the allowable fluctuation range of coal quality test data.

[0124] The posterior probability of the actual arrival time of coal flow in this scheme The calculation employs a Bayesian posterior probability model, well-known in the field. The method for determining this model is as follows: first, based on the belt operation condition monitoring method specified in MT / T877-2000, determine the set of operating parameters for belt linear speed, load rate, and slippage coefficient. Then, the posterior probability of the actual arrival time of the coal flow is calculated using Bayes' theorem. The prior probability is determined based on historical operating data of the coal preparation plant's belt conveyor system, and the likelihood probability is determined based on the operating condition fluctuation correction standard specified in MT / T877-2000. The Bayesian probability model is a probability and statistical model known to those skilled in the art and can be directly implemented using well-known probability calculation tools such as Python and Matlab. The Jaccard similarity coefficient... The calculation employs the Jaccard similarity algorithm, well-known in the field. The method for determining this similarity is as follows: calculate the ratio of the intersection to the union of the raw coal batch identifier set and the coal flow feature tag set, using the formula: The algorithm is a well-known method for calculating set similarity in the field and can be directly reproduced; third, the coal quality characteristic consistency correction coefficient. The determination method is as follows: based on the allowable fluctuation range of coal quality test data in GB / T15224.1-2010, when the fluctuations of ash and sulfur content in coal quality test data are within the allowable range of the national standard, The value is 1.0; when the fluctuation exceeds the allowable range but does not exceed 20%, the value is adjusted linearly according to the fluctuation amplitude, ranging from 0.9 to 1.1, and is determined entirely in accordance with national standards and specifications.

[0125] The model hierarchy of this scheme is divided into three progressive levels: The first level is the data input layer, with the core input being the labeled dataset output from step S1, as well as the conveying path parameters and belt operating parameters collected by the existing system of the coal preparation plant. All input data are publicly available. The second level is the algorithm calculation layer, with the core being the matching metric calculation of the coal flow batch-cached index matching metric formula. After the a posteriori probability calculation, Jaccard similarity calculation, and correction coefficient matching, the matching degree value is finally output. The execution order of the levels is clear. The third level is the index output layer, which binds the matching degree weight with the batch identifier, feature label, and predicted arrival time to generate a unique spatiotemporal coupled cache index key. The output index mapping rule is directly used as the core input of the transportation-washing associated data aggregation cache module in step S3. The input-output relationship is completely closed loop.

[0126] The algorithm in this solution is integrated with the coal transportation and washing scenario as follows: the core inputs of the algorithm are production condition data that can be directly obtained from the coal preparation plant. The output of the algorithm directly serves the coal flow-data precision matching requirements of the heavy media separation feedforward control. The algorithm corrects the coal flow arrival time through a Bayesian probability model, directly adapting to common operating conditions in the coal transportation scenario such as belt slippage and feed rate fluctuations. The Jaccard similarity coefficient is used to strengthen the binding relationship between data and batches, directly adapting to the batch production characteristics of the coal washing scenario. Those skilled in the art can directly adapt the algorithm to the scenario based on the on-site parameters of the coal preparation plant.

[0127] This technical solution takes raw coal batch identification as its core, corrects the actual arrival time of coal flow through a Bayesian probability model, replaces the static data collection timestamp of the existing technology, strengthens the binding correlation between data and batch through Jaccard similarity coefficient, and adapts the coal quality fluctuation characteristics through coal quality consistency correction coefficient. Finally, the coal flow batch matching degree value is calculated and used as the core addressing weight of the cache index to generate a spatiotemporally coupled cache index that is dynamically adapted to the coal flow transportation process. This fundamentally solves the technical problem of misalignment between coal quality data and coal flow batch caused by the static index of the existing technology.

[0128] Existing technologies for optimizing cache indexes in industrial scenarios only optimize the conventional format of static timestamps. They fail to address the spatiotemporal displacement characteristics of coal flow in coal transportation scenarios, and do not design spatiotemporally coupled indexes based on dynamic adjustments under operating conditions. Furthermore, they do not deeply integrate cache indexes with the process requirements of feedforward control for heavy media sorting. In contrast, the index construction mechanism of this solution addresses the technical shortcomings of static timestamp indexes, which cannot adapt to fluctuations in operating conditions and lead to misalignment of coal flow data. It works synergistically with other technical features. Specifically, the batch matching degree value calculated by this solution serves as the core addressing weight of the cache index, directly determining the addressing logic of the S3 step's associated data aggregation cache, ensuring accurate aggregation and storage of strongly correlated data from the same batch. The spatiotemporally coupled cache generated by this solution... The index key value forms a one-to-one mapping with the control cycle of the feedforward control system for heavy media sorting, directly improving the matching accuracy of the feedforward control parameters and ensuring the stability of the sorting process. The Bayesian posterior probability calculation in this scheme works in conjunction with the working condition adaptation closed-loop optimization module in step S7. When the on-site conveyor belt working conditions change, the posterior probability and matching degree values ​​can be updated in real time to achieve dynamic adaptation between the index and the on-site working conditions. The Jaccard similarity coefficient calculation works in conjunction with the feature label system. Based on the defined batch identifier and feature label, it accurately quantifies the correlation between data and batch, avoiding cross-batch data index misalignment. The coal quality consistency correction coefficient in this scheme works in conjunction with the coal quality sortability grade label. Based on the coal quality fluctuation characteristics, it corrects the matching degree to ensure the adaptability of the index to coal quality characteristics.

[0129] By using the Jaccard similarity coefficient, the correlation between coal flow data feature tags and raw coal batch identifiers is quantified. Only data that is strongly correlated with the corresponding batch identifier can obtain a higher matching weight and a higher priority in cache addressing. This avoids confusion and misalignment of cross-batch data from the indexing mechanism level, ensuring the integrity and accuracy of batch-wide traceability data. This effect cannot be achieved by existing conventional indexing mechanisms because existing indexes only focus on the data collection time and device number, and do not design a dedicated addressing weight mechanism for the correlation of raw coal batches.

[0130] The index construction mechanism of this solution is deeply integrated with the core coal washing process, directly realizing the positive support of the caching architecture for the sorting process: the generated spatiotemporally coupled caching index key value is mapped one-to-one with the control cycle of the heavy media sorting feedforward control system. Each sorting control cycle corresponds to a set of exclusive index key values. The feedforward control system can accurately retrieve the coal quality detection data of the corresponding coal flow through the index key value without the need for additional data filtering and matching, which greatly reduces the calculation latency of the feedforward control system and improves the control response speed. This effect cannot be achieved by the existing general caching index mechanism because the general caching index is not deeply integrated with the control cycle of industrial production and cannot directly serve the control needs of the core process.

[0131] When the on-site conveyor belt conditions and coal quality characteristics change, the batch matching degree value and index key value can be updated in real time. The index can be dynamically adapted to the on-site conditions without manual intervention. At the same time, it works in conjunction with the on-site condition adaptation closed-loop optimization module to achieve continuous iterative optimization of index rules and dynamic updates in response to fluctuations in on-site conditions.

[0132] Based on any of the above technical solutions, the following optimization is made: The transportation-washing associated data aggregation and caching module strongly associates and binds the coal quality testing data, belt operation data, and feeding metering data of the same batch of raw coal with the heavy media separation process control data, feed parameter data, and product quality inspection data of the washing process, and aggregates and stores them in a dedicated partition of the same cache node; at the same time, it divides the cache into two-level partitions according to the washing process, namely the heavy media separation control partition, the equipment safety interlock partition, the transportation scheduling and control partition, and the coal quality traceability management partition, and sets up physically isolated access channels between each partition.

[0133] In this solution, the strongly correlated data aggregation storage of the same raw coal batch is achieved using the well-known same-node affinity scheduling mechanism in the field of distributed caching. Specifically, the generated spatiotemporal coupled cache index key value is used as the scheduling basis. Through the consistent hashing algorithm of the distributed cache, all data corresponding to the same raw coal batch identifier are scheduled to be stored in the dedicated partition of the same cache node. The consistent hashing algorithm is a well-known node scheduling algorithm in the distributed cache architecture field and can be directly implemented through the native functions of mainstream distributed cache components such as Redis and Memcached. The strong correlation between transportation data and washing data in this solution is achieved through a unique raw coal batch identifier. All data carries the same batch identifier, forming a correlated dataset with the batch identifier as the core within the cache partition. Those skilled in the art can directly implement this binding relationship using well-known cache key-value matching rules.

[0134] This technical solution addresses the production characteristics of cross-stage linkage between coal transportation and washing. It designs a two-layer aggregation caching mechanism with raw coal batches as the core and process links as auxiliary. The working principle is to aggregate and store strongly correlated data of the same batch across transportation and washing stages to the same cache node, using the raw coal batch identifier as the link, thus avoiding the delay caused by cross-node data retrieval. At the same time, it divides the data into two-level isolation partitions according to the core washing process, realizing partition control and access isolation of data with different functions. From the perspective of cache storage architecture, it is adapted to the strongly correlated data access characteristics of the coal transportation-washing linkage scenario.

[0135] Specifically, this solution aggregates and stores data from the same batch on the same node, working in conjunction with a spatiotemporally coupled cache index. Based on the generated index key value, it achieves precise node scheduling, ensuring that related data from the same batch are stored on the same node. This reduces the number of routing jumps for cross-node related queries from more than 3 in existing technologies to 0, significantly reducing query latency and improving cache hit rate.

[0136] The secondary process zoning in this solution works in conjunction with the industrial safety classification and scheduling module for coal washing and beneficiation. Different secondary zoning zones correspond to different industrial functional safety levels, and corresponding cache resource scheduling rules can be directly configured based on the zoning zones, eliminating the need for priority identification of individual data sets and significantly improving scheduling efficiency. The physical isolation access channels of this solution for each zone work in conjunction with hard safety constraints. Independent isolation channels are set up for equipment safety interlocking zones to ensure that the reading and writing of core safety data is not interfered with by data from other zones, guaranteeing the timeliness of reading and writing safety interlocking data. The coal quality traceability management zone in this solution works in conjunction with the batch full-cycle cache lifecycle management module, allowing the setting of exclusive lifecycle rules for traceability zones to ensure the compliant retention of traceability data.

[0137] This solution significantly reduces cross-node access latency for coal transportation and washing data with strong correlation, and substantially improves cache hit rate: Through a same-node affinity scheduling mechanism, all transportation and washing data with strong correlation for the same batch of raw coal are aggregated and stored in a dedicated partition of the same cache node. When the heavy media separation feedforward control system and production scheduling system need to retrieve cross-stage correlation data of the same batch, there is no need to perform routing jumps and data retrieval across multiple cache nodes. All correlation data can be read within the same node, thus improving the cache hit rate. This effect is completely unattainable by the existing cache architecture that partitions by data type, because the existing partitioning mode will disperse the transportation data and washing data of the same batch to different nodes, inevitably causing cross-node access latency.

[0138] This solution completely avoids the problems of dirty reads and timing misalignments in related data under concurrent access from multiple clients, ensuring the consistency of industrial control data: all related data in the same batch are stored in a dedicated partition on the same cache node, and all read and write operations are completed within the same node, avoiding timing misalignments caused by cross-node data synchronization time differences. At the same time, strong data binding is achieved through batch identifiers. When multiple clients access the data concurrently, only the complete dataset corresponding to the batch identifier can be retrieved, and there will be no dirty read problems caused by untimely reading of some data. This effect cannot be achieved by existing distributed partitioned storage architectures, because in existing architectures, related data in the same batch are stored in different nodes, and cross-node synchronization time differences can easily lead to data timing misalignments and dirty reads, directly affecting the accuracy of industrial control.

[0139] Based on any of the above technical solutions, the following optimization is made: When configuring the preemptive scheduling rules for cache resources in the washing and screening industrial safety hierarchical scheduling module, the priority score of each group of data in the cache partition is first calculated by the dynamic scoring formula of cache priority with hard constraints for industrial safety. Then, according to the order of scores from high to low, the allocation levels of cache bandwidth, storage resources, and read / write channels are divided, and the resource preemption permission of high-priority data is configured.

[0140] When the load on a cache node exceeds a set threshold or network bandwidth is limited, high-priority data can preempt the cache resources of low-priority data to ensure the read and write timeliness of core data.

[0141] The dynamic scoring formula for cache priority with hard constraints on industrial safety is as follows:

[0142] (2);

[0143] (2-1);

[0144] In the formula:

[0145] The read / write priority score for cached data, after being normalized by the Sigmoid function, ranges from 0 to 1. The higher the score, the higher the priority of cache resource allocation and scheduling.

[0146] The industrial functional safety level corresponding to the data is divided into 4 levels according to the Industrial Control System Functional Safety Assessment Standard, with corresponding values ​​of 4, 3, 2, and 1. The higher the safety level, the larger the value.

[0147] The process coupling degree between data and heavy media separation feedforward control, and transportation-washing capacity matching closed-loop control, is categorized according to the importance of each process control link, with a value ranging from 0 to 1. Higher coupling degrees result in larger values. Numerical positive correlation;

[0148] The real-time requirements for industrial control data are determined based on the data acquisition cycle specifications. The shorter the acquisition cycle, the higher the real-time requirements. The value range is 0~1.

[0149] , , : Weighted allocation coefficients, where The value is 0.5. The value is 0.3. The value is 0.2, and the coefficient value is determined based on the principles of industrial production control.

[0150] : Dedicated cache bandwidth reserved for the highest priority data of the equipment safety interlock; The total available bandwidth of the cache node is given by equation (2-1), which is a hard constraint for industrial security. Under no circumstances should low-priority data occupy this reserved resource.

[0151] Weighted allocation coefficient in this technical solution , , The determination method is based on the core principle of industrial production control—prioritizing safety, followed by process, and supplemented by management—as defined in GB / T33000-2016 "Basic Specifications for Enterprise Safety Production Standardization." Safety level is the core factor influencing priority, therefore it is assigned the highest weight coefficient of 0.5. Process coupling degree is the core factor affecting industrial production stability, assigned a weight coefficient of 0.3. Real-time requirements are a secondary influencing factor, assigned a weight coefficient of 0.2. The coefficient values ​​fully comply with the control principles of national standards. Industrial functional safety level... The determination method is based on the "Industrial Control System Functional Safety Assessment Specification". Equipment safety interlocking and emergency stop control data are divided into a maximum of 4 levels; heavy medium sorting closed-loop control data is divided into 3 levels; production status monitoring data is divided into 2 levels; and management traceability data is divided into 1 level. The corresponding values ​​are determined entirely according to national standards and specifications. Process coupling degree... The determination method is based on the "Technical Specification for Heavy Media Separation Process Control in Coal Preparation Plants". Data directly involved in the feedforward control of heavy media separation and the closed-loop control of transportation-washing capacity matching are assigned a process coupling degree of 0.8-1.0; data indirectly affecting process control are assigned a value of 0.4-0.8; and management data unrelated to process control are assigned a value of 0-0.4, which is positively correlated with the batch matching degree. The higher the matching degree, the higher the process coupling degree, determined according to industry standards. Real-time requirements are also considered. The determination method is based on the "General Technical Conditions for Monitoring Systems of Coal Mine Electromechanical Equipment". For safety interlock data collected at the 10ms level, the real-time requirement is set to 1.0; for closed-loop control data collected at the 100ms level, the value is set to 0.8; for monitoring data collected at the second level, the value is set to 0.4; and for management data collected at the minute level, the value is set to 0.1. The model hierarchy of this scheme is completely clear, divided into three progressive levels: the first level is the data input layer, the core inputs are the cached dataset after partition deployment, the batch matching degree value, and the safety level, process coupling degree, and real-time parameters determined by national standards; the second level is the algorithm calculation layer, the core of which is the priority score calculation of formula (2), the multi-dimensional weighted parameters are normalized by the Sigmoid function, and the standardized priority score in the 0-1 range is output. At the same time, industrial safety hard constraints are set, and the Sigmoid function is a normalization function known in the field; the third level is the strategy output layer, which divides the resource allocation level based on the priority score, configures the preemptive scheduling rules, and outputs the priority scheduling strategy. At the same time, it serves as the core input of the three-level heterogeneous network synchronization control module in step S5 and the batch full-cycle cache lifecycle management module in step S6. The input-output relationship is completely closed loop.

[0152] Existing technologies for resource scheduling optimization in distributed caching only rely on conventional scheduling based on data access frequency and data size. They fail to address the functional safety constraints of industrial production, lack a priority scheduling model centered on safety levels, and do not establish non-preemptible safety resource constraints. In contrast, the scheduling mechanism of this solution directly addresses the technical shortcomings explicitly stated in the background technologies: non-core data crowding out cache resources, excessive read / write latency of core control data, and the resulting production safety risks. The priority score calculated in this solution serves as the core input to the heterogeneous network synchronization management module, providing a core basis for cross-node synchronization priority allocation. Furthermore, the priority score in this solution also serves as the core input to the batch full-lifecycle cache lifecycle management module. This solution provides priority parameters for the dynamic adjustment of lifecycle duration; the industrial safety hard constraints of this solution work in conjunction with the physical isolation channels of equipment safety interlocking zones to provide dual protection for the read and write timeliness of core safety data from the two levels of resource reservation and channel isolation; the process coupling degree parameter of this solution works in conjunction with the batch matching degree value, and the process control data with higher matching degree has a higher priority score and higher resource allocation priority, further ensuring the read and write timeliness of sorting control data; the preemptive scheduling rules of this solution work in conjunction with the working condition adaptation closed-loop optimization module of the S7 step, and when the load of the cache node and the network bandwidth change, the resource allocation rules can be adjusted in real time to achieve dynamic adaptation of the scheduling strategy to the field working conditions.

[0153] This solution, from the perspective of cache resource scheduling, ensures the timeliness of reading and writing core safety data in coal industry production, completely avoiding the production safety risks brought about by the existing technical scheduling logic. By setting weighting coefficients, the industrial functional safety level is used as the core influencing factor for priority scoring. At the same time, a hard constraint of no less than 30% non-preemptible bandwidth is set for the safety interlock data of the highest safety level equipment. No matter how high the load of the cache node is or how tight the network bandwidth is, the core safety data has dedicated cache resources and bandwidth protection, ensuring that its read and write latency is always below the 10ms industrial control safety threshold. This avoids the problem of high-frequency non-core data crowding out resources and causing excessive latency of safety interlock data, which is a problem in the existing technology. It also avoids the production safety risks of safety interlock malfunctions and unplanned equipment shutdowns. This effect is completely impossible to achieve by the existing scheduling mechanism based on access frequency, because the existing scheduling logic does not have the concept of hard constraints on industrial functional safety and cannot guarantee the resource priority of core safety data.

[0154] This solution achieves deep integration of cache resource scheduling with the core washing and beneficiation process, directly improving the stability and accuracy of the closed-loop control of heavy media separation. By using the coupling degree between data and core processes as an important factor influencing priority scoring, data directly involved in the feedforward control of heavy media separation and the closed-loop control of transportation-washing capacity matching can obtain higher priority scores and resource allocation priorities, ensuring low latency in reading and writing process closed-loop control data, improving the response speed and stability of closed-loop control, significantly increasing the yield of clean coal, and reducing the ash content exceeding the standard rate.

[0155] Based on any of the above technical solutions, the following optimization is made: When the three-level heterogeneous network synchronization control module sets cross-node synchronization rules and directional pre-push mechanism, it first calculates the matching degree value between the data to be synchronized and the coal flow washing time sequence through the coal flow association data pre-push time sequence matching degree formula under heterogeneous network, and then matches the corresponding synchronization control strategy based on the matching degree value.

[0156] The formula for the time-series matching degree of pre-push coal flow correlation data under the heterogeneous network is:

[0157] (3)

[0158] In the formula:

[0159] The matching degree between the data to be pushed and the coal inflow washing time sequence, after cosine similarity normalization, takes a value of 0~1, which is used to trigger the targeted pre-push mechanism. The higher the matching degree, the higher the pre-push priority.

[0160] : Time series nodes within the time series window; The total number of nodes in the timing window, and the length of the timing window is determined according to the lead requirement of feedforward control for heavy medium sorting in GB / T35052-2018;

[0161] The progress feature vector of the coal flow arriving at the sorting equipment at time t is composed of the spatial displacement of the coal flow, the operating conditions of the belt conveyor, and the parameters of the feed bin. The core parameters come from the corresponding parameters of the spatiotemporal coupling index of the coal flow.

[0162] The time-series feature vector of the related data to be pushed at time t consists of data priority, process coupling degree, and single package data volume. The core priority parameter comes from... The calculation results;

[0163] : Transmission robustness correction coefficient for three-level heterogeneous networks, with a value range of 0 to 1. The parameter is determined based on the data transmission stability standards under different network environments.

[0164] The synchronization control strategy is as follows: when When, the cross-node related data targeted pre-push mechanism is triggered; when When, the dual-node redundancy backup synchronization mechanism is triggered; when When this happens, the local cache locking and breakpoint resume mechanism are triggered.

[0165] The total number of nodes in the timing window in this scheme The determination method is based on the lead time requirement of the feedforward control for heavy medium separation. The feedforward control lead time needs to cover the transportation time of the coal flow from the detection point to the separation equipment, which is usually 3-8 minutes. The timing window step size is set to 10 seconds, therefore the total number of nodes... Values ​​range from 18 to 48; Coal flow progress characteristic vector The determination method uses core parameters directly derived from the corresponding parameters of the coal flow spatiotemporal coupling index, including coal flow spatial displacement, belt operation conditions, and feed bin position parameters. All parameters are publicly known data that can be collected in real time by the existing system of the coal preparation plant, and the vector dimension and parameter values ​​are clearly defined; the pre-pushed data time series feature vector The method for determining priority parameters directly derives from priority scoring. The process coupling parameters are derived from the corresponding parameters in claim 5, the single packet data size is the actual size of the cached data, and all parameters are direct outputs of the preceding modules, requiring no additional calculation; network robustness correction coefficient. The determination method is based on data transmission stability standards under different network environments. When the network packet loss rate is less than 0.1%, The value is 1.0; when the packet loss rate is 0.1%-1%, the value is 0.8-0.99; when the packet loss rate is 1%-5%, the value is 0.5-0.79; when the packet loss rate is higher than 5%, the value is 0-0.49. The threshold division of the synchronization control strategy is determined based on the real-time requirements of coal washing industry control. The higher the matching degree, the higher the fit between the data and the coal inflow washing sequence, the higher the synchronization priority, and the threshold division is completely matched with the industrial control requirements.

[0166] The algorithm in this solution is deeply integrated with the three-level heterogeneous network scenario of coal transportation and washing. The integration method is clear: the core input of the algorithm is the coal inflow washing time sequence and data priority parameters, which directly adapts to the working characteristics of the three-level heterogeneous network of coal transportation, washing and washing internal network and group public network. The synchronization strategy output by the algorithm directly solves the technical problems of cross-node data synchronization lag and data loss when the network fluctuates in the heterogeneous network environment. Those skilled in the art can directly complete the scenario adaptation of the algorithm based on the three-level network architecture of the coal preparation plant and the on-site network status.

[0167] This solution achieves precise matching between cross-node data synchronization and coal inflow washing timing, completely solving the problem of existing technology's synchronization timing being out of sync with production timing, and significantly improving the response speed of heavy media separation feedforward control: through the cosine similarity algorithm, the matching degree between the data to be synchronized and the coal inflow washing timing is quantified. Only data that is strongly correlated with the coal flow about to be washed can obtain a high timing matching degree, triggering a targeted pre-push mechanism to push the corresponding data to the cache node of the washing and sorting network in advance. When the coal flow enters the sorting equipment, the feedforward control system can directly retrieve the required coal quality data from the local cache node without having to pull real-time data across three levels of network, thus avoiding control parameter mismatch caused by data synchronization lag. This solution achieves dynamic adaptation of cross-node synchronization strategies to three-tier heterogeneous network conditions, completely solving the problem of core data synchronization failure and loss during network fluctuations. By using a network robustness correction coefficient, it adapts to the transmission stability of the three-tier heterogeneous network in real time. When the network condition is good, a targeted pre-push mechanism is triggered to complete data synchronization in advance. When the network condition is average, a dual-node redundant backup synchronization mechanism is triggered to ensure the reliability of data synchronization. When the network condition is poor, a local cache locking and breakpoint resume mechanism is triggered to avoid data loss. Regardless of network fluctuations, the synchronization reliability of core data can be guaranteed.

[0168] This solution significantly reduces bandwidth usage in the three-tier heterogeneous network and optimizes cross-network data transmission efficiency: based on the time-series matching degree value, only data strongly correlated with the coal flow about to be washed is pre-pushd in a targeted manner, avoiding bandwidth usage caused by cross-network synchronization of all data. At the same time, based on data priority and matching degree, core control data is transmitted first, while unnecessary non-core data is filtered out, greatly reducing the cross-network transmission of invalid data.

[0169] Based on any of the above technical solutions, the following further optimization is made: The batch full-cycle cache lifecycle management module tracks the progress of the entire production process of raw coal batches from incoming acceptance, belt conveying, washing and sorting, product quality inspection to finished product delivery. Through a batch cache lifecycle dynamic adaptation formula with traceability and compliance constraints, the dynamic lifecycle duration of the corresponding batch cache data is calculated, and the storage strategy of the cache data is adjusted synchronously. When the raw coal batch completes finished product delivery and full-process traceability archiving, the corresponding cache data invalidation cleanup and cold data archiving operations are triggered.

[0170] The dynamic adaptation formula for the batch cache lifecycle with traceability compliance constraints is as follows:

[0171] (4)

[0172] (4-1)

[0173] In the formula:

[0174] : The dynamic lifecycle duration of the corresponding raw coal batch cache data;

[0175] The basic lifecycle constant term is determined based on the retention requirements of production process data, and its value is 6.58, corresponding to a basic lifecycle of 720 hours.

[0176] : The completion progress of the entire production process of the raw coal batch, with a value range of 0 to 1. After the batch is completed, the finished product is shipped out and archived, the value is 1.

[0177] : The read / write priority score for the corresponding cached data;

[0178] The data traceability compliance level is divided into 3 levels according to the coal quality traceability management standard, with corresponding values ​​of 1, 0.5 and 0, and the core traceability data has a value of 1.

[0179] , , Life cycle adjustment factor, where The value is -1.2. The value is 0.5. The value is 0.8, and the coefficient is determined according to the coal industry production data traceability management standard. The earlier the batch progress, the higher the priority, the higher the compliance level, and the longer the life cycle.

[0180] Random error term, with a value range of ±0.05, is used to adapt to small fluctuations in on-site working conditions;

[0181] The minimum retention time for cached data is determined to be 168 hours according to the information management standards of the coal industry. Equation (4-1) is a hard constraint for compliance.

[0182] Basic life cycle constants The method for determining this is based on the minimum retention requirements for coal production process data. The regulations clearly require that production process data be retained for at least 30 days (720 hours). Values Life cycle adjustment factor , , The method for determining this is based on the Coal Industry Production Data Traceability Management Standards: A value of -1.2 is a negative coefficient, which means that the earlier the production progress of the raw coal batch, the longer the required life cycle. After the batch is archived, the life cycle gradually shortens. A value of 0.5 is a positive coefficient, representing higher data priority and a longer lifecycle, ensuring the retention time of core data; A value of 0.8 is a positive coefficient, representing a higher level of data compliance and a longer lifecycle, ensuring the compliant retention of traceability data. The coefficient value fully complies with industry traceability management standards; batch progress. The determination method is based on the entire process of raw coal batches from entry to finished product exit. A value of 0 is assigned after entry acceptance, 0.2 after belt conveyor completion, 0.5 after washing and sorting, 0.8 after product quality inspection, and 1 after finished product exit and archiving. This progress division corresponds perfectly to the coal preparation plant's production process and can be directly obtained through the plant's existing production management system; priority scoring. The value of is completely consistent with the calculation result of formula (2) in claim 5, and the output parameters of the preceding module are directly reused; compliance level The determination method is based on the "Coal Quality Traceability Management Standard". Core data for coal quality traceability (coal quality testing data, product quality inspection data, and process control data) are classified as Level 1, with a value of 1; production process monitoring data are classified as Level 2, with a value of 0.5; non-core management data are classified as Level 3, with a value of 0; sixth, the minimum retention time in constraint condition (4-1). The method for determining this is based on the "Coal Industry Information Management Standard," which clearly requires that production process data be retained for at least 7 days (168 hours). The value is 168 hours, which is a hard compliance constraint. Under no circumstances should the lifespan of cached data be less than this value.

[0183] This technical solution focuses on the entire production process of raw coal batches, combining data priority and traceability compliance level. Through a log-linear regression model, it dynamically calculates the cache lifecycle of corresponding batch data, while setting a hard constraint on the minimum retention time required by industry standards. Based on the lifecycle duration, the cache storage strategy is dynamically adjusted to achieve precise matching between the cache lifecycle and the production progress of raw coal batches. From the perspective of lifecycle management, it solves the technical problems of cache resource waste, batch traceability data disconnection, and non-compliance retention caused by the unified lifecycle management of existing technologies.

[0184] By using a log-linear regression model, the cache lifecycle is deeply linked to the production progress of raw coal batches. The earlier the batch production progress, the longer the corresponding data lifecycle. As the batch production process progresses, the lifecycle of non-core data is dynamically adjusted, while the lifecycle of core traceability data always maintains the compliance requirements. This ensures that the traceability data of the same raw coal batch is completely retained in the cache throughout the entire production cycle, preventing data loss due to premature expiration. When an abnormality in the quality of refined coal occurs, the complete full-process data can be retrieved through the batch identifier to quickly locate the cause of the abnormality. This effect is completely impossible to achieve with existing fixed lifecycle management mechanisms.

[0185] This solution significantly optimizes the utilization of cache resources while ensuring compliant data retention, avoiding the problem of invalid data occupying cache resources for a long time: after the raw coal batch completes finished product delivery and full-process traceability archiving, the life cycle of non-core data is dynamically shortened, triggering invalid cleanup and cold data archiving operations. The non-core data that has been archived is cleaned up from the hot cache and archived to the backend cold storage, while only the core traceability data is retained in the hot cache. This satisfies the compliant retention requirements while releasing a large amount of hot cache resources.

[0186] Based on any of the above technical solutions, the following optimization is made: the preemptive scheduling rules configured in the washing and screening industrial safety hierarchical scheduling module set a cache read and write latency threshold. When the read and write latency of core control data exceeds the 10ms industrial control safety threshold specified in MT / T1097-2008, the read and write pause and resource release operations of low-priority data are automatically triggered to ensure core safety and the timeliness of read and write control data.

[0187] The cache read / write latency threshold setting in this solution is based on clear industry standards. According to the response time requirements for coal mine electromechanical equipment monitoring systems, the standards clearly stipulate that the system response time for core data related to equipment safety interlocks and production closed-loop control must not exceed 10ms. Therefore, this solution sets the cache read / write latency threshold for core control data to 10ms. The scope of core control data in this solution is clearly defined, falling within the industrial functional safety level. Data with values ​​of 4 and 3 include equipment safety interlocks, emergency stop control data, heavy media sorting closed-loop control data, and transportation-washing capacity matching closed-loop control data. This range corresponds completely to the safety level classification. Those skilled in the art can directly determine the range of core control data based on the safety level of the data without additional identification and screening.

[0188] Existing technologies for emergency optimization of cache resource scheduling are all merely conventional load balancing when nodes are overloaded. They do not address the mandatory safety thresholds of industrial control, nor do they design mandatory pause and resource reclamation mechanisms for low-priority data. Furthermore, they do not bind the trigger thresholds to the mandatory industry standards of the coal industry. In contrast, the emergency triggering mechanism of this solution directly addresses the technical defects that cause excessive read / write latency of core control data, leading to malfunctions in safety interlocks and production failures, and forms a synergistic relationship with other technical features. Specifically, the latency threshold setting in this solution synergizes with the hard constraints of industrial safety, providing dual protection for the read / write timeliness of core safety data from two levels: bandwidth reservation and emergency forced reclamation. Even if bandwidth reservation resources are occupied by extreme operating conditions, the forced reclamation mechanism ensures that the latency of core data always remains below the safety threshold. The scope of core control data in this solution fully corresponds to the safety level classification, ensuring that the target of emergency operations is clear and will not affect the normal read / write of core control data. The emergency operation instructions in this solution directly act on the secondary cache partition, pausing read / write operations on data in non-core partitions without affecting the normal operation of the equipment safety interlock partition and the re-media sorting control partition. The latency monitoring mechanism in this solution synergizes with the operating condition adaptation closed-loop optimization module in step S7. The trigger record of latency exceeding the standard is synchronously fed back to the closed-loop optimization module, iteratively updating the priority scheduling rules to fundamentally prevent the recurrence of latency exceeding the standard events.

[0189] Example 2: Compared with Example 1, this example also includes the following technical features:

[0190] Based on any of the above technical solutions, the following further optimization is made: the working condition adaptation closed-loop optimization module monitors the belt running condition, the washing and screening equipment operating status, the signal status of the three-level heterogeneous network, and the load and operating status of the buffer nodes in real time. When changes in working conditions such as belt slippage, feed fluctuation, network signal abnormality, or buffer node overload are detected, the module automatically iteratively updates the correction parameters of the spatiotemporal coupling index, priority scheduling rules, synchronization control strategies, and lifecycle configuration to achieve real-time adaptation between the buffer management strategy and the on-site production conditions.

[0191] The triggering conditions for changes in operating conditions in this solution are clearly defined, all of which are fluctuations in normal operating conditions at coal transportation and washing sites. The triggering thresholds are all based on clear industry standards: the triggering condition for belt slippage is that the deviation between the actual linear speed of the belt and the rated linear speed of the motor exceeds 5%, determined according to the belt slippage judgment standard; the triggering condition for feed rate fluctuation is that the deviation between the instantaneous feed rate of the belt scale and the set value exceeds 10%, determined according to the allowable fluctuation range of coal metering; the triggering condition for network signal abnormality is that the network packet loss rate exceeds 1%, determined according to the transmission stability standard of industrial Ethernet; the triggering condition for cache node overload is that the node CPU utilization and memory utilization continuously exceed 80%, determined according to the normal load safety threshold of industrial-grade servers. The thresholds for all triggering conditions are based on clear standards or conventional standards.

[0192] When fluctuations occur in the normal operating conditions on site, the core parameters and rules of each preceding module are automatically iterated and updated to achieve real-time dynamic adaptation of the entire cache management system to the on-site operating conditions. From a global perspective, this solves the core technical problem that the existing static cache configuration cannot adapt to fluctuations in on-site operating conditions.

[0193] Existing technologies for distributed caching optimization rely on manually preset static configuration rules or routine load balancing optimization only for the cache nodes themselves. They fail to design a closed-loop optimization mechanism covering the entire caching process across all production conditions in the coal transportation and washing operations. Furthermore, they do not deeply integrate caching strategy updates with changes in belt operation, washing production, and network status. This solution's closed-loop optimization mechanism directly addresses the technical shortcomings of static cache configurations, which cannot adapt to fluctuations in on-site conditions, leading to a disconnect between cache services and production needs. It works synergistically with preceding modules. Specifically, the belt operation monitoring and parameter updates in this solution collaborate with the coal flow spatiotemporal coupling index construction module. When belt slippage or feed rate fluctuations occur, the predicted coal flow arrival time is corrected in real time, ensuring the cache index is aligned with the coal flow. The actual conveying progress is always matched, fundamentally avoiding coal flow data misalignment; the network status monitoring and strategy update of this solution work in conjunction with the three-level heterogeneous network synchronization control module. When network signal anomalies occur, the synchronization control strategy is adjusted in real time to ensure the synchronization reliability of core data; the cache node load monitoring and rule update of this solution work in conjunction with the washing and beneficiation industry safety graded scheduling module. When the node load exceeds the limit, the priority scheduling rules are optimized in real time to ensure the resource priority of core data; the washing and beneficiation equipment operation status monitoring and configuration update of this solution work in conjunction with the batch full-cycle cache lifecycle management module. When the production progress of raw coal batches changes, the lifecycle configuration is adjusted in real time to ensure that the cache storage strategy is adapted to the production progress; all optimization results of this solution will have a reverse effect on the preceding modules to form a closed loop.

[0194] This solution enables dynamic real-time adaptation of the entire cache management system to the production conditions of the entire coal transportation-washing process, completely solving the technical problem that the existing static configuration architecture cannot adapt to fluctuations in operating conditions. Through comprehensive monitoring of operating conditions, it captures all changes in operating conditions of belt operation, washing and beneficiation production, network status, and cache nodes in real time. For each change in operating conditions, it automatically updates the parameters and rules of the entire cache system process accordingly. No matter what kind of normal operating condition fluctuations occur on site, the configuration of the cache system can be adapted in real time, ensuring that the cache service is always consistent with production needs, and realizing the deep integration of the cache system with the entire industrial production process.

[0195] This solution's closed-loop optimization mechanism ensures the overall stability of the caching system from a global perspective, avoiding system-level risks caused by changes in the operating conditions of a single link. The closed-loop optimization module covers the entire process of the caching system, from index building, storage, scheduling, synchronization to lifecycle management. When the operating conditions of a certain link change, not only is the configuration of the corresponding link updated, but the rules of other related links are also updated synchronously to achieve coordinated optimization of the entire system. For example, when the network signal is abnormal, not only is the synchronization control policy updated, but the priority scheduling rules and lifecycle configuration are also optimized synchronously. This addresses network fluctuations from multiple dimensions and avoids system-level risks caused by insufficient optimization of a single link. This effect cannot be achieved by existing conventional local optimization mechanisms, because existing optimization mechanisms only perform local optimization on a single link of the caching system and cannot achieve coordinated optimization of the entire process.

[0196] Based on any of the above technical solutions, a further optimization is made: the spatiotemporal coupling cache index key value generated by the coal flow spatiotemporal coupling index construction module forms a one-to-one mapping with the control cycle of the heavy medium separation feedforward control system. Each control cycle corresponds to a set of exclusive index key values, ensuring that the coal quality data retrieved by the feedforward control is completely matched with the actual coal flow entering the washing process.

[0197] The control cycle of the heavy media separation feedforward control system in this scheme has clear industry standard requirements. According to the technical specifications for heavy media separation process control in coal preparation plants, the closed-loop control cycle for the density of the heavy media separation suspension must not exceed 100ms. Secondly, the one-to-one mapping relationship between the spatiotemporal coupling cache index key value and the control cycle in this scheme is clearly implemented using a well-known timing synchronization method: the generation cycle of the spatiotemporal coupling cache index key value is completely synchronized with the control cycle of the heavy media separation feedforward control system, both being 100ms. For each control cycle, a unique set of spatiotemporal coupling cache index key values ​​is generated. These index key values ​​contain the timestamp of the corresponding control cycle, the raw coal batch identifier, and the predicted arrival time of the coal flow, ensuring a one-to-one correspondence between the index key value and the control cycle, without duplication or omission. Those skilled in the art can directly achieve this one-to-one mapping relationship through well-known timing synchronization triggering mechanisms.

[0198] This technical solution uses the 100ms control cycle of the heavy medium separation feedforward control as the timing benchmark, and synchronously generates a dedicated spatiotemporal coupling cache index key value for the corresponding cycle. In each control cycle, the feedforward control system accurately retrieves the matching data of the corresponding coal flow through the dedicated index key value. From the index application level, it ensures that the coal quality data retrieved by the feedforward control is completely matched with the actual coal flow entering the washing process.

[0199] The one-to-one mapping mechanism of this scheme works in conjunction with the coal flow batch matching quantification formula. The exclusive index key value generated in each control cycle is based on the batch matching degree value updated in the corresponding cycle, ensuring that the addressing weight of the index key value matches the actual progress of the coal flow in real time. The index key value generation cycle of this scheme works in conjunction with the monitoring cycle of the working condition adaptation closed-loop optimization module. The correction parameters of the index can be updated synchronously in each control cycle, ensuring that the index key value adapts to the on-site working conditions in real time. The exclusive index key value of this scheme works in conjunction with the cache storage of the heavy medium sorting control zone. The feedforward control system can only access the corresponding data in the heavy medium sorting control zone through the exclusive index key value, further improving the efficiency and security of data retrieval. The one-to-one mapping mechanism of this scheme directly binds the cache index to the core process control cycle, upgrading the cache system from a low-level data storage tool to a core link that directly serves the core process control, forming global collaboration with all modules.

[0200] This solution completely eliminates the misalignment problem between coal quality data and the incoming coal flow in the feedforward control of heavy media separation, improving the accuracy of feedforward control parameter matching to nearly 100%. Through a one-to-one mapping between index key values ​​and control cycles, each control cycle corresponds to a unique set of index key values. These key values ​​are generated based on the latest coal flow operating parameters and accurately correspond to the coal flow that will enter the separation equipment in the current control cycle. The coal quality data retrieved by the feedforward control system through this key value is completely matched with the actual incoming coal flow, completely eliminating data matching deviations caused by belt condition fluctuations and timestamp misalignments. This solution can improve the accuracy of feedforward control parameter matching and avoid control parameter mismatch caused by data misalignment. This is because the existing method only retrieves data through static timestamps, which cannot form a precise time synchronization with the control cycle, inevitably leading to data misalignment.

[0201] This solution significantly reduces the data retrieval and calculation latency of the feedforward control system for heavy media sorting, and improves the response speed and stability of closed-loop control. In each control cycle, the feedforward control system can directly retrieve the complete dataset with precise matching through a dedicated index key value, without the need for additional calculation operations such as filtering, matching, and timestamp correction of massive amounts of data. This greatly shortens the latency of data retrieval and preprocessing, leaving sufficient time for the calculation of feedforward control parameters, and significantly improves the response speed and stability of closed-loop control.

[0202] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0203] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A distributed cache storage management system for coal transportation and washing data, characterized in that, The system performs the following steps: S1. Collect production data of the entire process of coal transportation and washing, and after preprocessing, bind a unique batch identifier of raw coal entering the plant and a coal flow characteristic label to each data set, and output a labeled dataset. S2. Receive the labeled dataset output from the previous step, combine it with coal flow transportation conditions and spatial displacement data to construct a spatiotemporal coupled cache index, complete the cache data addressing mapping, and output the index mapping rules. S3. Receive the index mapping rules and tagged dataset generated in the previous step, aggregate and store the transportation and washing data of the same batch of raw coal to the corresponding cache node, complete the differentiated cache partition deployment, and output the cache dataset after partition deployment. S4. Based on the data attributes within the preceding cache partition, divide the cache read and write priorities according to the industrial functional safety level, configure the cache resource preemptive scheduling rules, and output the priority scheduling strategy. S5. Based on priority scheduling strategy and cache partition deployment, it matches the heterogeneous network conditions of transportation network, washing and beneficiation network and group public network, sets cross-node synchronization rules and a targeted pre-push mechanism for coal washing flow related data, and outputs cross-network synchronization control strategy. S6. Track the entire production process progress of raw coal batches, combine priority scheduling strategies, dynamically adjust the lifecycle of corresponding batch cached data, complete cache invalidation control and compliance archiving, and output lifecycle configuration rules. S7 monitors the on-site transportation and washing process and the running status of cache nodes in real time. Based on the preceding output rules, iteratively updates the index mapping rules, priority scheduling strategies, synchronization control strategies, and lifecycle configuration rules to adapt to on-site production needs.

2. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, The raw coal batch data tag binding module collects multi-source production data, including belt operation status data, feeding and metering data, online coal quality testing data upon arrival, vehicle transportation trajectory data, and transfer point operation data during the raw coal transportation process. It also includes process control data for heavy media separation in the washing and beneficiation process, equipment safety interlock data, coal quality testing data, product quality inspection data, and coal slurry water treatment operation data; The coal flow characteristic tags include coal flow source mine tag, coal quality washability grade tag, washing process type tag, data security grade tag, and data real-time grade tag. All tags form a one-to-one mapping and binding relationship with the unique batch identifier of raw coal entering the plant.

3. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, When constructing the spatiotemporal coupling index for coal flow, the spatiotemporal coupling cache index is performed by the following steps: The first step is to obtain the coal quality testing point collection time, the conveying path parameters from the testing point to the feed inlet of the heavy medium cyclone, the real-time operating condition parameters of the belt conveyor, and the coal quality characteristic data for the corresponding batch of raw coal. The second step is to calculate the matching degree between the coal quality test data and the corresponding coal batch entering the washing process by using the coal batch-cache index matching quantification formula, and use the matching degree as the core addressing weight of the cache index. The third step is to bind the matching degree weight with the raw coal batch identifier, coal flow feature label, and coal flow predicted arrival time to generate a unique spatiotemporal coupled cache index key value, and complete the addressing mapping of cache data. The coal flow batch-cached index matching metric formula is as follows: （1）； In the formula: The matching degree between coal quality test data and the corresponding batch of raw coal to be washed is the core addressing weight of the spatiotemporal coupling cache index. The value ranges from 0 to 1. The higher the matching degree, the higher the cache addressing priority. The posterior probability of the actual arrival time of the coal flow at the sorting equipment is obtained by adapting the Bayesian probability model to the fluctuation of the coal flow transportation conditions. The actual time it takes for the coal stream to reach the feed inlet of the heavy medium cyclone separator; : The time when coal quality data corresponding to the coal flow is collected at the online detection point of the conveyor belt; : A set of characteristic parameters for belt operation, including belt linear speed, load rate, and slippage coefficient; Jaccard similarity coefficient between raw coal batch identifiers and coal flow characteristic tags is used to quantify the binding correlation between data and batches, and avoid cross-batch data index misalignment; The set of unique batch identifiers for raw coal entering the plant; : The set of feature labels corresponding to coal flow data; : Coal quality characteristic consistency correction coefficient, with a value range of 0.9~1.

1. The parameter is determined based on the allowable fluctuation range of coal quality test data.

4. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, The transportation-washing associated data aggregation and caching module strongly correlates and binds coal quality testing data, belt operation data, and feeding metering data of the same batch of raw coal with heavy media separation process control data, feed parameter data, and product quality inspection data of the washing process, and aggregates and stores them in a dedicated partition of the same cache node. At the same time, it divides the cache into two-level partitions according to the washing process: heavy media separation control partition, equipment safety interlock partition, transportation scheduling and control partition, and coal quality traceability management partition. Physically isolated access channels are set up between each partition.

5. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, When configuring the preemptive scheduling rules for cache resources in the washing and screening industrial safety hierarchical scheduling module, the priority score of each group of data in the cache partition is first calculated by the dynamic scoring formula of cache priority with hard constraints of industrial safety. Then, according to the order of scores from high to low, the allocation level of cache bandwidth, storage resources and read and write channels is divided, and the resource preemption permission of high priority data is configured. When the load on a cache node exceeds a set threshold or network bandwidth is limited, high-priority data can preempt the cache resources of low-priority data to ensure the read and write timeliness of core data. The dynamic scoring formula for cache priority with hard constraints on industrial safety is as follows: （2）； （2-1）； In the formula: The read / write priority score for cached data, after being normalized by the Sigmoid function, ranges from 0 to 1; The industrial functional safety level corresponding to the data is divided into 4 levels according to the Industrial Control System Functional Safety Assessment Standard, with corresponding values ​​of 4, 3, 2, and 1. The higher the safety level, the larger the value. The process coupling degree between data and heavy media separation feedforward control, and transportation-washing capacity matching closed-loop control, is categorized based on the importance of each process control link, with a value ranging from 0 to 1. Higher coupling degrees result in larger values. Numerical positive correlation; The real-time requirements for industrial control data are determined based on the data acquisition cycle specifications. The shorter the acquisition cycle, the higher the real-time requirements. The value range is 0~1. , , : Weighted allocation coefficients, where The value is 0.

5. The value is 0.

3. The value is 0.2; : Dedicated cache bandwidth reserved for the highest priority data of the equipment safety interlock; The total available bandwidth of the cache node is given by equation (2-1), which is a hard constraint for industrial security. Under no circumstances should low-priority data occupy this reserved resource.

6. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, When the three-level heterogeneous network synchronization control module sets cross-node synchronization rules and targeted pre-push mechanism, it first calculates the matching degree value between the data to be synchronized and the coal flow washing time sequence by using the pre-push time sequence matching degree formula of coal flow association data under heterogeneous network, and then matches the corresponding synchronization control strategy based on the matching degree value. The formula for the time-series matching degree of pre-push coal flow correlation data under the heterogeneous network is: （3）； In the formula: The matching degree between the data to be pushed and the coal inflow washing time sequence, after cosine similarity normalization, takes a value of 0~1, which is used to trigger the targeted pre-push mechanism. The higher the matching degree, the higher the pre-push priority. : Time series nodes within the time series window; The total number of nodes in the timing window, and the length of the timing window is determined according to the lead requirement for feedforward control of heavy medium sorting in GB / T35052-2018; The progress feature vector of the coal flow arriving at the sorting equipment at time t is composed of the spatial displacement of the coal flow, the operating conditions of the belt conveyor, and the parameters of the feed bin. The core parameters come from the corresponding parameters of the spatiotemporal coupling index of the coal flow. The time-series feature vector of the related data to be pushed at time t consists of data priority, process coupling degree, and single package data volume. The core priority parameter comes from... The calculation results; : Transmission robustness correction coefficient for three-level heterogeneous networks, with a value range of 0 to 1. The parameter is determined based on the data transmission stability standard under different network environments. The synchronization control strategy is as follows: when When, the cross-node related data targeted pre-push mechanism is triggered; when When, the dual-node redundancy backup synchronization mechanism is triggered; when When this happens, the local cache locking and breakpoint resume mechanism are triggered.

7. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, The batch full-cycle cache lifecycle management module tracks the progress of the entire production process of raw coal batches, from incoming acceptance, belt conveying, washing and sorting, product quality inspection to finished product delivery. Through a batch cache lifecycle dynamic adaptation formula with traceability and compliance constraints, it calculates the dynamic lifecycle duration of the corresponding batch cache data and adjusts the cache data storage strategy accordingly. When the raw coal batch completes finished product delivery and full-process traceability archiving, it triggers the corresponding cache data invalidation cleanup and cold data archiving operations. The dynamic adaptation formula for the batch cache lifecycle with traceability compliance constraints is as follows: （4）； （4-1）； In the formula: : The dynamic lifecycle duration of the corresponding raw coal batch cache data; The basic lifecycle constant term is determined based on the retention requirements of production process data, and its value is 6.58, corresponding to a basic lifecycle of 720 hours. : The completion progress of the entire production process of the raw coal batch, with a value range of 0 to 1. After the batch is completed, the finished product is shipped out and archived, the value is 1. : The read / write priority score for the corresponding cached data; The data traceability compliance level is divided into 3 levels according to the coal quality traceability management standard, with corresponding values ​​of 1, 0.5 and 0, and the core traceability data has a value of 1. , , Life cycle adjustment factor, where The value is -1.

2. The value is 0.

5. The value is 0.8, and the coefficient is determined according to the coal industry production data traceability management standard. The earlier the batch progress, the higher the priority, the higher the compliance level, and the longer the life cycle. Random error term, with a value range of ±0.05, is used to adapt to small fluctuations in on-site working conditions; The minimum retention time for cached data is determined to be 168 hours according to the information management standards of the coal industry. Equation (4-1) is a hard constraint for compliance.

8. The distributed cache storage management system for coal transportation and washing data according to claim 5, characterized in that, The preemptive scheduling rules configured in the washing and screening industrial safety hierarchical scheduling module set a cache read / write latency threshold. When the read / write latency of core control data exceeds the 10ms industrial control safety threshold, the read / write pause and resource release operations of low-priority data are automatically triggered.

9. The distributed cache storage management system for coal transportation and washing data according to claim 1, characterized in that, The working condition adaptation closed-loop optimization module monitors the belt running conditions, washing and screening equipment operating status, three-level heterogeneous network signal status, and cache node load and operating status in real time. When it detects changes in working conditions such as belt slippage, feed rate fluctuation, network signal abnormality, or cache node overload, it automatically iterates and updates the correction parameters of the spatiotemporal coupling index, priority scheduling rules, synchronization control strategy, and lifecycle configuration to achieve real-time adaptation of cache management strategy to on-site production conditions.

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