Coal yard intelligent comprehensive management system and method

Abstract

This invention relates to the field of coal yard management technology and discloses an intelligent integrated management system and method for coal yards. The system includes: a data reorganization module that acquires historical production data corresponding to multiple historical moments of the coal yard to be managed, reorganizes the data to obtain multiple sets of historical production data; a data parsing module that parses the sets of historical production data and calculates historical production fluctuation judgment values; a deviation analysis module that performs deviation analysis on all historical production fluctuation judgment values ​​and calculates historical production fluctuation deviation values; and an integrated management module that, based on the historical production fluctuation deviation values ​​and preset historical production fluctuation deviation values, determines whether the coal yard to be managed meets the safety management conditions. If so, an intelligent integrated management strategy for the coal yard is generated. By determining the fluctuation characteristics and deviation characteristics of historical production data over time, the system can accurately determine whether the coal yard meets the safety management requirements, laying the foundation for setting an intelligent integrated management strategy for the coal yard and avoiding the generation of risky strategies.

Description

Technical Field

[0001] This invention relates to the field of coal yard management technology, and more specifically, to an intelligent integrated management system and method for coal yards. Background Technology

[0002] Coal, as a crucial basic energy source and industrial raw material in my country, directly impacts energy supply security and industrial economic operation through its production, storage, transportation, and management efficiency. Coal yards, as a key node in the coal industry chain, undertake important functions such as coal storage, allocation, quality control, and safe production. However, traditional coal yard management relies heavily on manual experience, resulting in incomplete data collection, outdated analytical methods, and lagging management decisions, making it difficult to cope with complex and ever-changing production environments and market demands.

[0003] Coal yards generate a large amount of production data during daily operations. However, this production data is often not systematically processed and analyzed, and cannot be effectively transformed into information to support management decisions. Moreover, production data fluctuates significantly over time, and traditional methods lack the ability to accurately mine the fluctuation patterns and identify anomalies, leading to safety hazards and efficiency losses. It is also impossible to accurately determine whether the coal yard is within the scope of safety management requirements. Summary of the Invention

[0004] This invention provides an intelligent integrated management system and method for coal yards. By determining the fluctuation and deviation characteristics of historical production data over time, it can accurately determine whether a coal yard meets safety management requirements, laying the foundation for setting intelligent integrated management strategies for coal yards and avoiding the generation of risky strategies.

[0005] To achieve the above objectives, the present invention provides an intelligent integrated management system for coal yards, comprising: The data reorganization module is used to identify the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reorganize the historical production data corresponding to each historical moment to obtain multiple sets of historical production data. The data parsing module is used to parse each set of historical production data and calculate multiple historical production fluctuation judgment values ​​for the coal yard to be managed based on the parsing results. The deviation analysis module is used to perform deviation analysis on all historical production fluctuation judgment values, and calculate the historical production fluctuation deviation value of the coal yard to be managed based on the deviation analysis results. The integrated management module is used to pre-set preset historical production fluctuation deviation values. Based on the relationship between the historical production fluctuation deviation values ​​and the preset historical production fluctuation deviation values, it determines whether the coal yard to be managed meets the safety management conditions. If so, it generates an intelligent integrated management strategy for the coal yard.

[0006] Furthermore, it also includes: The data processing module is used to traverse and analyze the historical production data corresponding to each historical moment. The data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data. The historical production data set after data analysis is parsed.

[0007] Furthermore, the data parsing module is used for: Calculate the neighborhood data fluctuation metric value corresponding to each historical production data based on the historical production data set; The historical production fluctuation judgment value of the coal yard to be managed is calculated based on all neighborhood data fluctuation metrics.

[0008] Furthermore, the data parsing module is used for: Pre-set the first preset data value and the second preset data value; Based on the first preset data value, the preceding historical production data of the historical production data is determined, and based on the second preset data value, the subsequent historical production data of the historical production data is determined. Generate a historical production data sequence corresponding to the historical production data based on the historical production data, the previous historical production data, and the subsequent historical production data; Calculate the neighborhood data fluctuation metric value corresponding to the historical production data based on the historical production data sequence; The neighborhood data fluctuation metric is calculated using the following formula: ; Where q is the neighborhood data fluctuation metric, a is the number of historical production data in the historical production data sequence, and r w For the w-th historical production data in the historical production data sequence, r w+1 This refers to the (w+1)th historical production data point in the historical production data sequence.

[0009] Furthermore, the data parsing module is used for: The historical moments are divided into multiple historical moment intervals; Extract the variance of the neighborhood data fluctuation metric corresponding to each historical time interval; A neighborhood data volatility metric curve is constructed based on the variance of all neighborhood data volatility metrics, and a volatility factor is determined, wherein the volatility factor is the maximum slope of the neighborhood data volatility metric curve. Determine the mean of the neighborhood data fluctuation metrics for all neighborhood data fluctuation metrics, and use the product of the mean neighborhood data fluctuation metrics and the fluctuation factor as the historical production fluctuation judgment value for the coal yard to be managed.

[0010] Furthermore, the deviation analysis module is used for: Determine the mean and median of all historical production fluctuation judgment values. Determine the first deviation value between each historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values, wherein the first deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. Determine the second deviation value between each historical production fluctuation judgment value and the median of the historical production fluctuation judgment values, wherein the second deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. The product of the first and second outliers is determined as the outlier analysis value for each historical production fluctuation judgment value.

[0011] Furthermore, the deviation analysis module is used for: Determine the mean of the partial anomaly analysis values ​​for all partial anomaly analysis values; Extract the partial outlier values ​​that are greater than the mean of the partial outlier values, generate the first distinguishing code, and count the number of the first distinguishing codes; Extract the partial outlier value equal to the mean of the partial outlier values, generate the second distinguishing code, and count the number of second distinguishing codes; Extract the partial outlier values ​​that are less than the mean of the partial outlier values, generate a third distinguishing code, and count the number of third distinguishing codes; The historical production fluctuation deviation value of the coal yard to be managed is calculated based on the number of the first, second, and third distinguishing codes.

[0012] Furthermore, the integrated management module is used for: When the historical production fluctuation deviation value is less than the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed meets the safety management conditions. When the historical production fluctuation deviation value is greater than or equal to the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed does not meet the safety management conditions.

[0013] To achieve the above objectives, the present invention also provides an intelligent integrated management method for coal yards, comprising: Identify the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reorganize the historical production data corresponding to each historical moment to obtain multiple sets of historical production data; Each set of historical production data is parsed, and multiple historical production fluctuation judgment values ​​for the coal yard to be managed are calculated based on the parsing results. A bias analysis was performed on all historical production fluctuation judgment values, and the historical production fluctuation bias value of the coal yard to be managed was calculated based on the bias analysis results. A preset historical production fluctuation deviation value is set in advance. Based on the relationship between the historical production fluctuation deviation value and the preset historical production fluctuation deviation value, it is determined whether the coal yard to be managed meets the safety management conditions. If so, an intelligent integrated management strategy for the coal yard is generated.

[0014] Furthermore, before parsing each set of historical production data, the following steps are also included: The historical production data corresponding to each historical moment is traversed and analyzed, wherein the data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data; The historical production data set after data analysis is parsed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses an intelligent integrated management system and method for coal yards. The system includes: a data reorganization module that acquires historical production data corresponding to multiple historical moments of the coal yard to be managed, reorganizes the data to obtain multiple sets of historical production data; a data parsing module that parses the historical production data sets and calculates historical production fluctuation judgment values; a deviation analysis module that performs deviation analysis on all historical production fluctuation judgment values ​​and calculates historical production fluctuation deviation values; and an integrated management module that, based on the historical production fluctuation deviation values ​​and preset historical production fluctuation deviation values, determines whether the coal yard to be managed meets safety management conditions. If so, an intelligent integrated management strategy for the coal yard is generated. By determining the fluctuation characteristics and deviation characteristics of historical production data over time, the system can accurately determine whether the coal yard meets safety management requirements, laying the foundation for setting an intelligent integrated management strategy for the coal yard and avoiding the generation of risky strategies. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of the structure of an intelligent integrated management system for coal yards according to an embodiment of the present invention is shown; Figure 2 A flowchart illustrating an intelligent integrated management method for coal yards according to an embodiment of the present invention is shown. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent integrated management system for coal yards, including: a data reorganization module, a data parsing module, a deviation analysis module, and an integrated management module.

[0023] In some embodiments of this application, a data recombination module is used to determine the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reassemble the historical production data corresponding to each historical moment to obtain multiple sets of historical production data. In this embodiment, the historical time points are preset, and the number of historical time points is preferably 20, including the 10th minute, 20th minute, 30th minute, 40th minute, 50th minute, 60th minute, 70th minute, 80th minute, 90th minute, 100th minute, 110th minute, 120th minute, 130th minute, 140th minute, 150th minute, 160th minute, 170th minute, 180th minute, 190th minute, and 200th minute. It should be noted that the historical time points are counted based on the current time.

[0024] In this embodiment, historical production data includes the internal temperature of the coal pile, carbon monoxide concentration, dust concentration, pulverized coal content, sulfur content, etc., which are not shown one by one here.

[0025] In this embodiment, each historical moment corresponds to multiple historical production data.

[0026] In this embodiment, the historical production data corresponding to each historical moment is reorganized, that is, the historical production data of the same type are classified. For example, the internal temperature of the coal pile corresponding to the 10th minute in the past, the internal temperature of the coal pile corresponding to the 20th minute in the past, ..., the internal temperature of the coal pile corresponding to the 200th minute in the past are combined to obtain a set of historical production data that only concerns the internal temperature of the coal pile. The rest will not be shown one by one.

[0027] In some embodiments of this application, a data parsing module is used to parse each set of historical production data and calculate multiple historical production fluctuation judgment values ​​of the coal yard to be managed based on the parsing results. In some embodiments of this application, it also includes: The data processing module is used to traverse and analyze the historical production data corresponding to each historical moment. The data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data. The historical production data set after data analysis is parsed.

[0028] The beneficial effects of the above technical solution are: the present invention deletes duplicate historical production data, erroneous historical production data and invalid historical production data, which can ensure an accurate data foundation.

[0029] In some embodiments of this application, the data parsing module is used for: Calculate the neighborhood data fluctuation metric value corresponding to each historical production data based on the historical production data set; The historical production fluctuation judgment value of the coal yard to be managed is calculated based on all neighborhood data fluctuation metrics.

[0030] In some embodiments of this application, the data parsing module is used for: Pre-set the first preset data value and the second preset data value; Based on the first preset data value, the preceding historical production data of the historical production data is determined, and based on the second preset data value, the subsequent historical production data of the historical production data is determined. Generate a historical production data sequence corresponding to the historical production data based on the historical production data, the previous historical production data, and the subsequent historical production data; Calculate the neighborhood data fluctuation metric value corresponding to the historical production data based on the historical production data sequence; The neighborhood data fluctuation metric is calculated using the following formula: ; Where q is the neighborhood data fluctuation metric, a is the number of historical production data in the historical production data sequence, and r w For the w-th historical production data in the historical production data sequence, r w+1 This refers to the (w+1)th historical production data point in the historical production data sequence.

[0031] In this embodiment, the first preset data value is preferably 4, and the second preset data value is preferably 3. The specific values ​​can be adjusted according to the needs.

[0032] In this embodiment, four left-hand historical production data points, or front-end historical production data, are determined based on the first preset data value. Three right-hand historical production data points, or back-end historical production data, are determined based on the second preset data value. It should be noted that if the actual number is less than four or three, the actual number shall prevail.

[0033] The beneficial effects of the above technical solution are as follows: This invention generates a historical production data sequence corresponding to the historical production data based on historical production data, previous historical production data, and subsequent historical production data, integrates neighboring historical production data, calculates the neighboring data fluctuation metric value corresponding to the historical production data based on the historical production data sequence, realizes the neighborhood fluctuation characteristic analysis of each historical production data, and lays the foundation for overall fluctuation analysis.

[0034] In some embodiments of this application, the data parsing module is used for: The historical moments are divided into multiple historical moment intervals; Extract the variance of the neighborhood data fluctuation metric corresponding to each historical time interval; A neighborhood data volatility metric curve is constructed based on the variance of all neighborhood data volatility metrics, and a volatility factor is determined, wherein the volatility factor is the maximum slope of the neighborhood data volatility metric curve. Determine the mean of the neighborhood data fluctuation metrics for all neighborhood data fluctuation metrics, and use the product of the mean neighborhood data fluctuation metrics and the fluctuation factor as the historical production fluctuation judgment value for the coal yard to be managed.

[0035] In this embodiment, every four historical moments are divided into a historical moment interval, as mentioned above, that is, there are five historical moment intervals.

[0036] In this embodiment, each neighborhood data fluctuation metric is assigned a value sequentially based on the historical time sequence. The assigned values ​​include 1, 2, 3, 4, and 5. The neighborhood data fluctuation metric is used as the vertical axis and the corresponding assigned value is used as the horizontal axis to construct a neighborhood data fluctuation metric curve.

[0037] The beneficial effects of the above technical solution are: the present invention uses the product of the mean value of the neighborhood data fluctuation metric and the fluctuation factor as the historical production fluctuation judgment value of the coal yard to be managed, thereby realizing the fluctuation anomaly analysis of the overall historical production data and ensuring the accuracy of coal yard safety management judgment.

[0038] In some embodiments of this application, the deviation analysis module is used to perform deviation analysis on all historical production fluctuation judgment values, and calculate the historical production fluctuation deviation value of the coal yard to be managed based on the deviation analysis results. In some embodiments of this application, the partial deviation analysis module is used for: Determine the mean and median of all historical production fluctuation judgment values. Determine the first deviation value between each historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values, wherein the first deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. Determine the second deviation value between each historical production fluctuation judgment value and the median of the historical production fluctuation judgment values, wherein the second deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. The product of the first and second outliers is determined as the outlier analysis value for each historical production fluctuation judgment value.

[0039] The beneficial effects of the above technical solution are as follows: By calculating the mean and median of all historical production fluctuation judgment values, the overall distribution of historical production fluctuation judgment values ​​can be accurately grasped. Furthermore, by determining the first deviation value from the mean of historical production fluctuation judgment values ​​and the second deviation value from the median of historical production fluctuation judgment values ​​for each historical production fluctuation judgment value, the degree of deviation of each historical production fluctuation judgment value relative to the overall distribution can be meticulously characterized. The product of the first and second deviation values ​​is used as the deviation analysis value for each historical production fluctuation judgment value. This indicator comprehensively reflects the deviation of historical production fluctuation judgment values ​​in both the mean and median dimensions, providing a strong basis for subsequent calculation of the historical production fluctuation deviation value of the coal yard under management.

[0040] In some embodiments of this application, the partial deviation analysis module is used for: Determine the mean of the partial anomaly analysis values ​​for all partial anomaly analysis values; Extract the partial outlier values ​​that are greater than the mean of the partial outlier values, generate the first distinguishing code, and count the number of the first distinguishing codes; Extract the partial outlier value equal to the mean of the partial outlier values, generate the second distinguishing code, and count the number of second distinguishing codes; Extract the partial outlier values ​​that are less than the mean of the partial outlier values, generate a third distinguishing code, and count the number of third distinguishing codes; Calculate the historical production fluctuation deviation value of the coal yard to be managed based on the number of the first, second, and third distinguishing codes; The historical production fluctuation deviation of the coal yard under management is calculated based on the following formula: ; Where t is the historical production fluctuation deviation value of the coal yard to be managed, y1 is the number of the first distinguishing codes, y2 is the number of the second distinguishing codes, y3 is the number of the third distinguishing codes, and max is the maximum value sign.

[0041] In this embodiment, the distinguishing codes include Δy1, Δy2, and Δy3, which are used to distinguish data. Δy1 is generated for the outlier values ​​that are greater than the mean of the outlier values, Δy2 is generated for the outlier values ​​that are equal to the mean of the outlier values, and Δy3 is generated for the outlier values ​​that are less than the mean of the outlier values.

[0042] In this embodiment, before determining the mean of all the partial anomaly analysis values, all the partial anomaly analysis values ​​are normalized. The normalization method is the min-max normalization method, which normalizes all the partial anomaly analysis values ​​to the range [0, 1].

[0043] The beneficial effects of the above technical solution are: the historical production fluctuation deviation value of the coal yard to be managed is calculated based on the number of the first, second and third identification codes, and the data distribution of different deviation degrees is comprehensively considered, making the calculation results more scientific and reasonable, and providing strong data support for subsequent coal yard safety management.

[0044] In some embodiments of this application, the integrated management module is used to pre-set a preset historical production fluctuation deviation value, and determine whether the coal yard to be managed meets the safety management conditions based on the relationship between the historical production fluctuation deviation value and the preset historical production fluctuation deviation value. If so, an intelligent integrated management strategy for the coal yard is generated.

[0045] In some embodiments of this application, the integrated management module is used for: When the historical production fluctuation deviation value is less than the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed meets the safety management conditions. When the historical production fluctuation deviation value is greater than or equal to the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed does not meet the safety management conditions.

[0046] In this embodiment, the preset historical production fluctuation deviation value is preferably 0.8, but it can be adjusted adaptively according to actual needs.

[0047] The beneficial effects of the above technical solution are as follows: Based on the relationship between historical production fluctuation deviation values ​​and preset historical production fluctuation deviation values, this invention determines whether the coal yard under management meets the safety management conditions. By determining the fluctuation characteristics and deviation characteristics of historical production data over time, it can accurately determine whether the coal yard meets the safety management requirements, thus laying the foundation for setting up an intelligent integrated management strategy for the coal yard.

[0048] In some embodiments of this application, when the coal yard to be managed meets the safety management conditions, an intelligent integrated management strategy for the coal yard is generated. This strategy specifically includes: employing a high-speed coal inventory and stacker-reclaimer system to achieve automatic data transmission, realizing an automated stacking and reclaiming system and an unattended digital intelligent coal yard. Utilizing technologies such as laser coal inventory, wireless positioning, data overlay, and unattended bucket wheel excavator upgrades, and collecting data from systems such as the SIS system, stacker-reclaimer, and coal conveying control, an integrated management platform for coal stacking, storage, and retrieval is established. The coal yard implements zoned and stacked management, with coal storage and retrieval operations located at specific locations. It can display the real-time storage status of each coal yard and information such as mine type, quantity, quality, and price. Real-time inventory of the coal pile volume and 3D display of the coal pile graphic allow for real-time monitoring of the quantity, volume, and density of stored coal, effectively improving the intelligence level and production efficiency of the coal yard management, and providing a precise data source for coal blending, achieving energy conservation and efficiency improvement.

[0049] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0050] Correspondingly, such as Figure 2 As shown, this application also provides an intelligent integrated management method for coal yards, including: S110: Determine the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reorganize the historical production data corresponding to each historical moment to obtain multiple sets of historical production data; S120: Analyze each set of historical production data and calculate multiple historical production fluctuation judgment values ​​for the coal yard to be managed based on the analysis results; S130: Perform deviation analysis on all historical production fluctuation judgment values, and calculate the historical production fluctuation deviation value of the coal yard to be managed based on the deviation analysis results; S140: Pre-set a preset historical production fluctuation deviation value. Based on the relationship between the historical production fluctuation deviation value and the preset historical production fluctuation deviation value, determine whether the coal yard to be managed meets the safety management conditions. If so, generate an intelligent integrated management strategy for the coal yard.

[0051] In some embodiments of this application, before parsing each set of historical production data, the following is also included: The historical production data corresponding to each historical moment is traversed and analyzed, wherein the data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data; The historical production data set after data analysis is parsed.

[0052] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0053] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0054] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A coal yard intelligent integrated management system, characterized in that, include: The data reorganization module is used to identify the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reorganize the historical production data corresponding to each historical moment to obtain multiple sets of historical production data. The data parsing module is used to parse each set of historical production data and calculate multiple historical production fluctuation judgment values ​​for the coal yard to be managed based on the parsing results. The deviation analysis module is used to perform deviation analysis on all historical production fluctuation judgment values, and calculate the historical production fluctuation deviation value of the coal yard to be managed based on the deviation analysis results. The integrated management module is used to pre-set preset historical production fluctuation deviation values. Based on the relationship between the historical production fluctuation deviation values ​​and the preset historical production fluctuation deviation values, it determines whether the coal yard to be managed meets the safety management conditions. If so, it generates an intelligent integrated management strategy for the coal yard.

2. The intelligent integrated management system for coal yards according to claim 1, characterized in that, Also includes: The data processing module is used to traverse and analyze the historical production data corresponding to each historical moment. The data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data. The historical production data set after data analysis is parsed.

3. The intelligent integrated management system for coal yards according to claim 1, characterized in that, The data parsing module is used for: Calculate the neighborhood data fluctuation metric value corresponding to each historical production data based on the historical production data set; The historical production fluctuation judgment value of the coal yard to be managed is calculated based on all neighborhood data fluctuation metrics.

4. The intelligent integrated management system for coal yards according to claim 3, characterized in that, The data parsing module is used for: Pre-set the first preset data value and the second preset data value; Based on the first preset data value, the preceding historical production data of the historical production data is determined, and based on the second preset data value, the subsequent historical production data of the historical production data is determined. Generate a historical production data sequence corresponding to the historical production data based on the historical production data, the previous historical production data, and the subsequent historical production data; Calculate the neighborhood data fluctuation metric value corresponding to the historical production data based on the historical production data sequence; The neighborhood data fluctuation metric is calculated using the following formula: ； Where q is the neighborhood data fluctuation metric, a is the number of historical production data in the historical production data sequence, and r w For the w-th historical production data in the historical production data sequence, r w+1 This refers to the (w+1)th historical production data point in the historical production data sequence.

5. The intelligent integrated management system for coal yards according to claim 3, characterized in that, The data parsing module is used for: The historical moments are divided into multiple historical moment intervals; Extract the variance of the neighborhood data fluctuation metric corresponding to each historical time interval; A neighborhood data volatility metric curve is constructed based on the variance of all neighborhood data volatility metrics, and a volatility factor is determined, wherein the volatility factor is the maximum slope of the neighborhood data volatility metric curve. Determine the mean of the neighborhood data fluctuation metrics for all neighborhood data fluctuation metrics, and use the product of the mean neighborhood data fluctuation metrics and the fluctuation factor as the historical production fluctuation judgment value for the coal yard to be managed.

6. The intelligent integrated management system for coal yards according to claim 1, characterized in that, The deviation analysis module is used for: Determine the mean and median of all historical production fluctuation judgment values. Determine the first deviation value between each historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values, wherein the first deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. Determine the second deviation value between each historical production fluctuation judgment value and the median of the historical production fluctuation judgment values, wherein the second deviation value is the absolute value of the difference between the historical production fluctuation judgment value and the mean of the historical production fluctuation judgment values. The product of the first and second outliers is determined as the outlier analysis value for each historical production fluctuation judgment value.

7. The intelligent integrated management system for coal yards according to claim 6, characterized in that, The deviation analysis module is used for: Determine the mean of the partial anomaly analysis values ​​for all partial anomaly analysis values; Extract the partial outlier values ​​that are greater than the mean of the partial outlier values, generate the first distinguishing code, and count the number of the first distinguishing codes; Extract the partial outlier value equal to the mean of the partial outlier values, generate the second distinguishing code, and count the number of second distinguishing codes; Extract the partial outlier values ​​that are less than the mean of the partial outlier values, generate a third distinguishing code, and count the number of third distinguishing codes; The historical production fluctuation deviation value of the coal yard to be managed is calculated based on the number of the first, second, and third distinguishing codes.

8. The intelligent integrated management system for coal yards according to claim 1, characterized in that, The integrated management module is used for: When the historical production fluctuation deviation value is less than the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed meets the safety management conditions. When the historical production fluctuation deviation value is greater than or equal to the preset historical production fluctuation deviation value, it is determined that the coal yard to be managed does not meet the safety management conditions.

9. A method for intelligent integrated management of coal yards, applied to the intelligent integrated management system for coal yards as described in any one of claims 1-8, characterized in that, include: Identify the coal yard to be managed, obtain historical production data corresponding to multiple historical moments of the coal yard to be managed, and reorganize the historical production data corresponding to each historical moment to obtain multiple sets of historical production data; Each set of historical production data is parsed, and multiple historical production fluctuation judgment values ​​for the coal yard to be managed are calculated based on the parsing results. Anisotropy analysis was performed on all historical production fluctuation judgment values, and the historical production fluctuation anisotropy value of the coal yard to be managed was calculated based on the anisotropy analysis results. A preset historical production fluctuation deviation value is set in advance. Based on the relationship between the historical production fluctuation deviation value and the preset historical production fluctuation deviation value, it is determined whether the coal yard to be managed meets the safety management conditions. If so, an intelligent integrated management strategy for the coal yard is generated.

10. The intelligent integrated management method for coal yards according to claim 9, characterized in that, Before parsing each set of historical production data, the following is also included: The historical production data corresponding to each historical moment is traversed and analyzed, wherein the data analysis includes deleting duplicate historical production data, deleting erroneous historical production data, and deleting invalid historical production data; The historical production data set after data analysis is parsed.

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