Intelligent over-production violation identification method and system based on big data analysis

Abstract

The invention discloses an intelligent over-production violation identification method and system based on big data analysis, and relates to the technical field of safety production. The method comprises the following steps: obtaining power monitoring data and production monitoring data of a coal mine enterprise; comparing and analyzing the power monitoring data and the production monitoring data to obtain an analysis result; the super-capacity production behavior of the coal mine is judged based on the analysis result; and feeding back a judgment result and visually displaying the judgment result. By comprehensively comparing and analyzing the power consumption condition of the coal mine enterprise and other production information, whether the target coal mine enterprise has super-capability production behaviors or not is intelligently judged, and the super-capability production behaviors are fed back to departments involving safety production in time, so that the safety risk caused by illegal production is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of safe production, and in particular to an intelligent identification method and system for overproduction violations based on big data analysis. Background Art

[0002] For coal mining enterprises, safe production is the most important part of the industrial production process; due to the high safety risks in coal mining, overproduction is one of the main causes of safety accidents. Therefore, finding and identifying overproduction coal mining enterprises to manage and restrict their overproduction behavior can effectively reduce the incidence of safety accidents and reduce the losses caused by such risks.

[0003] At present, the informatization construction of safety production supervision for coal mining enterprises is usually based on comparing the production data with the actual enterprise scale to determine whether the target coal mining enterprise has overproduction. In this method, the overall intelligence level of the supervision system is very low, and only after the target coal mining enterprise has completed production for a period of time, can the statistical production data be used to determine whether there is overproduction. This also leads to serious lags, that is, the supervisors can only discover the overproduction after a period of time has occurred, and cannot prevent possible safety risks. The efficiency and immediacy of investigating and handling overproduction are both low. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an intelligent identification method and system for overproduction violations based on big data analysis to solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an intelligent identification method for overproduction violations based on big data analysis, including: obtaining power monitoring data and production monitoring data of a coal mining enterprise;

[0009] Comparatively analyzing the power monitoring data and the production monitoring data to obtain analysis results;

[0010] Determine the overcapacity production behavior of the coal mine based on the analysis results;

[0011] The judgment result is fed back and visualized.

[0012] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, wherein: the power monitoring data at least includes current power monitoring data, historical power monitoring data and power monitoring data of coal mines in the same region;

[0013] The production monitoring data at least includes personnel location monitoring data and water hazard monitoring data.

[0014] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, the comparative analysis of the power monitoring data and the production monitoring data includes:

[0015] Performing a year-on-year analysis based on the current power monitoring data and the historical power monitoring data to obtain a first analysis result;

[0016] Based on the comparison and analysis between the current power monitoring data and the power monitoring data of the coal mine in the same area, a second analysis result is obtained;

[0017] The production monitoring data is analyzed based on the preset production rules to obtain a third analysis result.

[0018] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, it also includes: obtaining a concentrated area of ​​personnel activities based on personnel positioning monitoring data, determining the number of mining operation points and working faces based on the concentrated area of ​​personnel activities, combined with the personnel substation name and personnel type information in the personnel positioning monitoring data; based on the number of working faces, analyzing and judging using preset production rules to obtain working face analysis results;

[0019] Based on the production monitoring data, the personnel location data corresponding to each mining operation point is determined to obtain the number of personnel corresponding to each mining operation point; according to the number of personnel corresponding to each mining operation point, the preset production rules are used to perform analysis and judgment to obtain the number of personnel analysis results;

[0020] According to the work surface analysis results and the number of workers analysis results, the third analysis result is obtained.

[0021] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, the overproduction behavior of a coal mine based on the analysis results includes:

[0022] If in the first analysis result, the current power monitoring data exceeds the historical power monitoring data and exceeds the first threshold;

[0023] Moreover, in the second analysis result, the current power monitoring data exceeds the power monitoring data of any other coal mining enterprise in the coal mine power monitoring data in the same region;

[0024] Moreover, in the third analysis result, in the production monitoring data, the number of operating faces exceeds the number of operating faces specified in the preset production rules, or the number of personnel corresponding to any mining operation point exceeds the limit number of personnel specified in the preset production rules;

[0025] It is determined that the coal mine has exceeded its capacity in production;

[0026] Otherwise, it is determined that the coal mine does not engage in overcapacity production.

[0027] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, wherein: the comparative analysis of the power monitoring data and the production monitoring data further comprises: generating an initial power prediction model based on a power data prediction function;

[0028] The power data prediction function is expressed as:

[0029]

[0030] Among them, f(X norm ) is the predicted value of power data, n is the number of data, i represents the i-th data, and β i represents the parameter coefficient in the optimal regression function, is the kernel function of support vector regression, and the function value is a vector and X norm The inner product of

[0031] Generate a training set and a test set based on historical power monitoring data in the power monitoring data;

[0032] Preprocessing current power monitoring data and historical power monitoring data;

[0033] The power prediction model is trained and tested using the training set and the test set to obtain a tested power prediction model;

[0034] The tested power forecasting model is used to forecast the power data of the target coal enterprise to obtain the power forecast data.

[0035] As a preferred solution of the method for intelligently identifying overproduction violations based on big data analysis described in the present invention, judging the overproduction behavior of a coal mine based on the analysis results also includes:

[0036] Compare power forecast data with current power monitoring data;

[0037] If the magnitude of the current power monitoring data is greater than the power forecast data by more than the preset magnitude threshold, it is determined that the coal mine has overcapacity production behavior;

[0038] Otherwise, it is determined that the coal mine does not engage in overcapacity production.

[0039] In a second aspect, the present invention provides an intelligent identification system for overproduction violations based on big data analysis, comprising:

[0040] Data acquisition module, used to obtain power monitoring data and production monitoring data of coal mining enterprises;

[0041] A data analysis module, used to compare and analyze the power monitoring data and the production monitoring data to obtain analysis results;

[0042] A judgment module, used for judging the overcapacity production behavior of the coal mine based on the analysis result;

[0043] The result feedback module is used to provide feedback and visually display the judgment result.

[0044] In a third aspect, the present invention provides a computing device, comprising:

[0045] Memory and processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for intelligently identifying overproduction violations based on big data analysis are implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for intelligently identifying overproduction violations based on big data analysis.

[0048] Compared with the prior art, the present invention has the following beneficial effects: the present invention makes the judgment result more accurate through comprehensive analysis results, greatly reduces the possibility of misjudgment, helps supervisors to discover overproduction, effectively improves the safety of production and operation in the coal industry and the timeliness of supervision, reduces safety risks, and, under the premise of fully ensuring production safety, protects the normal production rights and interests of various coal enterprises; wherein, the personnel positioning monitoring data in the production monitoring data is used to determine the mining operation points and working faces in the coal mining enterprise, and the number of working faces and the number of people at each mining operation point are obtained based on this, and then compared and analyzed with the relevant regulations on the number of working faces and the number of mining people, so that it can be more accurately determined whether the enterprise has overproduction, and further obtain relevant information on the specific working face or operation point of overproduction, so that supervisors can obtain more relevant information of the target coal mining enterprise, which helps them to locate and process more quickly in the subsequent supervision procedures, further reducing the safety risks in production. In addition, by setting up a power forecasting model for the target coal enterprise, training and testing it with historical power monitoring data, using the model to predict the power data, and comparing the predicted value with the monitoring data, it is determined whether there is overproduction behavior. This further improves the intelligence of the supervision system in the process of informatization construction of enterprise safety production supervision and inspection in the coal mining industry, helps to understand the production trends of the enterprises, and makes a more scientific judgment on whether there is overproduction behavior. Based on big data analysis and machine learning technology, the accuracy of such judgments has been greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 A method flow chart of an overproduction violation intelligent identification method and system based on big data analysis according to an embodiment of the present invention;

[0051] Figure 2 A flowchart of a method for analyzing working faces and the number of operators of an intelligent identification method for overproduction violations based on big data analysis and a system according to an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of an overproduction violation judgment process of an overproduction violation intelligent identification method and system based on big data analysis according to an embodiment of the present invention;

[0053] Figure 4A schematic diagram of an overproduction violation analysis and judgment process of an overproduction violation intelligent identification method and system based on big data analysis according to an embodiment of the present invention;

[0054] Figure 5 A system structure block diagram of an overproduction violation intelligent identification method and system based on big data analysis according to an embodiment of the present invention;

[0055] Figure 6 A schematic diagram of the structure of a computing device of a method and system for intelligently identifying overproduction violations based on big data analysis according to an embodiment of the present invention;

[0056] Figure 7 This is a comparison chart of annual electricity detection of a coal enterprise according to an intelligent identification method and system for overproduction violations based on big data analysis according to an embodiment of the present invention;

[0057] Figure 8 A comparison chart of monthly electricity consumption detection of a coal enterprise according to an intelligent identification method and system for overproduction violations based on big data analysis according to an embodiment of the present invention;

[0058] Fig. 9 A schematic diagram of a normal power consumption range of a coal mine according to an intelligent identification method and system for overproduction violations based on big data analysis according to an embodiment of the present invention;

[0059] Fig.10 A comparison chart of power monitoring data of coal enterprises in the same region according to an intelligent identification method and system for overproduction violations based on big data analysis according to an embodiment of the present invention;

[0060] Fig.11 A schematic diagram of overcapacity production judgment of a coal enterprise using an intelligent overproduction violation identification method and system based on big data analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Example 1

[0068] Reference Figure 1 to Figure 6 , is an embodiment of the present invention, which provides an intelligent identification method for overproduction violations based on big data analysis, including:

[0069] S101: Obtaining power monitoring data and production monitoring data of coal mining enterprises;

[0070] In the present application, the power monitoring data at least includes current power monitoring data, historical power monitoring data, and power monitoring data of coal mines in the same region;

[0071] The production monitoring data includes at least personnel location monitoring data and water hazard monitoring data.

[0072] S102: Compare and analyze the power monitoring data and the production monitoring data to obtain analysis results;

[0073] In this application example, the comparative analysis of power monitoring data and production monitoring data includes:

[0074] Performing year-on-year analysis based on current power monitoring data and historical power monitoring data to obtain a first analysis result;

[0075] Based on the comparative analysis of the current power monitoring data and the power monitoring data of coal mines in the same area, the second analysis result is obtained;

[0076] The production monitoring data is analyzed based on the preset production rules to obtain a third analysis result.

[0077] In the present application, the present application also includes: obtaining a personnel activity concentration area based on personnel positioning monitoring data; determining the number of mining operation points and working faces based on the personnel activity concentration area and combining the personnel substation name and personnel type information in the personnel positioning monitoring data; and obtaining a working face analysis result based on the number of working faces by using preset production rules for analysis and judgment;

[0078] Based on the production monitoring data, the personnel location data corresponding to each mining operation point is determined to obtain the number of personnel corresponding to each mining operation point; according to the number of personnel corresponding to each mining operation point, the preset production rules are used to analyze and judge to obtain the analysis results of the number of operators;

[0079] According to the work surface analysis results and the number of workers analysis results, the third analysis result is obtained.

[0080] In the present application, comparative analysis of power monitoring data and production monitoring data further includes: generating an initial power prediction model based on the power data prediction function;

[0081] The power data prediction function is expressed as:

[0082]

[0083] Among them, f(X norm ) is the predicted value of power data, n is the number of data, i represents the i-th data, and β i represents the parameter coefficient in the optimal regression function, is the kernel function of support vector regression, and the function value is a vector and X norm The inner product of

[0084] Generate a training set and a test set based on historical power monitoring data in the power monitoring data;

[0085] Preprocessing current power monitoring data and historical power monitoring data;

[0086] The power prediction model is trained and tested using the training set and the test set to obtain a tested power prediction model;

[0087] The tested power forecasting model is used to forecast the power data of the target coal enterprise to obtain the power forecast data.

[0088] S103: judging the overcapacity production behavior of the coal mine based on the analysis results;

[0089] In this application example, judging the overcapacity production behavior of a coal mine based on the analysis results includes:

[0090] If in the first analysis result, the current power monitoring data exceeds the historical power monitoring data and exceeds the first threshold;

[0091] Moreover, in the second analysis result, the current power monitoring data exceeds the power monitoring data of any other coal mining enterprise in the coal mine power monitoring data in the same region;

[0092] Moreover, in the third analysis result, in the production monitoring data, the number of operating faces exceeds the number of operating faces specified in the preset production rules, or the number of personnel corresponding to any mining operation point exceeds the limit number of personnel specified in the preset production rules;

[0093] It is determined that the coal mine has exceeded its capacity in production;

[0094] Otherwise, it is determined that the coal mine does not engage in overcapacity production.

[0095] In this application example, judging the overcapacity production behavior of the coal mine based on the analysis results also includes:

[0096] Compare power forecast data with current power monitoring data;

[0097] If the magnitude of the current power monitoring data is greater than the power forecast data by more than the preset magnitude threshold, it is determined that the coal mine has overcapacity production behavior;

[0098] Otherwise, it is determined that the coal mine does not engage in overcapacity production.

[0099] S104: Feedback the judgment result and display it visually.

[0100] According to the intelligent identification method for overproduction violations based on big data analysis provided by the embodiment of the present application, the power monitoring data and production monitoring data of the coal mine enterprise are obtained; the power monitoring data and production monitoring data are compared and analyzed to obtain analysis results; based on the analysis results, it is judged whether the coal mine has overcapacity production behavior; the judgment results are fed back and displayed to the staff. Through the intelligent identification method for overproduction violations based on big data analysis provided by the present embodiment, by obtaining the power monitoring data and production monitoring data of the coal mine enterprise, a year-on-year analysis is performed based on the current power monitoring data and historical power monitoring data, and a comparative analysis is performed with the power monitoring data of coal mines in the same region, and the production monitoring data is analyzed according to relevant production regulations to obtain analysis results, the working situation of the enterprise is judged by the power consumption of the enterprise, and based on this, several analysis results are combined to scientifically judge whether the target coal mine enterprise has overproduction behavior. The comprehensive analysis can make the judgment result more accurate, greatly reduce the possibility of misjudgment, help supervisors discover overproduction, and effectively improve the safety and supervision of production and operation in the coal industry. The timeliness of supervision reduces safety risks and, under the premise of fully ensuring production safety, protects the normal production rights and interests of various coal enterprises; among them, the personnel positioning monitoring data in the production monitoring data is used to determine the mining points and working faces in the coal mining enterprises, and the number of working faces and the number of people at each mining point are obtained based on this, and then compared and analyzed with the relevant regulations on the number of working faces and the number of mining people, it can more accurately determine whether the enterprise has overproduction, and further obtain relevant information on the specific working faces or working points of overproduction, so that supervisors can obtain more relevant information of the target coal mining enterprises, which will help them to locate and handle more quickly in the subsequent supervision procedures, further reducing safety risks in production. In addition, by setting up a power forecasting model for the target coal enterprise, training and testing it with historical power monitoring data, using the model to predict the power data, and comparing the predicted value with the monitoring data, it is determined whether there is overproduction behavior. This further improves the intelligence of the supervision system in the process of informatization construction of enterprise safety production supervision and inspection in the coal mining industry, helps to understand the production trends of the enterprises, and makes a more scientific judgment on whether there is overproduction behavior. Based on big data analysis and machine learning technology, the accuracy of such judgments has been greatly improved.

[0101] Example 2

[0102] Reference Figure 2 to Figure 11 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides an overproduction violation intelligent identification system based on big data analysis, including: a data acquisition module 501, a data analysis module 502, a judgment module 503 and a result feedback module 504;

[0103] Data acquisition module 501, used to acquire power monitoring data and production monitoring data of coal mining enterprises;

[0104] Preferably, the power monitoring data includes at least current power monitoring data, historical power monitoring data, and power monitoring data of coal mines in the same region;

[0105] The production monitoring data includes at least personnel location monitoring data and water hazard monitoring data.

[0106] The data analysis module 502 is used to compare and analyze the power monitoring data and the production monitoring data to obtain analysis results. In the embodiment of the present application, the comparison and analysis of the power monitoring data and the production monitoring data includes:

[0107] Performing year-on-year analysis based on current power monitoring data and historical power monitoring data to obtain a first analysis result;

[0108] Based on the comparative analysis of the current power monitoring data and the power monitoring data of coal mines in the same area, the second analysis result is obtained;

[0109] The production monitoring data is analyzed based on the preset production rules to obtain a third analysis result.

[0110] In the embodiments of the present application, Figure 2 The work surface and operator number analysis method based on production rules includes:

[0111] Step S201, determining the number of working faces based on the personnel positioning monitoring data in the production monitoring data, and obtaining working face analysis results based on the production rule analysis;

[0112] Specifically, based on the personnel positioning monitoring data in the production monitoring data, the personnel activity concentration area is obtained; based on the personnel activity concentration area, combined with the personnel substation name and personnel type information in the personnel positioning monitoring data, the mining operation point is determined, and the number of working faces is further determined;

[0113] Based on the number of working faces, the preset production rules are used for analysis and judgment to obtain the working face analysis results.

[0114] Preferably, based on the map information of the target coal enterprise and the personnel positioning monitoring data, it is possible to determine which work areas the operating personnel are specifically gathered in, that is, the activity concentration areas; then based on the personnel activity concentration areas, combined with the personnel substation name and personnel type information, the personnel and the corresponding number of personnel belonging to the specific substation and the corresponding type of work in the underground work in the area are determined, and the mining operation points and working faces are determined through cluster analysis, and the number of working faces is obtained;

[0115] The current number of working faces is determined according to the number of working faces specified in the production rules to obtain the working face analysis results.

[0116] Preferably, the production rules can be determined according to the Coal Mine Safety Regulations;

[0117] At the same time, according to the characteristics of coal mines in different regions, local special regulations, local laws, departmental regulations and other normative documents that are suitable for this can also be added. Users can make adjustments according to actual conditions, and the specific content of the production rules is not limited here.

[0118] Step S202, determining the mining operation points and the corresponding number of personnel based on the production monitoring data, and obtaining the analysis result of the number of operators based on the production rule analysis;

[0119] Specifically, the location data of the personnel at each mining operation point corresponding to each mining operation point is determined from the production detection data, and the number of personnel corresponding to each mining operation point is obtained;

[0120] According to the number of personnel corresponding to each mining operation point, the preset production rules are used for analysis and judgment to obtain the analysis results of the number of operators.

[0121] Preferably, based on the personnel positioning monitoring data and the location of each mining operation point, the number of operators corresponding to each mining operation point is calculated, and the current number of operators is determined according to the number of operators at the mining operation point specified in the production rules to obtain the operator number analysis result.

[0122] Preferably, after the mining operation point is determined, a correlation analysis can be performed based on the number of workers at the mining operation point and changes in power monitoring data in the area of ​​the operation point to further accurately determine the mining operation point of the coal mine.

[0123] Step S203, combining the work surface analysis result and the operator number analysis result to obtain the work surface and operator number analysis result;

[0124] By using the personnel positioning monitoring data in the production monitoring data, the mining points and working faces in the coal mining enterprise can be determined, and the number of working faces and the number of people at each mining point can be obtained based on this. By comparing and analyzing with the relevant regulations on the number of working faces and the number of mining people, it is possible to more accurately determine whether the enterprise has overproduction, and further obtain relevant information on the specific working faces or working points of overproduction, so that supervisors can obtain more relevant information on the target coal mining enterprises, which will help them to locate and handle more quickly in the subsequent supervision procedures, further reducing safety risks in production.

[0125] A judgment module 503 is used to judge the overcapacity production behavior of the coal mine based on the analysis result;

[0126] In the embodiments of the present application, Figure 3 The overproduction violation judgment methods shown include:

[0127] Step S301, obtaining a first analysis result, a second analysis result, and a third analysis result.

[0128] Step S302, judging whether the coal mining enterprise has overcapacity production behavior according to the first analysis result, the second analysis result and the third analysis result.

[0129] Specifically, if in the first analysis result, the current power monitoring data exceeds the historical power monitoring data and exceeds the first threshold;

[0130] Moreover, in the second analysis result, the current power monitoring data exceeds the power monitoring data of any other coal mining enterprise in the coal mine power monitoring data in the same region;

[0131] Moreover, in the third analysis result, in the production monitoring data, the number of operating faces exceeds the number of operating faces specified in the preset production rules, or the number of personnel corresponding to any mining operation point exceeds the limit number of personnel specified in the preset production rules;

[0132] Then execute step S303;

[0133] Otherwise, execute step S304.

[0134] Preferably, based on the current power monitoring data and the historical power monitoring data, through year-on-year analysis, the abnormal power consumption of the target coal enterprise is captured, and the comparison between the current power monitoring data and the historical power monitoring data is determined as the first analysis result; then based on the first analysis result, it is judged whether the proportion of the current power monitoring data exceeding the historical power monitoring data exceeds the first threshold; for example, the first threshold may be 30%, that is, whether the proportion of the current power monitoring data exceeding the historical power monitoring data exceeds 30%.

[0135] Preferably, based on the current power monitoring data and the power monitoring data of coal mines in the same region, through cluster analysis, the coal mining enterprises in the region are grouped from the spatial dimension and the enterprise similarity dimension, and the power monitoring data of the target coal mining enterprise is compared with that of other coal mining enterprises in the same group. According to the comparison result, a second analysis result is obtained, and thereby it is determined whether the current power monitoring data exceeds the power monitoring data of any other coal mining enterprise in the same group among the power monitoring data of coal mines in the same region.

[0136] Preferably, the number of personnel corresponding to the mining operation point exceeds the limit number specified in the preset production rules. It can also be stipulated that the number of personnel corresponding to the mining operation point exceeds the limit number specified in the preset production rules by more than 20%.

[0137] Step S303, determining whether the coal mine has overcapacity production behavior.

[0138] Step S304, determining whether the coal mine has any overcapacity production behavior.

[0139] According to the above method, based on the multiple analysis results obtained previously, a comprehensive judgment can be made to more reasonably determine the actual working situation of the coal mining enterprise and scientifically judge whether the target coal mining enterprise has overproduction behavior. Comprehensive analysis can make the judgment results more accurate and greatly reduce the possibility of misjudgment.

[0140] In the embodiments of the present application, Figure 4 As shown, the comparative analysis of power monitoring data and production monitoring data also includes:

[0141] Step S401, generating an initial power prediction model based on the power data prediction function;

[0142]

[0143] and β i represents the parameter coefficient in the optimal regression function, is the kernel function of support vector regression, and the function value is a vector and X norm The inner product of

[0144] Step S402, generating a training set and a test set based on the historical power monitoring data in the power monitoring data;

[0145] Specifically, through the normalization formula

[0146]

[0147] The current power monitoring data and historical power monitoring data are preprocessed; where μ is the data mean and σ is the standard deviation.

[0148] Step S403, preprocessing the current power monitoring data and the historical power monitoring data;

[0149] Step S404, training and testing the power prediction model using the training set and the test set to obtain a tested power prediction model;

[0150] Step S405, using the tested power forecasting model to forecast the power data of the target coal enterprise, to obtain power forecasting data.

[0151] Specifically, if yes, execute step S406; if no, execute step S407.

[0152] Preferably, the preset amplitude threshold may be set to 30%.

[0153] Step S406, determining whether the coal mine has overcapacity production behavior.

[0154] Step S407, determining whether the coal mine has any overcapacity production behavior.

[0155] According to the above method, by setting up a power forecasting model for the target coal enterprise, using historical power monitoring data to train and test it, using the model to predict the power data, and comparing the predicted value with the monitoring data, it is determined whether there is overproduction behavior. In the process of informatization construction of enterprise safety production supervision and inspection in the coal mining industry, the intelligence level of the supervision system is further improved, which helps to understand the production trends of the enterprises and make a more scientific judgment on whether there is overproduction behavior. Based on big data analysis and machine learning technology, the accuracy of such judgments is greatly improved.

[0156] The result feedback module 504 is used to provide feedback and visualize the judgment result.

[0157] For example, Figure 7 , Figure 8 As shown, Figure 7 A comparison chart of annual power consumption detection of a coal enterprise according to an embodiment of the present invention is shown. Figure 8 A comparison chart of monthly electricity consumption detection of a coal enterprise according to an embodiment of the present invention is shown;

[0158] like Fig. 9 As shown, Fig. 9 A schematic diagram of the normal power consumption range of a coal enterprise according to an embodiment of the present invention is shown; accordingly, a year-on-year analysis is performed based on the historical data of annual and monthly power consumption of the coal enterprise.

[0159] like Fig.10 As shown, Fig.10 A comparison chart of power monitoring data of coal enterprises in the same region according to an embodiment of the present invention is shown. Fig.11 As shown, Fig.11 A schematic diagram of determining overcapacity production of a coal enterprise according to an embodiment of the present invention is shown.

[0160] An intelligent identification system for overproduction violations based on big data analysis provided by this embodiment obtains power monitoring data and production monitoring data of coal mine enterprises, conducts year-on-year analysis based on current power monitoring data and historical power monitoring data, conducts comparative analysis with power monitoring data of coal mines in the same region, and analyzes production monitoring data according to relevant production regulations to obtain analysis results, judges the working situation of the enterprise based on its electricity consumption, and scientifically judges whether the target coal mine enterprise has overproduction behavior by combining several analysis results. Comprehensive analysis can make the judgment result more accurate, greatly reduce the possibility of misjudgment, help supervisors discover overproduction, effectively improve the safety of production and operation in the coal industry and the timeliness of supervision, reduce safety risks, and protect the normal production rights and interests of various coal enterprises on the premise of fully ensuring production safety.

[0161] The present invention also provides a non-volatile computer storage medium, which stores at least one executable instruction, and the executable instruction can execute the operations that can be achieved by the overproduction violation intelligent identification system based on big data analysis in any of the above system embodiments.

[0162] Figure 6 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0163] like Figure 6 As shown, the computing device may include: a processor (processor) 602 , a communications interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0164] in:

[0165] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0166] The communication interface 604 is used to communicate with other devices such as clients or other servers.

[0167] The processor 602 is used to execute the program 610, and specifically can execute the operations that can be achieved by the overproduction violation intelligent identification system based on big data analysis in the above-mentioned embodiment of the overproduction violation intelligent identification system based on big data analysis.

[0168] Specifically, the program 610 may include program codes, and the program codes include computer operation instructions.

[0169] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0170] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0171] Program 610 can be specifically used to enable processor 602 to perform operations that can be implemented by the overproduction violation intelligent identification system based on big data analysis in any of the above-mentioned system embodiments. The specific implementation of each step in program 610 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned overproduction violation intelligent identification system based on big data analysis, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the aforementioned method embodiment, which will not be repeated here.

[0172] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0173] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0174] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all of the features of the individual embodiments previously disclosed. Therefore, the claims that follow the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present invention.

[0175] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0176] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0177] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0178] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent identification method for overproduction violations based on big data analysis, characterized in that: include: Obtain power monitoring data and production monitoring data of coal mining enterprises; Comparatively analyzing the power monitoring data and the production monitoring data to obtain analysis results; Determine the overcapacity production behavior of the coal mine based on the analysis results; The judgment result is fed back and visualized.

2. The method for intelligently identifying overproduction violations based on big data analysis according to claim 1, characterized in that: The power monitoring data at least includes current power monitoring data, historical power monitoring data and power monitoring data of coal mines in the same region; The production monitoring data at least includes personnel location monitoring data and water hazard monitoring data.

3. The method for intelligently identifying overproduction violations based on big data analysis according to claim 2, characterized in that: The comparative analysis of the power monitoring data and the production monitoring data includes: Performing a year-on-year analysis based on the current power monitoring data and the historical power monitoring data to obtain a first analysis result; Based on the comparison and analysis between the current power monitoring data and the power monitoring data of the coal mine in the same area, a second analysis result is obtained; The production monitoring data is analyzed based on the preset production rules to obtain a third analysis result.

4. The method for intelligently identifying overproduction violations based on big data analysis according to claim 3, characterized in that: Also includes: Based on the personnel positioning monitoring data, the personnel activity concentration area is obtained. Based on the personnel activity concentration area, combined with the personnel substation name and personnel type information in the personnel positioning monitoring data, the mining operation point and the number of working faces are determined; based on the number of working faces, the preset production rules are used for analysis and judgment to obtain the working face analysis results; Based on the production monitoring data, the personnel location data corresponding to each mining operation point is determined to obtain the number of personnel corresponding to each mining operation point; according to the number of personnel corresponding to each mining operation point, the preset production rules are used to perform analysis and judgment to obtain the number of personnel analysis results; According to the work surface analysis results and the number of workers analysis results, the third analysis result is obtained.

5. The method for intelligently identifying overproduction violations based on big data analysis according to claim 4, characterized in that: Based on the analysis results, the overcapacity production behaviors of coal mines include: If in the first analysis result, the current power monitoring data exceeds the historical power monitoring data and exceeds the first threshold; Moreover, in the second analysis result, the current power monitoring data exceeds the power monitoring data of any other coal mining enterprise in the coal mine power monitoring data in the same region; Moreover, in the third analysis result, in the production monitoring data, the number of operating faces exceeds the number of operating faces specified in the preset production rules, or the number of personnel corresponding to any mining operation point exceeds the limit number of personnel specified in the preset production rules; It is determined that the coal mine has exceeded its capacity in production; Otherwise, it is determined that the coal mine does not engage in overcapacity production.

6. The method for intelligently identifying overproduction violations based on big data analysis according to claim 3 or 5, characterized in that: Comparative analysis of the power monitoring data and production monitoring data further includes: generating an initial power prediction model based on the power data prediction function; The power data prediction function is expressed as: Among them, f(X norm ) is the predicted value of power data, n is the number of data, i represents the i-th data, and β i represents the parameter coefficient in the optimal regression function, k(X normi , X norm ) is the kernel function of support vector regression, and the function value is vector X normi and X norm The inner product of Generate a training set and a test set based on historical power monitoring data in the power monitoring data; Preprocessing current power monitoring data and historical power monitoring data; The power prediction model is trained and tested using the training set and the test set to obtain a tested power prediction model; The tested power forecasting model is used to forecast the power data of the target coal enterprise to obtain the power forecast data.

7. The method for intelligently identifying overproduction violations based on big data analysis according to claim 6, characterized in that: Based on the analysis results, the overcapacity production behavior of coal mines also includes: Compare power forecast data with current power monitoring data; If the magnitude of the current power monitoring data is greater than the power forecast data by more than the preset magnitude threshold, it is determined that the coal mine has overcapacity production behavior; Otherwise, it is determined that the coal mine does not engage in overcapacity production.

8. An intelligent identification system for overproduction violations based on big data analysis, characterized in that: include: Data acquisition module, used to obtain power monitoring data and production monitoring data of coal mining enterprises; A data analysis module, used to compare and analyze the power monitoring data and the production monitoring data to obtain analysis results; A judgment module, used for judging the overcapacity production behavior of the coal mine based on the analysis result; The result feedback module is used to provide feedback and visually display the judgment result.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for intelligently identifying overproduction violations based on big data analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for intelligently identifying overproduction violations based on big data analysis as described in any one of claims 1 to 7.

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