A sand and gravel aggregate processing control system

Abstract

This invention discloses a sand and gravel aggregate processing control system, relating to the field of aggregate processing technology. Key technical features include a data acquisition module that collects historical processing procedure combinations and corresponding historical production capacity and quality feedback results from the sand and gravel aggregate processing system; generates collaborative architecture units and unit linkage markers based on the historical processing procedure combinations and historical production capacity and quality feedback results; collects real-time process monitoring data from the sand and gravel aggregate processing system and obtains process control requirements factors based on the real-time process monitoring data; obtains the target linkage architecture unit corresponding to the target collaborative architecture unit based on the unit linkage marker of the target collaborative architecture unit; constructs a process control scheme set based on the target linkage architecture unit corresponding to the target collaborative architecture unit; and obtains the optimal process control strategy for sand and gravel aggregate processing based on the process control scheme set, thereby improving the production capacity efficiency of sand and gravel aggregate processing.

Description

Technical Field

[0001] This invention relates to the field of aggregate processing technology, and more specifically, to a sand and gravel aggregate processing control system. Background Technology

[0002] In the field of sand and gravel aggregate processing, traditional sand and gravel aggregate processing control systems require operators to rely on experience to judge the connection rhythm of each process. However, human experience is highly subjective, and different operators have different judgment standards, which can easily lead to problems such as process connection bottlenecks and unreasonable parameter matching. At the same time, traditional methods lack systematic utilization of historical processing data. It is impossible to refer to the production capacity, quality and effect corresponding to similar process combinations in the past, and it is difficult to trace the root process of the problem. In addition, the lack of standardized control basis leads to poor consistency of the processing flow, making it difficult to meet the needs of large-scale production. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a sand and gravel aggregate processing control system.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A sand and gravel aggregate processing control system, comprising: Data Acquisition Module: Collects historical processing procedure combination schemes and corresponding historical production capacity and quality feedback results of the sand and gravel aggregate processing system; Generation module: Generates collaborative architecture units and unit linkage markers based on historical processing procedure combination schemes and historical capacity and quality feedback results; The first processing module collects real-time process monitoring data of the sand and gravel aggregate processing system and obtains the process control requirements factors of sand and gravel aggregate processing based on the real-time process monitoring data. Matching module: Generates control input parameters for sand and gravel aggregate processing based on process control requirements, and matches the control input parameters with the collaborative architecture unit to obtain the target collaborative architecture unit corresponding to sand and gravel aggregate processing; The second processing module obtains the target linkage architecture unit corresponding to the target collaborative architecture unit based on the unit linkage mark of the target collaborative architecture unit; Output module: Constructs a set of process control schemes based on the target collaborative architecture unit and the target linkage architecture unit corresponding to the target collaborative architecture unit, and obtains the optimal process control strategy for sand and gravel aggregate processing based on the set of process control schemes.

[0005] Preferably, the collaborative architecture unit and unit linkage marker are generated based on historical processing procedure combination schemes and historical capacity and quality feedback results, specifically including the following steps: Establish a collaborative architecture unit benchmark; wherein, the collaborative architecture unit benchmark includes a process combination sub-benchmark and a capacity quality characteristic sub-benchmark; Semantic analysis is performed on both historical processing procedure combination schemes and historical capacity and quality feedback results to obtain the semantic analysis results corresponding to the historical processing procedure combination schemes and historical capacity and quality feedback results. Based on the semantic parsing results and the collaborative architecture unit benchmark, generate the historical processing procedure combination scheme and the collaborative architecture unit corresponding to the historical capacity and quality feedback results; The correlation between historical processing combination schemes and historical capacity quality feedback results is a causal relationship of process effects; The unit linkage markers of the collaborative architecture units are obtained based on the causal relationship of the process effect; among them, the unit linkage markers include process optimization linkage markers and effect tracing linkage markers.

[0006] Preferably, the process collaborative control architecture includes at least one collaborative architecture unit, and each collaborative architecture unit includes a core process combination and the corresponding production capacity quality characteristics of the core process combination.

[0007] Preferably, the real-time process monitoring data includes process connection status data and process parameter matching data for sand and gravel aggregate processing.

[0008] Preferably, the process control requirements for sand and gravel aggregate processing are obtained based on real-time process monitoring data, specifically including the following steps: Based on the process connection status data, the connection optimization and control factors for sand and gravel aggregate processing are obtained; Based on the process parameter matching data, the parameter adaptation and control factors for sand and gravel aggregate processing are obtained; The process control requirements for sand and gravel aggregate processing are composed of connection optimization control factors and parameter adaptation control factors.

[0009] Preferably, the control input parameters for sand and gravel aggregate processing are generated based on process control requirements, specifically including the following steps: Semantic analysis of process control demand factors yields semantic analysis results. Based on the semantic parsing results and the benchmark of the collaborative architecture unit, real-time collaborative factors are generated corresponding to the process control demand factors. The real-time collaborative factors include the core process items and the core process features. The control input parameters for sand and gravel aggregate processing are constituted by real-time collaborative factors corresponding to process control requirements.

[0010] Preferably, the target collaborative architecture unit corresponding to sand and gravel aggregate processing is obtained by matching the control input parameters with the collaborative architecture unit, specifically including the following steps: The core process items of the real-time collaborative factor in the control input parameters are matched with the core process items of the collaborative architecture unit to obtain the first matching collaborative unit corresponding to the real-time collaborative factor. The core process characteristics of the real-time collaborative factor are matched with the production capacity and quality characteristics of the first matching collaborative unit to obtain the target collaborative architecture unit corresponding to the real-time collaborative factor.

[0011] Preferably, obtaining the target linkage architecture unit corresponding to the target collaborative architecture unit based on the unit linkage tag of the target collaborative architecture unit specifically includes the following steps: The target linkage architecture unit includes a target process optimization unit and a target effect tracing unit; If the process control requirement factor corresponding to the target collaborative architecture unit is a connection optimization control factor, then obtain the process optimization linkage mark of the target collaborative architecture unit; Based on the process optimization linkage marker of the target collaborative architecture unit, the process optimization association link of the target collaborative architecture unit is obtained, and all collaborative architecture units in the process optimization association link are marked as target process optimization units; If the process control requirement factor corresponding to the target collaborative architecture unit is a parameter adaptation control factor, then obtain the effect traceability linkage mark of the target collaborative architecture unit; Based on the effect tracing linkage marker, the target effect tracing unit corresponding to the target collaborative architecture unit is obtained.

[0012] Preferably, obtaining the target effect tracing unit corresponding to the target collaborative architecture unit based on the effect tracing linkage marker specifically includes the following steps: The effect tracing and linkage links of the target collaborative architecture unit are obtained based on the effect tracing and linkage markers of the target collaborative architecture unit. All collaborative architecture units in the effect tracing link are marked as target effect tracing units.

[0013] Preferably, a set of process control schemes is constructed based on the target collaborative architecture unit corresponding to the target linkage architecture unit, specifically including the following steps: Based on the core process combination of the unit and the corresponding capacity and quality characteristics of the unit core process combination in the process control scheme, the optimal process control strategy for sand and gravel aggregate processing is formed.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention extracts process control demand factors from real-time process monitoring data, enabling timely detection of connection or parameter issues in the current processing. The matching module transforms these real-time demands into control input parameters and matches them with target collaborative architecture units to avoid deviations in control direction. The second processing module finds the target linkage architecture unit based on unit linkage markers, meaning that adjustments are not made in isolation for single processes, but rather linked to related process units to form a complete control chain. The process combination and capacity characteristics of the linkage units are integrated to generate the optimal control strategy, ensuring that both capacity and quality remain stable and up to standard after parameter adjustments. Ultimately, this continuously and stably improves the capacity efficiency of sand and gravel aggregate processing while ensuring product quality consistency and reducing resource waste and failure risks during processing. Attached Figure Description

[0015] Figure 1 This invention provides a schematic diagram of a sand and gravel aggregate processing control system. Figure 2 This invention provides a schematic diagram illustrating the steps of process control requirements in a sand and gravel aggregate processing control system. Figure 3 The present invention provides a system flowchart for a sand and gravel aggregate processing and control system. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-3 As shown.

[0020] The embodiments further illustrate the sand and gravel aggregate processing control system proposed in this invention.

[0021] A sand and gravel aggregate processing control system, comprising: Data Acquisition Module: Collects historical processing procedure combination schemes and corresponding historical production capacity and quality feedback results of the sand and gravel aggregate processing system; Generation module: Generates collaborative architecture units and unit linkage markers based on historical processing procedure combination schemes and historical capacity and quality feedback results; The first processing module collects real-time process monitoring data of the sand and gravel aggregate processing system and obtains the process control requirements factors of sand and gravel aggregate processing based on the real-time process monitoring data. Matching module: Generates control input parameters for sand and gravel aggregate processing based on process control requirements, and matches the control input parameters with the collaborative architecture unit to obtain the target collaborative architecture unit corresponding to sand and gravel aggregate processing; The second processing module obtains the target linkage architecture unit corresponding to the target collaborative architecture unit based on the unit linkage mark of the target collaborative architecture unit; Output module: Constructs a set of process control schemes based on the target collaborative architecture unit and the target linkage architecture unit corresponding to the target collaborative architecture unit, and obtains the optimal process control strategy for sand and gravel aggregate processing based on the set of process control schemes.

[0022] Based on historical processing combination schemes and historical capacity and quality feedback results, collaborative architecture units and unit linkage markers are generated, specifically including the following steps: Establish a collaborative architecture unit benchmark; wherein, the collaborative architecture unit benchmark includes a process combination sub-benchmark and a capacity quality characteristic sub-benchmark; Semantic analysis is performed on both historical processing procedure combination schemes and historical capacity and quality feedback results to obtain the semantic analysis results corresponding to the historical processing procedure combination schemes and historical capacity and quality feedback results. Based on the semantic parsing results and the collaborative architecture unit benchmark, generate the historical processing procedure combination scheme and the collaborative architecture unit corresponding to the historical capacity and quality feedback results; The correlation between historical processing procedure combination schemes and historical capacity and quality feedback results is a causal relationship of process effects; The unit linkage markers of the collaborative architecture units are obtained based on the causal relationship of the process effect; among them, the unit linkage markers include process optimization linkage markers and effect tracing linkage markers.

[0023] First, it is necessary to set benchmarks for collaborative architecture units. These benchmarks include process combination sub-benchmarks and capacity quality characteristic sub-benchmarks. For example, in the sand and gravel aggregate processing scenario, the process combination sub-benchmark is the process flow specification for crushing, screening, and washing, while the capacity quality characteristic sub-benchmark is the performance indicator specification for a daily capacity of not less than 800 tons and a mud content of not more than 1%.

[0024] Semantic analysis of historical processing combination schemes and historical capacity and quality feedback results transforms actual historical data into structured information that can be matched with benchmarks. For example, a historical processing combination scheme might consist of jaw crushing, circular vibrating screen screening, and wheel washing. After semantic analysis, key information such as the process type and equipment type can be extracted. The corresponding historical capacity and quality feedback results are a daily capacity of 850 tons and a mud content of 0.8%. Semantic analysis extracts the characteristic information of the capacity value and quality indicators. These extracted contents constitute the semantic analysis results.

[0025] Based on the semantic parsing results and the collaborative architecture unit benchmark, corresponding collaborative architecture units are generated. For example, the extracted process information of jaw crushing, circular vibrating screen screening, and wheel washing is matched with the crushing, screening, and washing processes in the process combination sub-benchmark. At the same time, its corresponding daily production capacity of 850 tons and mud content of 0.8% match the indicators of the production capacity quality characteristic sub-benchmark. In this way, this set of processes and effects can be bound to form a collaborative architecture unit. The collaborative architecture unit contains both the specific process combination content and the corresponding production capacity quality characteristics.

[0026] Clarify the correlation between historical processing procedure combination schemes and historical capacity and quality feedback results. This relationship is a causal relationship of process effect, that is, the combination of processes will correspond to specific capacity and quality effects. For example, the combination of jaw crushing, circular vibrating screen screening, and wheel washing processes is the reason why the daily capacity of 850 tons and mud content of 0.8% are achieved. There is a direct causal relationship between the two.

[0027] Based on this causal relationship between process effects, unit linkage markers are obtained for collaborative architecture units. Unit linkage markers include process optimization linkage markers and effect tracing linkage markers. For example, when there is room for optimization in the process combination corresponding to a certain collaborative architecture unit, a process optimization linkage marker will be attached, pointing to other process combinations that can optimize the connection problem; while when a certain production capacity quality characteristic fluctuates, the effect tracing linkage marker will point to the process link that caused the fluctuation. For example, if the mud content exceeds the standard, the effect tracing linkage marker will be associated with the collaborative architecture unit corresponding to the cleaning process, which facilitates subsequent tracing of the root cause.

[0028] The process coordination control architecture includes at least one coordination architecture unit, and each coordination architecture unit includes a core process combination and the corresponding capacity quality characteristics of the core process combination.

[0029] The process coordination and control architecture for sand and gravel aggregate processing consists of at least one coordination architecture unit, which is the core component of the control architecture. Each coordination architecture unit includes a core process combination and the corresponding production capacity and quality characteristics.

[0030] For example, the core process combination of a certain collaborative architecture unit could be jaw crushing, circular vibrating screen screening, and wheel washing. This set of processes represents the specific processing flow corresponding to that unit. The capacity quality characteristics corresponding to this core process combination are the actual processing results achieved by this combination, such as a daily capacity of 850 tons and a mud content of 0.8%. These indicators represent the capacity quality characteristics corresponding to this core process combination.

[0031] Different collaborative architecture units correspond to different core process combinations and capacity / quality characteristics. For example, another collaborative architecture unit might have a core process combination of impact crushing, linear vibrating screen screening, and spiral washing, with corresponding capacity / quality characteristics of 900 tons per day and a mud content of 0.7%. These collaborative architecture units, which include specific processes and corresponding effects, collectively form the basis of the process collaborative control architecture.

[0032] Based on real-time process monitoring data, the process control requirements for sand and gravel aggregate processing are determined, specifically including the following steps: Real-time process monitoring data includes process connection status data and process parameter matching data for sand and gravel aggregate processing.

[0033] Based on the process connection status data, the connection optimization and control factors for sand and gravel aggregate processing are obtained; Based on the process parameter matching data, the parameter adaptation and control factors for sand and gravel aggregate processing are obtained; The process control requirements for sand and gravel aggregate processing are composed of connection optimization control factors and parameter adaptation control factors.

[0034] Real-time process monitoring data in the sand and gravel aggregate processing control system is the core basis for obtaining control requirements, specifically including process connection status data and process parameter matching data in sand and gravel aggregate processing.

[0035] Process connection status data corresponds to the connection status between different processes in the processing flow. For example, in a process combination of jaw crushing, circular vibrating screen screening, and wheel washing, the process connection status data is the conveying time from the crushing process output to the screening process. If the actual monitored conveying time exceeds the normal range (for example, the standard time is 2 minutes, but it actually reaches 3 minutes), it indicates that there is a bottleneck in the connection between the crushing and screening processes. Based on such process connection status data, corresponding connection optimization and control factors are obtained, which means that the connection rhythm between these two processes needs to be adjusted.

[0036] The process parameter matching data corresponds to the matching status of each process's own operating parameters with the standard parameters. The process parameter matching data is the jaw plate clearance of the crushing process. If the standard jaw plate clearance should be 10 cm to ensure that the crushed particle size is ≤50 mm, but the actual monitored jaw plate clearance is 12 cm, it will cause the crushed particle size to exceed the standard. Based on such process parameter matching data, the corresponding parameter adaptation and control factors are obtained, that is, the jaw plate clearance parameter of the crushing process needs to be adjusted.

[0037] Finally, the connection optimization control factors and parameter adaptation control factors are integrated to form the process control demand factors for sand and gravel aggregate processing.

[0038] The process involves generating control input parameters for sand and gravel aggregate processing based on process control requirements. This includes the following steps: Semantic analysis of process control demand factors yields semantic analysis results. Based on the semantic parsing results and the benchmark of the collaborative architecture unit, real-time collaborative factors are generated corresponding to the process control demand factors. The real-time collaborative factors include the core process items and the core process features. The control input parameters for sand and gravel aggregate processing are constituted by real-time collaborative factors corresponding to process control requirements.

[0039] In the aggregate processing control system, semantic analysis of process control demand factors is required, which transforms these demand factors into structured information. For example, process control demand factors might include adjusting the connection rhythm between crushing and screening processes and adjusting the jaw plate gap parameters in the crushing process. After semantic analysis, the core process links and control directions are extracted. This extracted information is the semantic analysis result of the process control demand factors.

[0040] By combining semantic parsing results and the baseline of the collaborative architecture unit, real-time collaborative factors corresponding to the process control requirements are generated. These real-time collaborative factors include core process items and core process features. For the requirement to adjust the connection rhythm between crushing and screening processes, the core process items are the crushing and screening processes, and the core process feature is the process connection duration. For the requirement to adjust the jaw plate gap parameter of the crushing process, the core process item is the crushing process itself, and the core process feature is the correspondence between the jaw plate gap and the crushed particle size.

[0041] The real-time collaborative factors corresponding to the process control requirements are integrated to form the control input parameters for sand and gravel aggregate processing. Based on the control input parameters, the corresponding collaborative architecture units are matched to advance the process control process.

[0042] The target collaborative architecture unit for sand and gravel aggregate processing is obtained by matching the control input parameters with the collaborative architecture unit. The specific steps include: The core process items of the real-time collaborative factor in the control input parameters are matched with the core process items of the collaborative architecture unit to obtain the first matching collaborative unit corresponding to the real-time collaborative factor. The core process characteristics of the real-time collaborative factor are matched with the production capacity and quality characteristics of the first matching collaborative unit to obtain the target collaborative architecture unit corresponding to the real-time collaborative factor.

[0043] In the matching stage of the sand and gravel aggregate processing control system, a process item matching operation is performed. The core process items controlled by the real-time coordination factor in the control input parameters are compared one by one with the core process combinations of the unit in the coordination architecture. For example, if the core process items controlled by the real-time coordination factor are crushing and screening, and one unit in the coordination architecture has a core process combination of jaw crushing, circular vibrating screen screening, and wheel washing, which includes crushing and screening processes, then after this process item matching step, this coordination architecture unit is selected as the first matching coordination unit corresponding to the real-time coordination factor.

[0044] After completing the process item matching, feature matching is performed. The core process characteristics of the real-time collaborative factor are compared with the capacity and quality characteristics of the first matching collaborative unit. For example, the core process characteristics of the real-time collaborative factor are the process connection time and the crushing particle size corresponding to the jaw plate gap, while the capacity and quality characteristics of the first matching collaborative unit include a crushing and screening process connection time of 2 minutes and a jaw plate gap of 10 cm corresponding to a crushing particle size of ≤50 mm. After matching the features of the two to confirm that the feature dimensions are consistent and meet the control requirements, the first matching collaborative unit is determined as the target collaborative architecture unit corresponding to the real-time collaborative factor, providing a basic unit for subsequent linkage control.

[0045] The target collaborative architecture unit is obtained by identifying the target linkage architecture unit based on its unit linkage marker. This process includes the following steps: The target linkage architecture unit includes a target process optimization unit and a target effect traceability unit; If the process control requirement factor corresponding to the target collaborative architecture unit is a connection optimization control factor, then obtain the process optimization linkage mark of the target collaborative architecture unit; Based on the process optimization linkage marker of the target collaborative architecture unit, the process optimization association link of the target collaborative architecture unit is obtained, and all collaborative architecture units in the process optimization association link are marked as target process optimization units; If the process control requirement factor corresponding to the target collaborative architecture unit is a parameter adaptation control factor, then obtain the effect traceability linkage mark of the target collaborative architecture unit; Based on the effect tracing linkage marker, the target effect tracing unit corresponding to the target collaborative architecture unit is obtained.

[0046] In the aggregate processing control system, the target linkage architecture unit includes target process optimization units and target effect tracing units. If the process control requirement factor corresponding to the target collaborative architecture unit is a connection optimization control factor, such as needing to adjust the connection rhythm of crushing and screening processes, then the process optimization linkage mark of the target collaborative architecture unit is obtained. The process optimization linkage mark is set based on the causal relationship of process effects in historical data, and it corresponds to the associated link that can optimize the connection problem. Then, based on the process optimization linkage mark, the process optimization associated link corresponding to the target collaborative architecture unit is found. For example, this link includes collaborative architecture units for adjusting the connection rhythm of crushing and screening and adapting the cycle time of screening and washing processes. All collaborative architecture units in the associated link are marked as target process optimization units. These units together constitute the linkage subject of connection optimization.

[0047] If the process control requirement factor corresponding to the target collaborative architecture unit is a parameter adaptation control factor, such as needing to adjust the jaw plate gap parameter in the crushing process, then the effect tracing linkage mark of the target collaborative architecture unit is obtained. The effect tracing linkage mark points to the related content that can trace the root cause of parameter problems based on the historical causal relationship of process effects. According to the effect tracing linkage mark, the target effect tracing unit corresponding to the target collaborative architecture unit is found. For example, if the target effect tracing unit is a collaborative architecture unit for matching the jaw plate gap and particle size in the crushing process, it can clarify the effect correlation corresponding to parameter adjustment, providing a tracing basis for subsequent parameter adaptation.

[0048] Based on the effect tracing linkage marker, the target effect tracing unit corresponding to the target collaborative architecture unit is obtained, which specifically includes the following steps: The effect tracing and linkage links of the target collaborative architecture unit are obtained based on the effect tracing and linkage markers of the target collaborative architecture unit. All collaborative architecture units in the effect tracing link are marked as target effect tracing units.

[0049] In the effect tracing stage of the sand and gravel aggregate processing control system, the effect tracing linkage marker of the target collaborative architecture unit is used to locate the related links. If the process control requirement factor corresponding to the target collaborative architecture unit is a parameter adaptation control factor, such as needing to adjust the jaw plate gap parameter of the crushing process, the effect tracing linkage marker pre-set by the target collaborative architecture unit is first invoked. The effect tracing linkage marker is established based on the causal relationship of historical process effects and can point to the process association logic related to the current parameter problem.

[0050] Based on the effect traceability linkage markers, the effect traceability linkage links corresponding to the target collaborative architecture units are identified. For example, the effect traceability linkage links include collaborative architecture units for jaw plate gap setting, feed particle size pretreatment, and crushing particle size detection in the crushing process. The collaborative architecture units are the linkage units in historical data that are related to crushing process parameters and corresponding capacity and quality characteristics.

[0051] All collaborative architecture units in the effect tracing link are marked as target effect tracing units. Target effect tracing units together constitute the tracing system for parameter problems.

[0052] Based on the target collaborative architecture unit and the target linkage architecture unit corresponding to the target collaborative architecture unit, a set of process control schemes is constructed, which includes the following steps: Based on the core process combination of the unit and the corresponding capacity and quality characteristics of the unit core process combination in the process control scheme, the optimal process control strategy for sand and gravel aggregate processing is formed.

[0053] In the final control stage of the sand and gravel aggregate processing control system, a target linkage architecture unit based on a set of process control schemes is used to integrate its key information to generate the optimal process control strategy. It is assumed that the target linkage architecture unit includes a target process optimization unit and a target effect traceability unit. The core process combination of the target process optimization unit is the adjustment of the crushing and screening connection rhythm and the adaptation of the screening and washing process cycle time. The corresponding capacity and quality characteristics are: the crushing to screening conveying time is stable at 2 minutes, the screening to washing process connection is smooth, and the daily capacity remains at 850 tons. In the parameter adaptation control scenario, the core process combination of the target effect traceability unit is the jaw plate gap setting and feed particle size pretreatment in the crushing process. The corresponding capacity and quality characteristics are: when the jaw plate gap is maintained at 10 cm, the crushed particle size is stable at ≤50 mm, and when the feed particle size is controlled within the specified range, the mud content remains at 0.8%.

[0054] The core processes of the target linkage architecture unit are integrated with their corresponding capacity and quality characteristics, and the process operation requirements and effect standards of different units are mapped together. For example, in the connection optimization scenario, the integrated process operation is to adjust the conveying rhythm from crushing to screening to a 2-minute interval between each batch of material, and to synchronously match the start and stop rhythms of screening and washing processes, while using a daily capacity of 850 tons and seamless connection as effect constraints. In the parameter adaptation scenario, the process operation is to adjust the jaw plate gap of the crushing process to 10 cm and control the feed particle size within a preset range, while using crushed particle size ≤ 50 mm and mud content 0.8% as effect constraints. These process operation requirements and effect standards together constitute the optimal process control strategy for sand and gravel aggregate processing, ensuring that the processing process not only meets the control requirements of process connection or parameter matching, but also stably achieves the expected capacity and quality targets.

[0055] Select a set of processes from an actual sand and gravel aggregate processing scenario, such as the continuous process of feeding, crushing, and screening. For example, first set the feeding speed (e.g., 120 tons / hour) and the initial particle size distribution of the material (e.g., maximum 800 mm, average 350 mm) in the feeding stage, corresponding to the jaw plate gap (10 cm) and the upper limit of the feed particle size (e.g., 300 mm) in the crushing stage, and then associate it with the screen aperture in the screening stage, such as a screen aperture of 50 mm, combined with the capacity target (e.g., hourly capacity ≥ 100 tons) and quality indicators (crushed particle size ≤ 50 mm). With parameters of ≥95% millimeter content and ≤0.8% mud content, the system fully simulates the entire process from process initiation and real-time monitoring data feedback (such as feed rate fluctuations and warnings of excessive crushing particle size) to the triggering of control strategies (such as adjusting the feed frequency and fine-tuning the jaw plate gap). Through the linkage effect of parameters in each link (such as the change in the probability of excessive crushing particle size when the feed rate increases by 10%) and the response timeliness of control measures (such as the verification of the quality index returning to the threshold within 3 minutes after parameter adjustment), the system demonstrates the actual operation status and effect of the control system.

[0056] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0057] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sand and gravel aggregate processing control system, characterized in that, include: Data Acquisition Module: Collects historical processing procedure combination schemes and corresponding historical production capacity and quality feedback results of the sand and gravel aggregate processing system; Generation module: Generates collaborative architecture units and unit linkage markers based on historical processing procedure combination schemes and historical capacity and quality feedback results; The first processing module collects real-time process monitoring data of the sand and gravel aggregate processing system and obtains the process control requirements factors of sand and gravel aggregate processing based on the real-time process monitoring data. Matching module: Generates control input parameters for sand and gravel aggregate processing based on process control requirements, and matches the control input parameters with the collaborative architecture unit to obtain the target collaborative architecture unit corresponding to sand and gravel aggregate processing; The second processing module obtains the target linkage architecture unit corresponding to the target collaborative architecture unit based on the unit linkage tag of the target collaborative architecture unit, specifically including the following steps: The target linkage architecture unit includes a target process optimization unit and a target effect tracing unit; If the process control requirement factor corresponding to the target collaborative architecture unit is a connection optimization control factor, then obtain the process optimization linkage mark of the target collaborative architecture unit; Based on the process optimization linkage marker of the target collaborative architecture unit, the process optimization association link of the target collaborative architecture unit is obtained, and all collaborative architecture units in the process optimization association link are marked as target process optimization units; If the process control requirement factor corresponding to the target collaborative architecture unit is a parameter adaptation control factor, then obtain the effect traceability linkage mark of the target collaborative architecture unit; Based on the effect tracing linkage markers, the target effect tracing unit corresponding to the target collaborative architecture unit is obtained; Output module: Constructs a set of process control schemes based on the target collaborative architecture unit and the target linkage architecture unit corresponding to the target collaborative architecture unit, and obtains the optimal process control strategy for sand and gravel aggregate processing based on the set of process control schemes.

2. The sand and gravel aggregate processing control system according to claim 1, characterized in that, Based on historical processing combination schemes and historical capacity and quality feedback results, collaborative architecture units and unit linkage markers are generated, specifically including the following steps: Establish a collaborative architecture unit benchmark; wherein, the collaborative architecture unit benchmark includes a process combination sub-benchmark and a capacity quality characteristic sub-benchmark; Semantic analysis is performed on both historical processing procedure combination schemes and historical capacity and quality feedback results to obtain the semantic analysis results corresponding to the historical processing procedure combination schemes and historical capacity and quality feedback results. Based on the semantic parsing results and the collaborative architecture unit benchmark, generate the historical processing procedure combination scheme and the collaborative architecture unit corresponding to the historical capacity and quality feedback results; The correlation between historical processing combination schemes and historical capacity quality feedback results is a causal relationship of process effects; The unit linkage markers of the collaborative architecture units are obtained based on the causal relationship of the process effect; among them, the unit linkage markers include process optimization linkage markers and effect tracing linkage markers.

3. The sand and gravel aggregate processing control system according to claim 2, characterized in that, The process coordination control architecture includes at least one coordination architecture unit, and each coordination architecture unit includes a core process combination and the corresponding capacity quality characteristics of the core process combination.

4. The sand and gravel aggregate processing control system according to claim 3, characterized in that, The real-time process monitoring data includes process connection status data and process parameter matching data for sand and gravel aggregate processing.

5. A sand and gravel aggregate processing control system according to claim 4, characterized in that, Based on real-time process monitoring data, the process control requirements for sand and gravel aggregate processing are determined, specifically including the following steps: Based on the process connection status data, the connection optimization and control factors for sand and gravel aggregate processing are obtained; Based on the process parameter matching data, the parameter adaptation and control factors for sand and gravel aggregate processing are obtained; The process control requirements for sand and gravel aggregate processing are composed of connection optimization control factors and parameter adaptation control factors.

6. The sand and gravel aggregate processing control system according to claim 5, characterized in that, The process involves generating control input parameters for sand and gravel aggregate processing based on process control requirements. This includes the following steps: Semantic analysis of process control demand factors yields semantic analysis results. Based on the semantic parsing results and the benchmark of the collaborative architecture unit, real-time collaborative factors are generated corresponding to the process control demand factors. The real-time collaborative factors include the core process items and the core process features. The control input parameters for sand and gravel aggregate processing are constituted by real-time collaborative factors corresponding to process control requirements.

7. The sand and gravel aggregate processing control system according to claim 6, characterized in that, The target collaborative architecture unit for sand and gravel aggregate processing is obtained by matching the control input parameters with the collaborative architecture unit. The specific steps include: The core process items of the real-time collaborative factor in the control input parameters are matched with the core process items of the collaborative architecture unit to obtain the first matching collaborative unit corresponding to the real-time collaborative factor. The core process characteristics of the real-time collaborative factor are matched with the production capacity and quality characteristics of the first matching collaborative unit to obtain the target collaborative architecture unit corresponding to the real-time collaborative factor.

8. A sand and gravel aggregate processing control system according to claim 7, characterized in that, Based on the effect tracing linkage marker, the target effect tracing unit corresponding to the target collaborative architecture unit is obtained, which specifically includes the following steps: The effect tracing and linkage links of the target collaborative architecture unit are obtained based on the effect tracing and linkage markers of the target collaborative architecture unit. All collaborative architecture units in the effect tracing link are marked as target effect tracing units.

9. A sand and gravel aggregate processing control system according to claim 8, characterized in that, Based on the target collaborative architecture unit and the target linkage architecture unit corresponding to the target collaborative architecture unit, a set of process control schemes is constructed, which includes the following steps: Based on the core process combination of the unit and the corresponding capacity and quality characteristics of the unit core process combination in the process control scheme, the optimal process control strategy for sand and gravel aggregate processing is formed.

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