A full life cycle operation data intelligent management method of a coal preparation plant production device

Abstract

The present application relates to the technical field of industrial internet, in particular to a kind of full life cycle operation data intelligent management method of coal preparation plant production equipment. It includes: obtaining operation data;Calculate information barrier performance, obtain monitoring intensive time period, calculate stage information fusion support force;Determine the whole process interference equipment from stage production equipment, determine the production stage of whole process interference equipment as conflict interference stage, calculate maintenance interference intensity;Calculate real anomaly retention degree;Calculate the de-interference fusion support force of each whole process interference equipment in each conflict interference stage;Calculate the information fusion rationality of each whole process interference equipment, calculate the information fusion rationality of each non-whole process interference equipment;Mark the equipment with information fusion rationality greater than preset rationality threshold as efficient equipment, and perform data fusion on the operation data of efficient equipment.The present application can improve the efficiency of data fusion of each equipment in the process of coal production.

Description

Technical Field

[0001] This invention relates to the field of industrial internet technology, specifically to an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment. Background Technology

[0002] The entire coal production system mainly comprises three sequential production stages: raw coal stage → sorting stage → post-processing stage. These three stages together constitute a complete coal production lifecycle, with each stage containing multiple cooperating production equipment. In a coal preparation plant's production system, "production equipment" does not refer to a single industrial device, but rather to the complete system process that coal undergoes from raw material (coarse material) to finished products meeting different user needs. This system process encompasses multiple stages, including raw coal preparation, sorting, product processing, and auxiliary stages. Its main technological objectives include removing impurities such as gangue, reducing ash and sulfur content, improving coal quality, and producing various specifications of products to meet diverse user demands.

[0003] Currently, while coal preparation plants attempt to analyze and manipulate data from a holistic lifecycle perspective when implementing intelligent management of operational data across the entire lifecycle of production equipment, significant "information silos" exist between different devices—meaning that operational data from different devices exhibits barriers in format, frequency, and dimension, hindering effective data integration. This makes it difficult for the management system to obtain comprehensive and macro-level information support. Consequently, the data samples upon which intelligent decision-making relies are fragmented and one-sided; a unified situational awareness across stages and devices cannot be formed; and maintenance strategies lack foresight and coordination, leading to delayed and inefficient maintenance, impacting the continuity and stability of production. Summary of the Invention

[0004] This invention provides an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment to solve existing problems.

[0005] The present invention provides an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment, which adopts the following technical solution:

[0006] One embodiment of the present invention provides an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment, the method comprising the following steps:

[0007] Obtain operational data of coal production within a preset monitoring period. The operational data includes at least two production stages, each production stage includes at least two production equipment stages, and each production equipment stage includes at least one operational data sequence.

[0008] For each production equipment in each production stage, the information barrier performance is calculated based on the length and sampling frequency of each running data sequence, the monitoring intensive time period is obtained, and the stage information fusion support is calculated based on the monitoring intensive time period and the information barrier performance.

[0009] The entire process interference equipment is identified from the stage production equipment. The production stage of the entire process interference equipment is identified as the conflict interference stage. For each entire process interference equipment, the maintenance interference intensity is calculated based on the number of conflict interference stages and the stage information fusion support of the entire process interference equipment.

[0010] For each stage of conflict interference, the degree of retention of the true anomaly is calculated based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process;

[0011] Based on the maintenance interference intensity of each full-process jamming device and the actual anomaly retention degree of each conflict jamming stage, calculate the de-jamming fusion support capacity of each full-process jamming device in each conflict jamming stage;

[0012] The information fusion rationality of each full-process interference device is calculated based on the interference fusion support capability, and the information fusion rationality of each non-full-process interference device is also calculated.

[0013] Devices whose information fusion rationality exceeds a preset rationality threshold are marked as high-efficiency devices, and their operating data are fused.

[0014] Optionally, based on the length and sampling frequency of each running data sequence, the information barrier performance of the production equipment at each stage is calculated, specifically including:

[0015] The product of the length of the nth running data sequence and the sampling frequency is used to determine the integrity of the nth running data sequence.

[0016] Obtain the completeness sum of other running data sequences, where other running data sequences are the running data sequences in the stage production equipment excluding the nth running data sequence;

[0017] Calculate the absolute value of the difference between the integrity of the nth running data sequence and the sum of the integrity of other running data sequences to obtain the information barrier performance of the nth running data sequence, where n is a positive integer;

[0018] The information barrier performance of each running data sequence is summed to obtain the information barrier performance of the stage production equipment.

[0019] Optionally, the monitoring-intensive time periods can be obtained, specifically including:

[0020] The timelines of all running data sequences are merged to obtain a multimodal data timeline. The duration of adjacent moments in the multimodal data timeline is determined as the clustering distance metric. The moments in the multimodal data timeline are clustered to obtain moment clusters. The length of the multimodal data timeline is consistent with the preset monitoring period.

[0021] For each time-based cluster, the time interval between the earliest and latest times is defined as the intensive monitoring period for the stage production equipment.

[0022] Optionally, based on the period of intensive monitoring and the performance of information barriers, the information fusion support capability in the calculation stage is specifically included, including:

[0023] The m-th intensive monitoring period for production equipment during the acquisition phase;

[0024] The product of the number of data sequence types running in the m-th dense monitoring period and the shortest time interval between the m-th dense monitoring period and other dense monitoring periods is determined as the dense segment fusion contribution value of the m-th dense monitoring period, where m is a positive integer;

[0025] Calculate the sum of the dense segment fusion contribution values ​​for all densely monitored time periods to obtain the total dense segment fusion contribution value;

[0026] The information fusion basic capability coefficient is obtained by taking the reciprocal of the information barrier performance of the stage production equipment.

[0027] The product of the basic information fusion capability coefficient, the number of monitoring intensive time periods, and the total contribution value of fusion in intensive periods is determined as the stage information fusion support capability of the stage production equipment.

[0028] Optionally, process-wide interference equipment can be identified from the stage production equipment, specifically including:

[0029] Iterate through all production stages and record a list of production stages for each production device.

[0030] Production equipment in a production stage list with a length greater than 1 is identified as a whole-process interference device.

[0031] Optionally, based on the number of conflict interference stages and the fusion support capability of stage information of the interference equipment throughout the entire process, the sustained interference strength is calculated, specifically including:

[0032] The stage information fusion support force of the jamming equipment in the wth conflict jamming stage is determined as the wth stage support force.

[0033] The average value of the stage information fusion support force of the jamming equipment in other conflict jamming stages is determined as the jamming stage baseline support force, where other conflict jamming stages are all conflict jamming stages except the w-th conflict jamming stage, and w is a positive integer.

[0034] The absolute value of the difference between the support force in stage w and the baseline support force in the disturbance stage is determined as the stage support force offset.

[0035] The stage support force offset of the jamming equipment at each conflict jamming stage is obtained and summed to obtain the total support force offset;

[0036] Divide the total support force offset by the number of conflict interference stages to obtain the average offset. Normalize the average offset to obtain the maintenance interference strength.

[0037] Optionally, the degree of retention of true anomalies is calculated based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process, specifically including:

[0038] Calculate the standard deviation of each operational data sequence of the interfering equipment throughout the entire process;

[0039] Calculate the mean of the standard deviations of all operating data sequences of the interference equipment throughout the entire process to obtain the overall mean of the standard deviations;

[0040] The absolute value of the difference between the standard deviation of the nth operating data sequence of the interference device and the overall mean of the standard deviation is determined as the fluctuation deviation value of the nth operating data sequence.

[0041] The fluctuation deviation value of each operating data sequence of the interference device throughout the entire process is obtained and summed to obtain the true anomaly retention degree.

[0042] Optionally, the information fusion rationality of each interference device throughout the entire process is calculated based on the interference fusion support capability, specifically including:

[0043] For each interference device in the entire process, the conflict interference stage corresponding to the maximum interference fusion support capability is determined as the target stage;

[0044] Obtain all operational data sequences and their corresponding physical units in the target phase;

[0045] Sequences of operational data with the same physical unit are grouped into the same unit group;

[0046] Get the number of running data sequences in each unit group;

[0047] The maximum number of data sequences running in all unit groups is determined as the maximum number of sequences in the same unit.

[0048] The ratio of the maximum number of identical unit sequences to the total number of running data sequences in the target stage is determined as the unit matching degree;

[0049] The product of the maximum interference removal fusion support and the unit matching degree is determined as the initial information fusion rationality.

[0050] The initial information fusion rationality is normalized to obtain the information fusion rationality.

[0051] Optionally, the information fusion rationality of each non-full-process interference device is calculated, specifically including:

[0052] The stage production equipment excluding the whole-process interference equipment is defined as the non-whole-process interference equipment.

[0053] For each non-full-process interference device, obtain the number of types of operational data sequences;

[0054] Obtain the total number of duplicate types of the operational data sequences in the production stage corresponding to non-full-process interference equipment;

[0055] The ratio of the number of categories to the total number of categories is determined as the data coverage.

[0056] The product of the phase information fusion support and data coverage of non-full-process interference equipment is normalized to obtain the information fusion rationality of non-full-process interference equipment.

[0057] This invention proposes an intelligent management system for the entire lifecycle operation data of coal preparation plant production equipment, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment.

[0058] The beneficial effects of the technical solution of the present invention are:

[0059] In this embodiment of the invention, based on the operational data of each production device, the degree of support provided by the equipment at different production stages for the entire lifecycle maintenance, as well as the information fusion status between devices, are analyzed, and then the rationality of information fusion for each production device is calculated. This method quantifies the support effect of each device on the entire lifecycle of coal production from macro to micro levels, achieving intelligent classification and differentiated management of monitoring and operational data, thereby breaking through the "information silos" between devices and realizing effective data fusion and unified management. By quantifying the rationality of information fusion between devices, cross-stage and cross-device data fusion is achieved, forming a unified data view for the entire lifecycle. Based on the fused macro data support, the system can make more accurate equipment status judgments and maintenance suggestions, improving the effectiveness of intelligent management. By prioritizing the processing of highly efficient fusion equipment, the operational stability of key equipment is ensured, achieving preventative maintenance and optimized resource allocation. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment, provided in one embodiment of the present invention;

[0062] Figure 2 This is a structural diagram of an intelligent management system for the entire lifecycle operation data of coal preparation plant production equipment, provided in one embodiment of the present invention. Detailed Implementation

[0063] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for intelligent management of the entire lifecycle operation data of coal preparation plant production equipment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0065] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent management method for the entire life cycle operation data of coal preparation plant production equipment provided by the present invention.

[0066] This invention provides an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of an intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to an embodiment of the present invention. The method includes the following steps:

[0067] S101. Obtain operational data of coal production within a preset monitoring period, wherein the operational data includes at least two production stages, each production stage includes at least two production equipment, and each production equipment includes at least one operational data sequence.

[0068] For example, considering the cost and time constraints in actual operation and maintenance, coal preparation plants usually obtain the operating data of all production equipment within a complete monitoring cycle by periodically exporting the data, and then conduct maintenance analysis and decision-making based on this data.

[0069] In one specific embodiment of this application, the preset monitoring period is set to 10 days, that is, the system by default collects the operating data sequence generated by all production equipment in all stages within the period every 10 days, as the data input for subsequent full life cycle intelligent management.

[0070] It should be further explained that: the differences in operating data sequences: the types of operating data sequences (i.e., monitoring parameter types) and sampling frequencies of the operating data sequences collected by production equipment at different stages are usually different.

[0071] Pre-defined division of production stages: During system initialization, all production equipment is pre-assigned to different production stages based on the coal production process. For example, the raw coal stage may include equipment such as crushers and vibrating screens; the sorting stage may include equipment such as jigs and separators.

[0072] The entire life cycle of coal production consists of three sequential production stages: raw coal stage → sorting stage → post-processing stage.

[0073] From the perspective of whole life cycle management, the negative impact of the "information silos" (i.e., data barriers) that exist between the aforementioned production stages is particularly significant. Although each production stage carries a clear technological objective (e.g., the raw coal stage aims to remove impurities; the sorting stage aims to separate coal according to quality; the post-processing stage aims to remove residual reagents to ensure product usability), the "information silos" phenomenon exists not only between stages but also within the production equipment itself because each production stage contains multiple different stage production equipment.

[0074] Therefore, in order to effectively optimize the interference caused by cross-stage "information silos", we should first start from the micro level: based on the operation data of production equipment in each stage, analyze the degree of support of each stage's production equipment for the life cycle of that production stage, and then calculate the stage information fusion support capacity of each stage's production equipment, laying the foundation for subsequent macro-conflict analysis and global fusion.

[0075] Taking any production stage as an example, we statistically analyze all the production equipment included in that stage, defining it as the set of production equipment for the current stage. For each piece of production equipment in the set, we construct its operational data sequence based on the raw operational data collected. It should be noted that a production stage may contain multiple stages of production equipment, and a single piece of production equipment may correspond to one or more operational data sequences.

[0076] S102. For each production equipment in each production stage, calculate the information barrier performance based on the length and sampling frequency of each running data sequence, obtain the monitoring intensive time period, and calculate the stage information fusion support based on the monitoring intensive time period and the information barrier performance.

[0077] In this embodiment, the information barrier performance of the production equipment at each stage is calculated based on the length and sampling frequency of each running data sequence, specifically including:

[0078] The product of the length of the nth running data sequence and the sampling frequency is used to determine the integrity of the nth running data sequence.

[0079] Obtain the completeness sum of other running data sequences, where other running data sequences are the running data sequences in the stage production equipment excluding the nth running data sequence;

[0080] Calculate the absolute value of the difference between the integrity of the nth running data sequence and the sum of the integrity of other running data sequences to obtain the information barrier performance of the nth running data sequence, where n is a positive integer;

[0081] The information barrier performance of each running data sequence is summed to obtain the information barrier performance of the stage production equipment.

[0082] The monitoring intensive time periods are identified, specifically including:

[0083] The timelines of all running data sequences are merged to obtain a multimodal data timeline. The duration of adjacent moments in the multimodal data timeline is determined as the clustering distance metric. The moments in the multimodal data timeline are clustered to obtain moment clusters. The length of the multimodal data timeline is consistent with the preset monitoring period.

[0084] For each time-based cluster, the time interval between the earliest and latest times is defined as the intensive monitoring period for the stage production equipment.

[0085] Based on the intensive monitoring periods and information barrier performance, the information fusion support capability in the computation phase specifically includes:

[0086] The m-th intensive monitoring period for production equipment during the acquisition phase;

[0087] The product of the number of data sequence types running in the m-th dense monitoring period and the shortest time interval between the m-th dense monitoring period and other dense monitoring periods is determined as the dense segment fusion contribution value of the m-th dense monitoring period, where m is a positive integer;

[0088] Calculate the sum of the dense segment fusion contribution values ​​for all densely monitored time periods to obtain the total dense segment fusion contribution value;

[0089] The information fusion basic capability coefficient is obtained by taking the reciprocal of the information barrier performance of the stage production equipment.

[0090] The product of the basic information fusion capability coefficient, the number of monitoring intensive time periods, and the total contribution value of fusion in intensive periods is determined as the stage information fusion support capability of the stage production equipment.

[0091] For example, taking any stage of production equipment as an example, based on its corresponding several operational data sequences, the information barrier performance of the production equipment at that stage is as follows: The calculation formula can be:

[0092]

[0093] in, This indicates the total number of operational data sequences contained in the production equipment at this stage. This indicates the length of the nth sequence of operational data for the device. This indicates the sampling frequency of the nth data sequence of the device. This indicates the number of all running data sequences within the production equipment at this stage, excluding the nth running data sequence. This represents the length of the n1-th running data sequence, excluding the nth running data sequence. This represents the sampling frequency of the n1th running data sequence, excluding the nth running data sequence. The product of the length L and the sampling frequency T, L×T, is used to characterize the completeness of the information of the running data sequence in its respective data dimension.

[0094] Information barriers manifest The larger the value, the more significant the information barrier between different data dimensions (i.e. different operating data sequences) within the production equipment at that stage, reflecting the weaker the support of the equipment in representing the information of its production stage, that is, the worse the foundation for internal data fusion.

[0095] An initially empty timeline is constructed, and the acquisition time corresponding to each data point recorded in each operational data sequence of the production equipment in this stage is placed into this timeline. The integrated timeline is called the multidimensional operational timeline (also known as the multimodal data timeline) of the production equipment in this stage. Each stage of production equipment corresponds to an independent multidimensional operational timeline, which contains only a series of time points, and there may be multiple operational data sequences that acquire data at the same time, that is, duplicate time points are allowed within the timeline.

[0096] It should be noted that the total duration covered by this timeline must be consistent with the system's preset monitoring period (e.g., 10 days).

[0097] On the constructed multidimensional operating timeline, the time difference between adjacent moments is calculated and used as a clustering distance metric. Based on this metric, the DBSCAN density clustering algorithm is applied to all moments on the timeline to obtain several moment clusters. For each moment cluster, the interval between the earliest and latest moments covered by it on the multidimensional operating timeline is defined as a dense monitoring period for the production equipment in that stage.

[0098] Based on the period of intensive monitoring and the performance of information barriers Calculate the stage information fusion support capability of production equipment at this stage. The calculation formula used can be:

[0099]

[0100] in, This indicates the total number of monitoring-intensive time periods for the production equipment during this phase. This represents the number of types of operational data sequences included within the m-th intensive monitoring time period. This represents the shortest time interval between the m-th intensive monitoring period and the other intensive monitoring periods.

[0101] Based on the above parameters Defined as the dense segment fusion contribution value of the m-th dense monitoring time period. Defined as the basic capability coefficient for information fusion, where This demonstrates the information barrier capabilities of the device. Defined as the total contribution value of densely monitored time periods.

[0102] Phase Information Fusion Support The higher the value, the higher the temporal overlap and information integration of the multi-dimensional operational data reflected in the intensive monitoring period of the production equipment during that stage. This reflects the inherent strong basic capability of the equipment in information fusion of various dimensions, meaning that the actual obstacles encountered in the fusion process are relatively smaller, thus providing better support for information fusion in the production stage.

[0103] Thus, through the above steps, the stage information fusion support capability of each stage of production equipment within this production stage can be calculated and obtained. .

[0104] S103. Identify the whole-process interference equipment from the stage production equipment, and determine the production stage of the whole-process interference equipment as the conflict interference stage. For each whole-process interference equipment, calculate the maintenance interference intensity based on the number of conflict interference stages and the stage information fusion support of the whole-process interference equipment.

[0105] In this embodiment, the interference devices that disrupt the entire process are identified from the stage production equipment, specifically including:

[0106] Iterate through all production stages and record a list of production stages for each production device.

[0107] Production equipment in a production stage list with a length greater than 1 is identified as a whole-process interference device.

[0108] Based on the number of conflict interference phases and the fusion support capability of phase information from all interference devices throughout the entire process, the sustained interference strength is calculated, specifically including:

[0109] The stage information fusion support force of the jamming equipment in the wth conflict jamming stage is determined as the wth stage support force.

[0110] The average value of the stage information fusion support force of the jamming equipment in other conflict jamming stages is determined as the jamming stage baseline support force, where other conflict jamming stages are all conflict jamming stages except the w-th conflict jamming stage, and w is a positive integer.

[0111] The absolute value of the difference between the support force in stage w and the baseline support force in the disturbance stage is determined as the stage support force offset.

[0112] The stage support force offset of the jamming equipment at each conflict jamming stage is obtained and summed to obtain the total support force offset;

[0113] Divide the total support force offset by the number of conflict interference stages to obtain the average offset. Normalize the average offset to obtain the maintenance interference strength.

[0114] For example, through the aforementioned steps, information fusion analysis of production equipment at each stage within the same production phase has been completed, and the calculated stage information fusion support capability has been obtained. It can effectively characterize the degree to which each production stage is affected by its internal "information silos".

[0115] Under conventional analytical approaches, the focus should be on further investigating the interactive interference caused by "information silos" between different production stages. However, a crucial unique characteristic exists in actual coal production processes: different production stages may deploy the same type of production equipment (here, "same type" specifically refers to the sampling frequency and the type of operational data sequence collected being consistent). For example, a vibrating screen may be used in both the raw coal stage and the sorting stage.

[0116] These types of production equipment, even if they are of the same model, often exhibit different operating states and data characteristics due to the different technological purposes they serve at different production stages. Consequently, they demonstrate varying levels of support for stage-specific information fusion at different stages. Value. Given that the entire coal production process is completed collaboratively across all production stages, failure to properly address the discrepancies in the level of information fusion support exhibited by similar equipment at different stages will directly constrain the overall efficiency of cross-stage information fusion management.

[0117] Therefore, in order to achieve true full lifecycle data intelligent management, after completing the phase analysis, the support capability is fused based on the phase information calculated for each production device. It specifically analyzes the conflicts and interferences caused by the same model of production equipment in different production stages on the maintenance of the entire life cycle, and calculates the final information fusion rationality of each production equipment accordingly.

[0118] By traversing all production stages, production equipment that appears in at least two different production stages is identified and marked as a process-wide interference device.

[0119] Analyzing conflict interference and calculating the sustained interference strength: Taking any fully-process interference device as an example, all its production stages are defined as the set of conflict interference stages for that device. Subsequently, the stage information fusion support capability of the device in each conflict interference stage is analyzed. Calculate the maintenance disturbance intensity of this equipment on the entire life cycle maintenance of coal. The specific calculation formula is as follows:

[0120]

[0121] in, This indicates the total number of conflict interference stages corresponding to the interference equipment throughout the entire process. This indicates the phase information fusion support capability of the jamming device in its w-th conflict jamming phase. This represents the average value of the stage information fusion support force of the jamming device in all jamming stages except for the w-th jamming stage, and is denoted as the jamming stage baseline support force of the w-th jamming stage.

[0122] Based on the above parameters Let this be denoted as the stage support force offset for the w-th conflict / interference stage. This is recorded as the total support force offset of the device.

[0123] Maintaining interference strength The larger the value, the more frequently the interference equipment is used across stages in the coal production process, and the more drastic the fluctuations in the support force formed by information fusion when it operates in different production stages. This directly reflects the stronger the interference conflict generated by the equipment in maintaining the continuous and stable operation of coal throughout its entire life cycle.

[0124] It should be noted that in the calculation If, except for the w-th conflict / interference phase, there is only one other conflict / interference phase, then the baseline support force for the interference phase is... The stage information fusion support value of the remaining stage is directly taken.

[0125] S104. For each stage of conflict interference, calculate the degree of retention of the true anomaly based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process.

[0126] In this embodiment, the degree of preservation of the true anomaly is calculated based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process, specifically including:

[0127] Calculate the standard deviation of each operational data sequence of the interfering equipment throughout the entire process;

[0128] Calculate the mean of the standard deviations of all operating data sequences of the interference equipment throughout the entire process to obtain the overall mean of the standard deviations;

[0129] The absolute value of the difference between the standard deviation of the nth operating data sequence of the interference device and the overall mean of the standard deviation is determined as the fluctuation deviation value of the nth operating data sequence.

[0130] The fluctuation deviation value of each operating data sequence of the interference device throughout the entire process is obtained and summed to obtain the true anomaly retention degree.

[0131] For example, the same interference device throughout the entire process can cause different interference effects at different production stages, which will inevitably have varying degrees of impact on the anomalies identified based on the original monitoring and operational data at each stage. Accurately identifying and retaining these anomalies is precisely the goal of intelligent monitoring and management of operational data.

[0132] To quantify the true level of anomaly in intra-phase operational data affected by cross-phase equipment interference, it is necessary to calculate the degree of true anomaly retention. Specifically, taking any conflict interference stage of the entire interference device as an example, the steps are as follows:

[0133] Calculate the standard deviation of the operational data sequences: During this conflict phase, calculate the standard deviation of each operational data sequence for the device. . The larger the value, the more unstable the data fluctuation of the running data sequence, reflecting a higher probability of anomalies.

[0134] Calculate the degree of preservation of true anomalies: based on the standard deviation of all running data sequences within this phase. Calculate the true anomaly retention level during this conflict interference phase. The specific calculation formula can be:

[0135]

[0136] in, This indicates the total number of operational data sequences contained in the jamming device during the current conflict jamming phase. This represents the standard deviation of the n2th running data sequence within this stage. This represents the mean of the standard deviations of all running data sequences during the conflict and interference phase, i.e., the overall mean of the standard deviations.

[0137] True anomaly retention level The larger the value, the more significant the deviation of the abnormal fluctuation characteristics of the interfering equipment's operating data from the overall average level during the current conflict interference phase. This reflects a higher probability that the equipment exhibits genuine anomalies during this phase. Therefore, in subsequent information fusion processes, it is necessary to retain and consider more of the abnormal information contained in this phase.

[0138] S105. Based on the maintenance interference intensity of each full-process interference device and the actual anomaly retention degree of each conflict interference stage, calculate the de-interference fusion support force of each full-process interference device in each conflict interference stage.

[0139] In this embodiment, based on the sustained interference strength of each full-process interference device and the degree of preservation of the actual anomaly in each conflict interference stage, the de-interference fusion support capability of each full-process interference device in each conflict interference stage is calculated, specifically including:

[0140] For each full-process jamming device, obtain the sustained jamming strength of that jamming device;

[0141] For each stage of the interference of the entire process of the interference device, obtain the actual degree of anomaly retention in that stage;

[0142] Divide the actual anomaly retention level of the conflict interference phase by the maintenance interference intensity of the full-process interference device to obtain the de-interference fusion support force of the full-process interference device in the conflict interference phase.

[0143] Traverse all conflict and interference stages of the entire process interference device and obtain the interference fusion support capability of the entire process interference device in all conflict and interference stages;

[0144] Iterate through all jamming devices throughout the entire process and obtain the de-jamming and fusion support capabilities of all jamming devices at all conflict and jamming stages.

[0145] For example, based on the calculated true anomaly retention level With maintaining interference strength Calculate the de-interference fusion support capability of the jamming equipment in the current conflict jamming phase throughout the entire process. The calculation formula is:

[0146]

[0147] Disruption fusion support The larger the value, the better it indicates that the interference caused by the device being used across different stages has been offset (by...). Following characterization, the operational data monitored during the current conflict disruption phase, due to the significant anomalous information it contains (by... (Characteristics) The better the effective support that the continuous and stable operation of coal throughout its entire life cycle can provide.

[0148] S106. Calculate the information fusion rationality of each full-process interference device based on the interference fusion support capability, and calculate the information fusion rationality of each non-full-process interference device.

[0149] In this embodiment, the information fusion rationality of each interference device throughout the entire process is calculated based on the interference removal fusion support capability, specifically including:

[0150] For each interference device in the entire process, the conflict interference stage corresponding to the maximum interference fusion support capability is determined as the target stage;

[0151] Obtain all operational data sequences and their corresponding physical units in the target phase;

[0152] Sequences of operational data with the same physical unit are grouped into the same unit group;

[0153] Get the number of running data sequences in each unit group;

[0154] The maximum number of data sequences running in all unit groups is determined as the maximum number of sequences in the same unit.

[0155] The ratio of the maximum number of identical unit sequences to the total number of running data sequences in the target stage is determined as the unit matching degree;

[0156] The product of the maximum interference removal fusion support and the unit matching degree is determined as the initial information fusion rationality.

[0157] The initial information fusion rationality is normalized to obtain the information fusion rationality.

[0158] Calculate the rationality of information fusion for each non-full-process interference device, specifically including:

[0159] The stage production equipment excluding the whole-process interference equipment is defined as the non-whole-process interference equipment.

[0160] For each non-full-process interference device, obtain the number of types of operational data sequences;

[0161] Obtain the total number of duplicate types of the operational data sequences in the production stage corresponding to non-full-process interference equipment;

[0162] The ratio of the number of categories to the total number of categories is determined as the data coverage.

[0163] The product of the phase information fusion support and data coverage of non-full-process interference equipment is normalized to obtain the information fusion rationality of non-full-process interference equipment.

[0164] For example, in addition to the above-mentioned data fluctuations ( ) and cross-stage interference ( Besides the analysis of ), in the process of information fusion, the uniformity of dimensions is another intuitive and key factor that affects the formation of information barriers and restricts the fusion effect.

[0165] Therefore, in order to obtain a final comprehensive assessment of the feasibility of data fusion for the entire process of interference equipment, the rationality of information fusion for the entire process of interference equipment is calculated by comprehensively analyzing its maximum anti-interference fusion support and the degree of dimensional uniformity. The calculation formula can be:

[0166]

[0167] in, This indicates that the interference device provides de-interference fusion support throughout all stages of its interference process. The maximum value, Indicates in In the corresponding conflict and interference stage (called the target stage), the total number of different physical units used by all running data sequences (i.e., the total number of dimensions after deduplication). This indicates the number of sequences contained in the largest group of running data sequences with the same physical unit during the target phase.

[0168] Reasonableness of information fusion The larger the value, the higher the support efficiency of the interference device during the data fusion process, considering its optimal performance stage. ) and the uniformity of units of data at this stage ( The smaller the actual obstacles to data integration throughout the entire life cycle of coal production, the higher the rationality of its data participation in integration.

[0169] It should be noted that the rationality of information fusion in computation is important. In this case, the parameters K and k are statistically represented as follows: K is the total number of running data sequences in the target stage. k is the number of sequences contained in the largest group of running data sequences with the same physical unit in the target stage (i.e., the maximum number of sequences with the same unit).

[0170] For example, if the physical units of the 7 running data sequences in the target stage are: seconds, seconds, seconds, meters, meters, grams, and amperes, then the groups with the same units are: the "seconds" group (3 sequences), the "meters" group (2 sequences), the "grams" group (1 sequence), and the "amperes" group (1 sequence). In this case, K=7 (total number of sequences). k=3 (because the "seconds" group contains the most sequences, which is 3).

[0171] Furthermore, for each stage of production equipment other than the full-process interference equipment (non-full-process interference equipment), the rationality of information fusion for non-full-process interference equipment is... The calculation formula can be:

[0172]

[0173] in, This indicates the ability of non-full-process interference equipment to support stage information fusion at its respective production stage. This indicates the number of types of operational data sequences monitored by non-full-process interference equipment. This represents the total number of unique data sequences in the production phase of the equipment, after deduplication. This represents the normalization function.

[0174] Based on the above parameters Indicates data coverage. Indicates the initial reasonableness. Reasonableness of information fusion for non-full-process interference equipment. The larger the size, the stronger its integration support during the stage ( The higher the level of data, the greater the coverage of its data types to the total number of types in the stage. The larger it is.

[0175] S107. Mark devices whose information fusion rationality is greater than the preset rationality threshold as high-efficiency devices, and perform data fusion on the operating data of high-efficiency devices.

[0176] For example, based on the calculated reasonableness of information fusion, all production equipment is classified and managed as follows:

[0177] Equipment Classification: Production equipment with an information fusion rationality greater than a preset rationality threshold (e.g., 0.6) is marked as high-efficiency fusion production equipment; the rest of the equipment is marked as low-efficiency fusion production equipment.

[0178] Data storage restructuring: Build a new, independent database. Migrate all operational data (including historical and real-time data) monitored by all high-efficiency integrated production equipment to this new database for storage.

[0179] Fusion and Display Strategy: In the subsequent data fusion processing stage (a suitable data fusion algorithm can be selected according to the specific application scenario), the system will prioritize the fusion calculation of the running data in the new database. The fusion result will be displayed as high-value information in the display module of the data processing platform.

[0180] Data usage principle: Unless there is a specific analytical need, the system will not call or use the operating data from inefficient integrated production equipment stored in the original database during the regular full life cycle intelligent management process.

[0181] Optionally, the preset reasonableness threshold can be dynamically configured and adjusted according to actual business needs or historical operation and maintenance experience. This application does not limit its specific value. In a preferred embodiment, it can be set to 0.6.

[0182] In summary, this invention analyzes the support level of equipment at different production stages for the entire lifecycle maintenance based on operational data from each production unit, as well as the information fusion status between equipment, and then calculates the information fusion rationality of each production unit. This method quantifies the support effect of each unit on the entire lifecycle of coal production from macro to micro perspectives, enabling intelligent classification and differentiated management of monitoring and operational data. This breaks down the "information silos" between equipment, achieving effective data fusion and unified management. By quantifying the information fusion rationality between equipment, cross-stage and cross-equipment data fusion is achieved, forming a unified data view for the entire lifecycle. Based on the fused macro data, the system can make more accurate equipment status judgments and maintenance suggestions, improving the effectiveness of intelligent management. By prioritizing highly integrated equipment, the system focuses on ensuring the operational stability of key equipment, achieving preventative maintenance and optimized resource allocation.

[0183] This invention also proposes an intelligent management system for the entire lifecycle operation data of coal preparation plant production equipment. Please refer to [link / reference]. Figure 2 The diagram illustrates the structure of an intelligent management system for the full lifecycle operation data of coal preparation plant production equipment according to an embodiment of the present invention. The system includes: a data acquisition module 101, a data processing module 102, and a data fusion module 103.

[0184] The data acquisition module 101 is used to acquire the operation data of coal production within a preset monitoring period. The operation data includes at least two production stages, each production stage includes at least two production equipment, and each production equipment includes at least one operation data sequence.

[0185] Data processing module 102 is used to calculate the information barrier performance for each production equipment in each production stage, based on the length and sampling frequency of each running data sequence, obtain the monitoring intensive time period, and calculate the stage information fusion support based on the monitoring intensive time period and the information barrier performance.

[0186] The entire process interference equipment is identified from the stage production equipment. The production stage of the entire process interference equipment is identified as the conflict interference stage. For each entire process interference equipment, the maintenance interference intensity is calculated based on the number of conflict interference stages and the stage information fusion support of the entire process interference equipment.

[0187] For each stage of conflict interference, the degree of retention of the true anomaly is calculated based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process;

[0188] Based on the maintenance interference intensity of each full-process jamming device and the actual anomaly retention degree of each conflict jamming stage, calculate the de-jamming fusion support capacity of each full-process jamming device in each conflict jamming stage;

[0189] The information fusion rationality of each full-process interference device is calculated based on the interference fusion support capability, and the information fusion rationality of each non-full-process interference device is also calculated.

[0190] The data fusion module 103 is used to mark devices whose information fusion rationality is greater than a preset rationality threshold as high-efficiency devices, and to perform data fusion on the operating data of high-efficiency devices.

[0191] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent management system for the full life cycle operation data of coal preparation plant production equipment and the intelligent management method for the full life cycle operation data of coal preparation plant production equipment provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0192] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0193] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0194] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent management of the entire lifecycle operation data of coal preparation plant production equipment, characterized in that, include: Obtain operational data of coal production within a preset monitoring period. The operational data includes at least two production stages, each production stage includes at least two production equipment stages, and each production equipment stage includes at least one operational data sequence. For each production equipment in each production stage, the information barrier performance is calculated based on the length and sampling frequency of each running data sequence, the monitoring intensive time period is obtained, and the stage information fusion support is calculated based on the monitoring intensive time period and the information barrier performance. The entire process interference equipment is identified from the stage production equipment. The production stage of the entire process interference equipment is identified as the conflict interference stage. For each entire process interference equipment, the maintenance interference intensity is calculated based on the number of conflict interference stages and the stage information fusion support of the entire process interference equipment. For each stage of conflict interference, the degree of retention of the true anomaly is calculated based on the standard deviation of the operating data sequence of the interference equipment throughout the entire process; Based on the maintenance interference intensity of each full-process jamming device and the actual anomaly retention degree of each conflict jamming stage, calculate the de-jamming fusion support capacity of each full-process jamming device in each conflict jamming stage; The information fusion rationality of each full-process interference device is calculated based on the interference fusion support capability, and the information fusion rationality of each non-full-process interference device is also calculated. Devices with information fusion rationality greater than the preset rationality threshold are marked as high-efficiency devices, and the operating data of high-efficiency devices are fused.

2. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of the information barrier performance of production equipment at each stage, based on the length and sampling frequency of each running data sequence, specifically includes: The product of the length of the nth running data sequence and the sampling frequency is used to determine the integrity of the nth running data sequence. Obtain the completeness sum of other running data sequences, where other running data sequences are the running data sequences in the stage production equipment excluding the nth running data sequence; Calculate the absolute value of the difference between the integrity of the nth running data sequence and the sum of the integrity of other running data sequences to obtain the information barrier performance of the nth running data sequence, where n is a positive integer; The information barrier performance of each running data sequence is summed to obtain the information barrier performance of the stage production equipment.

3. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The acquisition of the dense monitoring time period specifically includes: The timelines of all running data sequences are merged to obtain a multimodal data timeline. The duration of adjacent moments in the multimodal data timeline is determined as the clustering distance metric. The moments in the multimodal data timeline are clustered to obtain moment clusters. The length of the multimodal data timeline is consistent with the preset monitoring period. For each time-based cluster, the time interval between the earliest and latest times is defined as the intensive monitoring period for the stage production equipment.

4. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of information fusion support capabilities during the intensive monitoring period and the performance of information barriers specifically includes: The m-th intensive monitoring period for production equipment during the acquisition phase; The product of the number of data sequence types running in the m-th dense monitoring period and the shortest time interval between the m-th dense monitoring period and other dense monitoring periods is determined as the dense segment fusion contribution value of the m-th dense monitoring period, where m is a positive integer; Calculate the sum of the dense segment fusion contribution values ​​for all densely monitored time periods to obtain the total dense segment fusion contribution value; The information fusion basic capability coefficient is obtained by taking the reciprocal of the information barrier performance of the stage production equipment. The product of the basic information fusion capability coefficient, the number of monitoring intensive time periods, and the total contribution value of fusion in intensive periods is determined as the stage information fusion support capability of the stage production equipment.

5. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The process of identifying interference devices from the stage production equipment specifically includes: Iterate through all production stages and record a list of production stages for each production device. Production equipment in a production stage list with a length greater than 1 is identified as a whole-process interference device.

6. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of the sustained interference strength based on the number of conflict interference stages and the fusion support of stage information of the interference equipment throughout the entire process specifically includes: The stage information fusion support force of the jamming equipment in the wth conflict jamming stage is determined as the wth stage support force. The average value of the stage information fusion support force of the jamming equipment in other conflict jamming stages is determined as the jamming stage baseline support force, where other conflict jamming stages are all conflict jamming stages except the w-th conflict jamming stage, and w is a positive integer. The absolute value of the difference between the support force in stage w and the baseline support force in the disturbance stage is determined as the stage support force offset. The stage support force offset of the jamming equipment at each conflict jamming stage is obtained and summed to obtain the total support force offset; Divide the total support force offset by the number of conflict interference stages to obtain the average offset. Normalize the average offset to obtain the maintenance interference strength.

7. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of the true anomaly retention level based on the standard deviation of the operational data sequence of the interference equipment throughout the entire process specifically includes: Calculate the standard deviation of each operational data sequence of the interfering equipment throughout the entire process; Calculate the mean of the standard deviations of all operating data sequences of the interference equipment throughout the entire process to obtain the overall mean of the standard deviations; The absolute value of the difference between the standard deviation of the nth operating data sequence of the interference device and the overall mean of the standard deviation is determined as the fluctuation deviation value of the nth operating data sequence. The fluctuation deviation values ​​of each operating data sequence of the interference device throughout the entire process are obtained and summed to obtain the true anomaly retention level.

8. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of the information fusion rationality of each interference device throughout the entire process based on the interference fusion support capability specifically includes: For each interference device in the entire process, the conflict interference stage corresponding to the maximum interference fusion support capability is determined as the target stage; Obtain all operational data sequences and their corresponding physical units in the target phase; Sequences of operational data with the same physical unit are grouped into the same unit group; Get the number of running data sequences in each unit group; The maximum number of data sequences running in all unit groups is determined as the maximum number of sequences in the same unit. The ratio of the maximum number of identical unit sequences to the total number of running data sequences in the target stage is determined as the unit matching degree; The product of the maximum interference removal fusion support and the unit matching degree is determined as the initial information fusion rationality. The initial information fusion rationality is normalized to obtain the information fusion rationality.

9. The intelligent management method for the entire lifecycle operation data of coal preparation plant production equipment according to claim 1, characterized in that, The calculation of the information fusion rationality of each non-full-process interference device specifically includes: The stage production equipment excluding the whole-process interference equipment is defined as the non-whole-process interference equipment. For each non-full-process interference device, obtain the number of types of operational data sequences; Obtain the total number of duplicate types of the operational data sequences in the production stage corresponding to non-full-process interference equipment; The ratio of the number of categories to the total number of categories is determined as the data coverage. The product of the phase information fusion support and data coverage of non-full-process interference equipment is normalized to obtain the information fusion rationality of non-full-process interference equipment.

10. A smart management system for the entire lifecycle operation data of coal preparation plant production equipment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent management method for the full life cycle operation data of coal preparation plant production equipment as described in any one of claims 1-9.

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