CN114819773A - Enterprise green production management and control method and system based on big data - Google Patents
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Abstract
The application discloses an enterprise green production management and control method and system based on big data, which belong to the field of data processing, and the method comprises the following steps: the method comprises the steps of obtaining an enterprise production chain of a target enterprise by accessing an enterprise information management terminal, further obtaining a plurality of production chain nodes and analyzing correlation, obtaining a plurality of correlation coefficients, analyzing pollution indexes by carrying out on each production node in the plurality of production chain nodes, outputting a plurality of pollution indexes, taking the plurality of correlation coefficients as input variables, taking the plurality of pollution indexes as input quantitives, building a response target function, outputting a plurality of iterative pollution indexes, positioning pollution control nodes in the enterprise production chain according to the iterative pollution indexes, and carrying out pollution amplitude reduction control. The technical problems that the production process of an enterprise cannot be accurately analyzed, accurate pollution indexes are obtained, and pollution control quality is low are solved. The technical effect of accurately controlling the global pollution index by adjusting the local production chain is achieved.
Description
Technical Field
The application relates to the field of data processing, in particular to an enterprise green production management and control method and system based on big data.
Background
The green production of enterprises is a comprehensive measure for controlling the pollution in the whole process of industrial production through a sound management system and minimizing the generation amount of pollutants. The green production aims at saving energy, reducing consumption and pollution, and the green production of research enterprises is beneficial to promoting the conversion of the industrial pollution control mode of China from pollution first and then treatment to pollution prevention, thereby realizing the aim of economic sustainable development.
At present, the production process of each link in the production process of a product is mainly evaluated by production management personnel for green production management of an enterprise, and pollution generated in the manufacturing process is determined, so that pollution projects are controlled, pollution indexes of each link reach inspection standards, and the whole production reaches the environmental protection standard.
However, each production link is not isolated in the production process of an enterprise, an upstream production link can affect a downstream production link, only the pollution control of the link is considered, the pollution effect of the upstream production link on the link is neglected, pollution errors can be accumulated continuously, the pollution control of each link has the pollution effect of the upstream link, the errors are accumulated continuously, and finally the final finished product cannot reach the environmental protection standard. The method has the technical problems that the production process of an enterprise cannot be accurately analyzed, accurate pollution indexes are obtained, and the pollution control quality is low.
Disclosure of Invention
The application aims to provide an enterprise green production management and control method and system based on big data, and the method and system are used for solving the technical problems that in the prior art, the production process of an enterprise cannot be accurately analyzed, accurate pollution indexes are obtained, and the pollution management and control quality is low.
In view of the above problems, the present application provides a method and a system for enterprise green production management and control based on big data.
In a first aspect, the application provides a big data-based enterprise green production management and control method, where the method is applied to an enterprise green production management and control system, the system is in communication connection with an enterprise information management terminal, and the method includes: accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise; acquiring a plurality of production chain nodes by performing attribute identification on each production node in the enterprise production chain; obtaining a plurality of correlation coefficients according to the correlation among the plurality of production chain nodes, wherein the correlation coefficients are the production correlation degrees among adjacent nodes; outputting a plurality of pollution indexes by analyzing the pollution indexes of each production node in the plurality of production chain nodes, wherein the plurality of pollution indexes correspond to the plurality of production chain nodes one to one; quantifying the plurality of pollution indexes as input based on the plurality of correlation coefficients as input variables, and constructing a response objective function, wherein the response objective function is used for iteratively adjusting the plurality of pollution indexes corresponding to the plurality of production chain link points; outputting a plurality of iterative pollution indexes according to the response objective function; and positioning a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes for pollution amplitude reduction control.
On the other hand, this application still provides an enterprise green production management and control system based on big data, wherein, the system includes: the production chain acquisition module is used for accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise; the node acquisition module is used for acquiring a plurality of production chain nodes by carrying out attribute identification on each production node in the enterprise production chain; a correlation coefficient obtaining module, configured to obtain a plurality of correlation coefficients according to correlations between the plurality of production chain nodes, where the plurality of correlation coefficients are degrees of production correlation between adjacent nodes; an index output module configured to output a plurality of pollution indexes by performing pollution index analysis on each of the plurality of production chain nodes, wherein the plurality of pollution indexes correspond to the plurality of production chain nodes one to one; the function building module is used for quantifying the plurality of pollution indexes as input based on the plurality of correlation coefficients as input variables and building a response objective function, wherein the response objective function is used for iteratively adjusting the plurality of pollution indexes corresponding to the plurality of production chain link points; the iteration index output module is used for outputting a plurality of iteration pollution indexes according to the response objective function; and the node positioning module is used for positioning a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes and performing pollution amplitude reduction control.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
this application is through from enterprise information management terminal, obtain the enterprise production chain of target enterprise, and then the effect of each production node is analyzed and attribute identification is carried out, thereby obtain a plurality of production chain nodes, then through the correlation between two liang of adjacent production chain nodes of analysis, obtain a plurality of correlation coefficient, and then each production node to in a plurality of production chain nodes pollutes the index analysis, and use a plurality of correlation coefficient as input variable, a plurality of pollution indexes are the input ration, come to build a plurality of pollution indexes that are used for a plurality of production chain point correspondences and carry out the response objective function of iterative adjustment, export a plurality of iterative pollution indexes through responding the objective function, thereby the pollution management and control node in the positioning enterprise production chain, pollute and reduce amplitude management and control. The method realizes the purpose of carrying out fine analysis on the production chain of an enterprise and carrying out index adjustment in a first-level mode, and achieves the technical effects of improving the accuracy of pollution control and improving the quality of pollution control.
Drawings
In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
Fig. 1 is a schematic flow chart of an enterprise green production control method based on big data according to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating a process of outputting a plurality of pollution indexes in the enterprise green production control method based on big data according to the embodiment of the present application;
fig. 3 is a schematic flow chart illustrating a process of positioning a pollution control node in an enterprise production chain in a big data based enterprise green production control method according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an enterprise green production management and control system based on big data according to the present application;
description of reference numerals: the system comprises a production chain acquisition module 11, a node acquisition module 12, a correlation coefficient acquisition module 13, an index output module 14, a function building module 15, an iteration index output module 16 and a node positioning module 17.
Detailed Description
The application provides an enterprise green production management and control method and system based on big data, and solves the technical problems that in the prior art, the production process of an enterprise cannot be accurately analyzed, accurate pollution indexes are obtained, and the pollution management and control quality is low. The technical effects of improving the accuracy of pollution control and improving the quality of pollution control are achieved.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
In the following, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.
Example one
As shown in fig. 1, the present application provides a big data-based enterprise green production management and control method, where the method is applied to an enterprise green production management and control system, the system is in communication connection with an enterprise information management terminal, and the method includes:
step S100: accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise;
specifically, the enterprise information management terminal is a terminal that manages information in the entire production cycle of an enterprise, and can store, call, and process enterprise information. The target enterprise refers to any enterprise which needs to be subjected to green production management and control. The enterprise production chain refers to a flow and a participating main body in the process from item establishment to finished product processing of a certain product in an enterprise, and is mainly embodied as a production activity set which has different processing purposes on the product in the production process and interacts with each other. In the production process of the automobile, the automobile production chain includes automobile design research and development, automobile raw material purchase, automobile production and manufacture and the like. Through obtaining the production chain of enterprise, can realize mastering the target of the production process of enterprise to reach and know the production condition of enterprise in detail, do the technological effect of laying down for follow-up further analysis pollution index based on the production chain condition of enterprise.
Step S200: acquiring a plurality of production chain nodes by performing attribute identification on each production node in the enterprise production chain;
step S300: obtaining a plurality of correlation coefficients according to the correlation among the plurality of production chain nodes, wherein the correlation coefficients are the production correlation degrees among adjacent nodes;
specifically, the production node refers to a process for performing a production process transition in a production process. The attribute identification means that the processing purpose of each production node for the product is obtained by analyzing the technological process of each production node, and therefore each production node is identified as the attribute according to the difference of the processing purpose. And the production chain node is the production node subjected to attribute identification. And judging whether the previous production chain node process influences the manufacture of the next production chain node or not by analyzing the mutual influence degree among a plurality of production chain nodes, thereby obtaining a plurality of correlation coefficients. Wherein the correlation analysis is for two adjacent production chain nodes, and thus the difference between the number of the plurality of production chain nodes and the number of the plurality of correlation coefficients is 1. Therefore, the purpose of analyzing the association degree between the production chain nodes in the production chain can be achieved, the analysis basis is provided for the gain influence degree of the subsequent pollution influence, and the technical effect of analyzing the accuracy of the pollution indexes in the whole production chain is improved.
Step S400: outputting a plurality of pollution indexes by analyzing the pollution indexes of each production node in the plurality of production chain nodes, wherein the plurality of pollution indexes correspond to the plurality of production chain nodes one to one;
further, as shown in fig. 2, in the step S400 of outputting a plurality of pollution indicators, the method further includes:
step S410: building a pollution index calculation model, wherein the pollution index calculation model comprises three-source pollution indexes, and the three-source pollution indexes comprise a wastewater pollution source, an exhaust gas pollution source and a waste residue pollution source;
step S420: performing three-source data acquisition and storage on each production node in the plurality of chain nodes through a data acquisition device, and generating a three-source data call library for calculating pollution indexes;
step S430: inputting the three-source data calling library into the pollution index calculation model to obtain a wastewater pollution index, a waste gas pollution index and a waste residue pollution index;
step S440: and comprehensively calculating the wastewater pollution index, the waste gas pollution index and the waste residue pollution index, outputting node pollution indexes, and obtaining a plurality of pollution indexes corresponding to the production nodes by analogy.
Specifically, pollution indexes are analyzed for each production node, pollution influences caused by process treatment on products in the production process are analyzed, and therefore a plurality of pollution indexes are obtained. Wherein the plurality of pollution indicators comprises: a wastewater pollution source, a waste gas pollution source, and a waste residue pollution source. The pollution index calculation model is a functional model used for analyzing and calculating pollution data in each production node to obtain a pollution index. The three-source pollution index is a type of the pollution index calculation model capable of analyzing and processing data, and mainly comprises the following steps: a wastewater pollution source, a waste gas pollution source, and a waste residue pollution source. The data acquisition device is used for carrying out the data acquisition of waste water pollution source, waste gas pollution source and waste residue pollution source to each production node in the production process, optionally, the data acquisition device includes: cameras, infrared cameras, water quality detectors, gas analyzers, and the like. The three-source database is obtained by acquiring three-source data in the production process through a data acquisition device and is mainly used for input data calling when index calculation is carried out on a calculation model.
Specifically, the three-source data generated by calling the node from the three-source data calling library is input into the pollution index calculation model, and the pollution condition of the node is obtained through calculation and analysis of the model, so that the wastewater pollution index, the waste gas pollution index and the waste residue pollution index are obtained. Wherein the wastewater pollution index is a pollution index for evaluating the pollution degree of the wastewater generated in the production process to the environment under the influence of the production and manufacturing process of the node. The waste gas pollution index is a pollution index for evaluating the pollution degree of waste gas generated in the production process to the environment under the influence of the production and manufacturing process of the node. The waste residue pollution index is an index for evaluating the pollution degree of waste residue generated in the production process to the environment under the influence of the production and manufacturing process of the node.
Specifically, the pollution degree of the node to the environment after the pollution sources are comprehensively considered, namely the node pollution index, is obtained by comprehensively calculating the wastewater pollution index, the exhaust gas pollution index and the waste residue pollution index of each node according to a certain calculation method. Preferably, the calculation method may be assigning according to a weight. Therefore, the method achieves the aim of grasping the pollution condition of each node to the environment, and achieves the technical effect of providing basic data for index adjustment by considering the pollution influence among the nodes subsequently.
Further, comprehensively calculating the pollution index of the wastewater, the pollution index of the exhaust gas and the pollution index of the waste residue, wherein the step S440 of the embodiment of the present application further includes:
step S441: acquiring enterprise address selection information of the target enterprise;
step S442: according to the enterprise address selection information, carrying out peripheral radius area division by taking the target enterprise as a center to obtain enterprise peripheral facility attributes;
step S443: performing information entropy calculation on the sub-pollution factors in the pollution indexes according to the attributes of the peripheral facilities of the enterprise, and outputting a weight division ratio;
step S444: and configuring the weight ratio of the wastewater pollution index, the waste gas pollution index and the waste residue pollution index according to the weight ratio.
Further, in order to obtain a plurality of pollution indexes corresponding to each production node, step S440 in the embodiment of the present application further includes:
step S445: acquiring information of a three-source processing flow of the target enterprise;
step S446: performing cross contamination probability calculation according to the information of the three-source processing flow, and outputting the cross contamination probability;
step S447: judging whether an index adjusting instruction is activated or not according to the cross contamination probability;
step S448: and carrying out index adjustment on the plurality of pollution indexes according to the index adjustment instruction.
Specifically, the enterprise address information refers to geographic location information of the target enterprise. And taking the target enterprise as a center, and dividing an analysis area according to a certain radius, wherein the radius is determined by an area range which can be influenced by pollution of the enterprise, and specific numerical values are set by a worker according to specific conditions. The enterprise peripheral facility attribute refers to a peripheral industrial environment condition in an analysis area range, and optionally includes: industrial parks, residential areas, commercial areas, etc. Different attributes of the peripheral facilities have different standards for environmental pollution control, and the influence of sub-pollution factors in the pollution indexes on the peripheral facilities is analyzed by obtaining the conditions of the peripheral facilities, so that the uncertainty condition of pollution influence, namely the information entropy is obtained. The proportion of the weights is obtained according to the size of the information entropy, the uncertainty of the information entropy is large, the proportion is small in analysis, the certainty of the information entropy is small, and the proportion is large in analysis. Wherein the sub-pollution factors are next-level pollution factors in pollution indexes, namely the wastewater pollution, the waste gas pollution and the waste residue pollution. The weight fraction is the proportion of the sub-contamination factors in the contamination analysis. And further, carrying out weight ratio configuration on the wastewater pollution index, the waste gas pollution index and the waste residue pollution index according to a weight ratio, so that the environmental pollution condition of each production node can be obtained. Therefore, the accuracy of analyzing the pollution condition of each node is improved, the analysis accuracy of each link is improved, and the technical effect of integrally improving the accuracy of production management and control is achieved.
Specifically, whether cross contamination occurs among the three sources in the process of processing the three sources is obtained by obtaining information of the three-source processing flow of the target enterprise, so that final pollution evaluation is influenced. The three-source treatment process is a treatment step of wastewater, waste gas and waste residue in the production process, and comprises a treatment process, a treatment position, a recovery method and the like. By acquiring the information, it is determined whether new pollution is generated among the three sources in the process of processing the three sources, for example, secondary pollution generated after mixing of wastewater and waste gas, and the cross-contamination probability representing the possibility of generating new pollution among the three sources is obtained. And setting a preset probability value according to the size of the cross contamination probability, and activating the index adjusting instruction to perform optimization adjustment on the plurality of pollution source indexes if the cross contamination probability exceeds the preset probability value. The index adjusting instruction is a command for instructing a production management and control system to perform pollution index adjusting operation. Therefore, the technical effect of considering the mutual influence among the three sources and improving the comprehensiveness of pollution analysis is achieved.
Further, outputting a plurality of pollution indicators, in step S400 of the embodiment of the present application, further includes:
step S450: performing noise test on the target enterprise to obtain noise test sample data, wherein the noise test sample data comprises internal environment test data and external environment test data;
step S460: determining the noise prevention performance of the target enterprise based on the noise reduction amplitude between the internal environment test data and the external environment test data;
step S470: when the noise prevention performance is less than or equal to the preset noise prevention performance, acquiring a new instruction;
step S480: and adding the noise index serving as a newly increased index into each node according to the newly increased instruction to perform pollution index optimization calculation.
Specifically, the noise test is a test for testing the quality of the sound insulation facility of the target enterprise, and the noise prevention performance is determined by verifying the noise reduction amplitude between the internal environment test data and the external environment test data of the target enterprise. Wherein the noise test comprises: and a common sound level meter is adopted for field detection and ISO near field detection. The noise test sample data is data generated in a noise test process, including: sound pressure in a sound field, frequency of sound pressure, etc. The internal environment test data is data generated by noise test inside an enterprise, and comprises the following steps: internal sound pressure, internal frequency. The external environment test data is data generated by noise test outside an enterprise, and comprises external sound pressure and external frequency. The noise reduction amplitude is a difference value between the internal environment test data and the external environment test data, and can reflect the noise prevention performance of an enterprise. The noise preventing performance is a performance of reducing noise emission. The predicted noise prevention performance is a preset noise prevention standard performance, and when the noise prevention performance is smaller than or equal to the preset noise prevention performance, it indicates that the noise insulation measures of enterprises are not enough, and noise conditions need to be improved. The new instruction is used for issuing a new noise index to the production management and control system to serve as an operation command of one of the pollution indexes. From this, reached and considered the noise influence, improved the comprehensive technological effect of production management and control.
Step S500: quantifying the plurality of pollution indexes as input based on the plurality of correlation coefficients as input variables, and constructing a response objective function, wherein the response objective function is used for iteratively adjusting the plurality of pollution indexes corresponding to the plurality of production chain link points;
specifically, the pollution indexes corresponding to each node are the same, but the correlation coefficients between adjacent nodes are different, so that the plurality of pollution indexes are used as input variables and are quantified, and a response objective function reflecting the gain condition of the pollution indexes of the downstream nodes caused by the pollution influence generated by the upstream node can be constructed. The response objective function is a function used for iteratively adjusting the pollution index of the node according to the influence condition of the previous node on the node. Therefore, the pollution indexes of the node are used as basic data of iterative adjustment, and the correlation coefficient is used as an adjustment basis, so that the technical effects of adjusting the pollution indexes of each level of the node and finally improving the accuracy of the whole pollution indexes are achieved.
Step S600: outputting a plurality of iterative pollution indexes according to the response objective function;
step S700: and positioning a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes for pollution amplitude reduction control.
Further, as shown in fig. 3, a pollution control node in the enterprise production chain is located according to the multiple iterative pollution indicators, and step S700 in the embodiment of the present application further includes:
step S710: inputting the plurality of pollution indexes and the plurality of iterative pollution indexes into an index comparison unit for comparison to obtain iterative index difference degrees;
step S720: classifying the plurality of production chain nodes according to the iteration index difference to obtain nodes to be controlled, wherein the nodes to be controlled are front nodes of nodes corresponding to the iteration index difference which is greater than or equal to the preset iteration index difference;
step S730: and taking the node to be controlled as the pollution control node to perform pollution index reduction control.
Further, using the node to be controlled as the pollution control node to perform pollution index reduction control, step S730 in the embodiment of the present application further includes:
step S731: acquiring a real-time pollution control index of the node to be controlled;
step S732: calculating a difference value between the iteration index difference degree of the node to be controlled and the preset iteration index difference degree on the basis of the iteration index difference degree of the node to be controlled, and outputting a difference value interval for reducing the pollution monitoring standard;
step S733: and acquiring a control parameter for performing amplitude reduction control by taking the difference interval as an adaptive target and the real-time pollution control index as an output variable.
Specifically, the multiple iterative pollution indexes are obtained by iteratively adjusting multiple production chain nodes and gradually considering the pollution influence of a previous node on a next node. And then, analyzing the plurality of iterative pollution indexes to obtain nodes needing pollution control, and controlling the key nodes to achieve the aim of reducing the overall pollution influence amplitude.
Specifically, the index comparison unit is a unit configured to compare a plurality of pollution indexes with a plurality of iterative pollution indexes to obtain a difference condition, and the iterative index difference degree is an index representing a degree of deviation between a pollution index of the node itself and an iterative pollution index after iterative adjustment. Optionally, the iterative index difference may be obtained by calculating a difference between the pollution index and the iterative pollution index. Preferably, a preset iteration index difference is set, the preset iteration index difference is an influence degree threshold value of a previous node on a next node, the preset iteration index difference is lower than the preset iteration index difference, the pollution index of the previous node on the next node is not greatly influenced, and the pollution index of the previous node on the next node is too greatly influenced to influence the calculation accuracy of the pollution index. Therefore, the node needing pollution control, namely the node to be controlled, is obtained.
Specifically, the real-time pollution control index is an index that the node to be controlled needs to be monitored in real time in the production process, and the requirement of environmental protection is guaranteed to be met, and optionally, the real-time pollution control index includes: wastewater monitoring index: ammonia nitrogen content, total phosphorus content, COD, water turbidity and transparency; exhaust gas monitoring index: sulfur dioxide content, nitrogen oxides, smoke dust, volatile organic content, and the like; waste residue monitoring index: harmful substance content, heavy metal content, etc. The difference interval is an index range within which the distance of the current node after being influenced by the previous node meets the pollution monitoring standard and needs to be improved. And then, taking the difference interval as an adaptive target, namely a pollution index reduction range which needs to be reached after adjustment, and taking the real-time pollution control index as an output variable, namely a variable which represents the effect reached by the control operation in real time after the pollution control adjustment, so as to further obtain a control parameter to be subjected to reduction control, namely an object to be subjected to the control operation. Therefore, the target of accurately judging the influence degree of the previous node on the next node is realized, and the technical effects of determining the control parameters and improving the control accuracy are achieved.
In summary, the enterprise green production control method based on big data provided by the application has the following technical effects:
1. the method comprises the steps of obtaining an enterprise production chain of a target enterprise, obtaining a plurality of production chain nodes by carrying out attribute identification on each production node in the enterprise production chain, obtaining a plurality of correlation coefficients according to the correlation among the production chain nodes, analyzing pollution indexes of each production node in the production chain nodes, outputting a plurality of pollution indexes, taking the pollution indexes as input quantification based on the correlation coefficients as input variables, building a response objective function, and carrying out iterative adjustment on the pollution indexes corresponding to the production chain nodes; and outputting a plurality of iterative pollution indexes according to the response objective function, and finally positioning a pollution control node in the enterprise production chain according to the iterative pollution indexes for pollution amplitude reduction control. The technical effect of accurately controlling the global pollution index by adjusting the local production chain is achieved.
2. This application is through setting up pollution index calculation model, then right each production node in a plurality of chain nodes carries out three source data acquisition and storage, and it is used for carrying out the pollution index calculation to generate three source data and transfer the storehouse and be used for, will three source data transfer the storehouse input among the pollution index calculation model, it is right waste water pollution index waste gas pollution index with the waste residue pollution index carries out the comprehensive calculation, and output node pollution index analogizes with this, acquires a plurality of pollution indexes that each production node corresponds. The method realizes the aim of grasping the pollution condition of each node to the environment, and achieves the technical effect of providing basic data for subsequently considering the pollution influence among the nodes and carrying out index adjustment.
Example two
Based on the same inventive concept as the enterprise green production control method based on the big data in the foregoing embodiment, as shown in fig. 4, the present application further provides an enterprise green production control system based on the big data, wherein the system includes:
the production chain acquisition module 11 is used for accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise;
a node obtaining module 12, where the node obtaining module 12 is configured to obtain a plurality of production chain nodes by performing attribute identification on each production node in the enterprise production chain;
a correlation coefficient obtaining module 13, where the correlation coefficient obtaining module 13 is configured to obtain a plurality of correlation coefficients according to correlations between the plurality of production chain nodes, where the plurality of correlation coefficients are degrees of production correlation between adjacent nodes;
an index output module 14, where the index output module 14 is configured to output a plurality of pollution indexes by performing pollution index analysis on each of the plurality of production chain nodes, where the plurality of pollution indexes correspond to the plurality of production chain nodes one to one;
a function building module 15, where the function building module 15 is configured to take the plurality of pollution indexes as input quantities based on the plurality of correlation coefficients as input variables, and build a response objective function, where the response objective function is configured to iteratively adjust the plurality of pollution indexes corresponding to the plurality of production chain link points;
an iteration index output module 16, where the iteration index output module 16 is configured to output a plurality of iteration pollution indexes according to the response objective function;
and the node positioning module 17 is configured to position a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes, and is configured to perform pollution amplitude reduction control.
Further, the system further comprises:
the pollution index calculation model comprises three source pollution indexes, wherein the three source pollution indexes comprise a wastewater pollution source, a waste gas pollution source and a waste residue pollution source;
the data call library generating unit is used for performing three-source data acquisition and storage on each production node in the plurality of chain nodes through a data acquisition device, and generating a three-source data call library for calculating the pollution indexes;
the index acquisition unit is used for inputting the three-source data calling library into the pollution index calculation model to acquire a wastewater pollution index, a waste gas pollution index and a waste residue pollution index;
and the index calculation unit is used for comprehensively calculating the wastewater pollution index, the waste gas pollution index and the waste residue pollution index, outputting node pollution indexes, and obtaining a plurality of pollution indexes corresponding to the production nodes by analogy.
Further, the system further comprises:
the system comprises an address selection information acquisition unit, a data processing unit and a data processing unit, wherein the address selection information acquisition unit is used for acquiring enterprise address selection information of the target enterprise;
the attribute acquisition unit is used for dividing peripheral radius areas by taking the target enterprise as a center according to the enterprise address selection information to acquire enterprise peripheral facility attributes;
the information entropy calculation unit is used for performing information entropy calculation on the sub-pollution factors in the pollution indexes according to the attributes of the peripheral facilities of the enterprise and outputting a weight division ratio;
and the weight distribution unit is used for carrying out weight ratio configuration on the wastewater pollution index, the waste gas pollution index and the waste residue pollution index according to the weight distribution ratio.
The noise test unit is used for obtaining noise test sample data by performing noise test on the target enterprise, wherein the noise test sample data comprises internal environment test data and external environment test data;
a noise prevention performance determination unit for determining noise prevention performance of the target enterprise based on a noise reduction between the internal environment test data and the external environment test data;
the instruction acquisition unit is used for acquiring a new instruction when the noise-proof performance is less than or equal to the preset noise-proof performance;
and the optimization calculation unit is used for adding the noise index serving as the newly increased index into each node according to the newly increased instruction to perform pollution index optimization calculation.
Further, the system further comprises:
the comparison unit is used for inputting the plurality of pollution indexes and the plurality of iterative pollution indexes into the index comparison unit for comparison to obtain iterative index difference degrees;
the node obtaining unit is used for classifying the plurality of production chain nodes according to the iteration index difference degree to obtain nodes to be controlled, wherein the nodes to be controlled are front nodes of nodes corresponding to the iteration index difference degree which is more than or equal to a preset iteration index difference degree;
and the amplitude reduction control unit is used for performing pollution index amplitude reduction control by taking the node to be controlled as the pollution control node.
Further, the system further comprises:
the real-time index acquisition unit is used for acquiring a real-time pollution control index of the node to be controlled;
the difference value calculation unit is used for performing difference value calculation with the preset iteration index difference degree on the basis of the iteration index difference degree of the node to be controlled and outputting a difference value interval for reducing the pollution monitoring standard;
and the control parameter acquisition unit is used for acquiring control parameters for performing amplitude reduction control by taking the difference interval as an adaptive target and the real-time pollution control index as an output variable.
Further, the system further comprises:
the flow information acquisition unit is used for acquiring the information of the three-source processing flow of the target enterprise;
the probability calculation unit is used for calculating the cross contamination probability according to the information of the three-source processing flow and outputting the cross contamination probability;
the judging unit is used for judging whether to activate an index adjusting instruction or not according to the cross contamination probability;
and the index adjusting unit is used for carrying out index adjustment on the plurality of pollution indexes according to the index adjusting instruction.
In the present description, each embodiment is described in a progressive manner, and the focus of the description of each embodiment is different from that of other embodiments, the aforementioned enterprise green production control method based on big data in the first embodiment of fig. 1 and the specific example are also applicable to the enterprise green production control system based on big data in this embodiment, and through the foregoing detailed description of the enterprise green production control method based on big data, those skilled in the art can clearly know the enterprise green production control system based on big data in this embodiment, so for the brevity of the description, detailed description is not repeated here. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The enterprise green production control method based on the big data is applied to an enterprise green production control system, the system is in communication connection with an enterprise information management terminal, and the method comprises the following steps:
accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise;
acquiring a plurality of production chain nodes by performing attribute identification on each production node in the enterprise production chain;
obtaining a plurality of correlation coefficients according to the correlation among the plurality of production chain nodes, wherein the correlation coefficients are the production correlation degrees among adjacent nodes;
outputting a plurality of pollution indexes by analyzing the pollution indexes of each production node in the plurality of production chain nodes, wherein the plurality of pollution indexes correspond to the plurality of production chain nodes one to one;
quantifying the plurality of pollution indexes as input based on the plurality of correlation coefficients as input variables, and constructing a response objective function, wherein the response objective function is used for iteratively adjusting the plurality of pollution indexes corresponding to the plurality of production chain link points;
outputting a plurality of iterative pollution indexes according to the response objective function;
and positioning a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes for pollution amplitude reduction control.
2. The method of claim 1, wherein the method further comprises:
building a pollution index calculation model, wherein the pollution index calculation model comprises three-source pollution indexes, and the three-source pollution indexes comprise a wastewater pollution source, an exhaust gas pollution source and a waste residue pollution source;
performing three-source data acquisition and storage on each production node in the plurality of chain nodes through a data acquisition device, and generating a three-source data call library for calculating pollution indexes;
inputting the three-source data calling library into the pollution index calculation model to obtain a wastewater pollution index, a waste gas pollution index and a waste residue pollution index;
and comprehensively calculating the wastewater pollution index, the waste gas pollution index and the waste residue pollution index, outputting node pollution indexes, and obtaining a plurality of pollution indexes corresponding to the production nodes by analogy.
3. The method of claim 2, wherein the wastewater pollution index, the off-gas pollution index, and the slag pollution index are comprehensively calculated, the method further comprising:
acquiring enterprise address information of the target enterprise;
according to the enterprise address selection information, carrying out peripheral radius area division by taking the target enterprise as a center to obtain enterprise peripheral facility attributes;
performing information entropy calculation on the sub-pollution factors in the pollution indexes according to the attributes of the peripheral facilities of the enterprise, and outputting a weight division ratio;
and carrying out weight ratio configuration on the wastewater pollution index, the waste gas pollution index and the waste residue pollution index according to the weight ratio.
4. The method of claim 1, wherein the method further comprises:
performing noise test on the target enterprise to obtain noise test sample data, wherein the noise test sample data comprises internal environment test data and external environment test data;
determining the noise prevention performance of the target enterprise based on the noise reduction amplitude between the internal environment test data and the external environment test data;
when the noise prevention performance is less than or equal to the preset noise prevention performance, acquiring a new instruction;
and adding the noise index serving as a newly increased index into each node according to the newly increased instruction to perform pollution index optimization calculation.
5. The method of claim 1, wherein a pollution management node in the enterprise production chain is located according to the plurality of iterative pollution indicators, the method further comprising:
inputting the plurality of pollution indexes and the plurality of iterative pollution indexes into an index comparison unit for comparison to obtain iterative index difference degrees;
classifying the plurality of production chain nodes according to the iteration index difference to obtain nodes to be controlled, wherein the nodes to be controlled are front nodes of nodes corresponding to the iteration index difference which is greater than or equal to the preset iteration index difference;
and taking the node to be controlled as the pollution control node to perform pollution index reduction control.
6. The method of claim 5, wherein the node to be managed is used as the pollution management node for pollution index reduction management, and the method further comprises:
acquiring a real-time pollution control index of the node to be controlled;
calculating a difference value between the iteration index difference degree of the node to be controlled and the preset iteration index difference degree on the basis of the iteration index difference degree of the node to be controlled, and outputting a difference value interval for reducing the pollution monitoring standard;
and acquiring a control parameter for performing amplitude reduction control by taking the difference interval as an adaptive target and the real-time pollution control index as an output variable.
7. The method of claim 2, wherein the method further comprises:
acquiring information of a three-source processing flow of the target enterprise;
performing cross contamination probability calculation according to the information of the three-source processing flow, and outputting the cross contamination probability;
judging whether an index adjusting instruction is activated or not according to the cross contamination probability;
and carrying out index adjustment on the plurality of pollution indexes according to the index adjustment instruction.
8. The enterprise green production management and control system based on the big data is characterized by comprising:
the production chain acquisition module is used for accessing the enterprise information management terminal to acquire an enterprise production chain of a target enterprise;
the node acquisition module is used for acquiring a plurality of production chain nodes by carrying out attribute identification on each production node in the enterprise production chain;
a correlation coefficient obtaining module, configured to obtain a plurality of correlation coefficients according to correlations between the plurality of production chain nodes, where the plurality of correlation coefficients are degrees of production correlation between adjacent nodes;
an index output module configured to output a plurality of pollution indexes by performing pollution index analysis on each of the plurality of production chain nodes, wherein the plurality of pollution indexes correspond to the plurality of production chain nodes one to one;
the function building module is used for quantifying the plurality of pollution indexes as input based on the plurality of correlation coefficients as input variables and building a response objective function, wherein the response objective function is used for iteratively adjusting the plurality of pollution indexes corresponding to the plurality of production chain link points;
the iteration index output module is used for outputting a plurality of iteration pollution indexes according to the response objective function;
and the node positioning module is used for positioning a pollution control node in the enterprise production chain according to the plurality of iterative pollution indexes and performing pollution amplitude reduction control.
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