CN114021964A - Industrial enterprise equipment working condition judging and environment-friendly condition monitoring method - Google Patents
Industrial enterprise equipment working condition judging and environment-friendly condition monitoring method Download PDFInfo
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Abstract
The invention provides a method for judging the working condition of industrial enterprise equipment and monitoring the environmental protection condition, which comprises the following steps of firstly extracting the classification information of the running state of electric equipment according to the historical electric data of an enterprise; then, establishing a matching relation of the sewage production and treatment equipment according to the basic information and the data association degree of the sewage production and treatment equipment of the enterprise; and finally, judging whether the equipment state falls in a reasonable interval according to the enterprise electricity utilization information obtained by monitoring. By mining the power data of each monitoring point of an enterprise and extracting the power characteristics of the equipment in different running states, the discrimination of various working states of the equipment can be realized; by calculating the correlation degree among the monitoring points, the accurate matching of the corresponding relation among the multi-production sewage treatment points can be realized; through the statistical analysis of the working conditions of the production and pollution control equipment of each group, the monitoring of the environmental protection condition of enterprises and the evaluation of the unreasonable production degree can be realized.
Description
Technical Field
The invention belongs to the technical field of big data and environmental science, and relates to a method for judging the working condition of industrial enterprise equipment and monitoring the environmental protection condition.
Background
In recent years, industrialization is rapidly developed, and industrial production pollution is the largest pollution source. The enterprise equipment is in a production mode of producing pollution and controlling pollution imbalance for a long time, and can cause serious damage to the surrounding ecological environment. Traditional enterprise environmental protection condition monitoring mainly relies on the on-the-spot personnel to patrol and examine, and this mode manpower and materials cost is higher and efficiency is lower, can not adapt to the scale and grow gradually, the more complicated industrial pollution condition of form.
The electric power is used as indispensable energy consumption in industrial production activities, and the electric power data can be used as an effective basis for pollution discharge monitoring of enterprises. The machine learning and big data analysis technology is utilized to carry out statistical analysis and characteristic mining on the enterprise electricity utilization data, so that the operation condition of an enterprise equipment point can be timely and accurately reflected, and further the unreasonable operation condition of the enterprise can be researched and judged.
In the existing similar scheme, the operation condition of the equipment is generally judged by directly setting a threshold for distinguishing the on-off state of the electric equipment, the method has poor flexibility and high misjudgment rate in practical application; or the linear regression model is adopted to process data, and the constraint conditions of the equipment of each enterprise are different, so that the identification effect of the working condition of the equipment is not ideal, and the pollution discharge monitoring effect is not good.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention aims to provide a method for judging the working condition of industrial enterprise equipment and monitoring the environmental protection condition. By establishing a multi-working-condition identification and enterprise environmental protection condition monitoring model for the enterprise equipment points, the on-off state and the existing various transition states of each equipment point can be identified, and the normality and the abnormality of the enterprise environmental protection condition can be judged in the time sequence aspect. The method comprises the following steps: preprocessing the power data of the industrial enterprise; determining the working condition judgment of the closing and opening states and possible transitional states of all equipment in the industrial enterprise; automatic matching of each joint production pollution treatment equipment in a multi-monitoring-point complex enterprise; and accurately positioning the unreasonable production condition of the enterprise, and evaluating the scores of the unreasonable operation of the enterprise in time periods.
The invention specifically adopts the following technical scheme:
a working condition judgment and environmental protection condition monitoring method for industrial enterprise equipment is characterized by comprising the following steps: firstly, extracting classification information of the running state of electric equipment according to historical electric data of an enterprise; then, establishing a matching relation of the sewage production and treatment equipment according to the basic information and the data association degree of the sewage production and treatment equipment of the enterprise; and finally, judging whether the equipment state falls in a reasonable interval according to the enterprise electricity utilization information obtained by monitoring.
Further, according to historical power utilization data of enterprises, classification information of multiple running states of the power utilization equipment is extracted, and the construction of an equipment point multi-working-condition recognition model is completed:
firstly, historical electricity utilization data X ═ X of each monitoring point in each enterprise is read1,x2,......,xn]As a sample set, in order to avoid the problem of cluster center shift caused by zero values in the clustering process, a zero value data part is taken to form a set X' ═ X1′,x2′,......,xa′]Taking the non-zero data part to form a set X ═ X1″,x2″,......,xb″](ii) a And calculating the mean value E of the non-zero electricity consumption set X ″xAnd standard deviation s, the formula is as follows:
performing data dimensionless standard on all elements in the non-zero electricity consumption collection X' by adopting a 0-mean standardization modeNormalized non-zero collections as
The calculation formula is as follows:
secondly, aiming at the part with the electricity consumption of zero value, defining the equipment at the moment as a determined closing state; aiming at the part of the set of non-zero electricity consumption, a hybrid clustering algorithm is introduced to classify the opening state and various possible transitional states;
in order to determine various possible running states of the equipment, rough clustering is carried out on a non-zero data set by adopting a Canopy algorithm, and the number K of initial clustering centers is determined1(ii) a Further using the initial clustering number K1The dynamic K-means clustering mode is used for carrying out non-zero data set X on each monitoring point in each enterprise*Clustering is carried out respectively, and contour coefficients under each initial condition are calculated; recording the corresponding clustering book as k when the contour coefficient is maximumxThe cluster centers under the condition are sorted from small to large and recorded as
Further, the mathematical expectation and standard deviation of each cluster center are calculated by the following formula:
if a certain cluster has two cluster center-to-center distances
If the number of the data points in the cluster is less than 5 and the 3sigma threshold is less than the 3sigma threshold under the 3sigma principle, two data points are usedCluster clustering and taking the central mean value thereof as the center of the merged cluster, namely:
recording the total clustering number after processing as k, and the clustering center of each cluster as Cx *＝[c1 *,c2 *,......,ck *](ii) a And completing the identification and classification of the switch states and various possible transition states of each device based on Canopy-dynamic Kmeans.
Further, establishing a matching relationship of the sewage production and treatment equipment according to the basic information and the data association degree of the sewage production and treatment equipment of the enterprise specifically comprises the following steps:
reading basic information of monitoring points of a simple enterprise with only two monitoring points, and if the two monitoring point devices belong to sewage discharge devices, judging that the enterprise belongs to an unreasonable operation enterprise; if the two monitoring points are respectively sewage disposal equipment and sewage production equipment, a one-to-one corresponding relation is directly formed to form a group of equipment groups;
reading basic information of monitoring points of complex enterprises with more than two monitoring points, and if one or only one stain is produced, forming a one-to-many equipment group by using the stain producing and treating equipment; if there is only one stain treatment point, the stain production and treatment equipment forms a group of many-to-one equipment group;
if the number of the sewage production equipment and the number of the sewage treatment equipment in the complex enterprise are both larger than two, the historical electricity utilization data of each equipment point is utilized to calculate the association degree between every two equipment points, and then the two sewage production equipment and the sewage treatment equipment with the association are matched to form an equipment group.
Further, the correlation degree between every two is calculated, and then two sewage production devices with correlation are matched with the sewage treatment device, and the specific process of forming the device group comprises the following steps:
under an enterprise, a sewage production equipment set A ═ A1,A2,......,An]The sewage treatment equipment collects B ═ B1,B2,......,Bm]Wherein, the sewage producing equipment AiHas a historical electricity consumption data set of PAi＝[pAi1,pAi2,.......,pAin]Sewage treatment equipment BjHas a historical electricity consumption data set of PBj＝[pBj1,pBj2,.......,pBjn](ii) a Calculating each pollution treatment equipment and pollution production equipment AiThe correlation coefficient between the two is calculated according to the following formula:
wherein p isAi(k) Indicating the k moment of the fouling production equipment AiPower data of pBj(k) Device for treating pollution B by indicating k timejPower data of (a);
all power data and product equipment A under all pollution control equipmentiA maximum distance value of all power data;
all power data and product equipment A representing all pollution treatment equipmentiA minimum distance value of all power data;
secondly, calculate the contamination producing equipment AiThe grey correlation coefficient between the sewage treatment equipment and each sewage treatment equipment has the following calculation formula:
therein, ζj(k) Indicating pollution treatment equipment BjWith sewage-producing equipment AiA correlation coefficient at time k;
concentrating the scattered correlation coefficients at each moment into a correlation degree, and solving the mean value of the correlation coefficients of the pollution production equipment and the pollution control equipment at each moment as the quantity expression of the correlation degree between the comparative equipment; the association degrees between the pollution control equipment and certain pollution production equipment are arranged according to the size sequence to form an association sequence, and the pollution control equipment with the highest association degree and the product equipment form a group to form an equipment group with a correlation relationship.
Furthermore, the corresponding conditions of all the devices in the multi-monitoring-point complex enterprise are integrated, and the corresponding matching of the sewage production and the sewage treatment device of the multi-point enterprise is completed.
Further, the enterprise electricity utilization information is judged by establishing an enterprise environmental protection condition monitoring and evaluating model:
based on the equipment multiplex condition is judged and the associated equipment in the multiple monitoring point enterprise is matched to 8 data points are used as data windows, whether the operation condition of the enterprise is reasonable is judged in a sliding mode: recording n kinds of determined off state, multiple transition state and switch state of a certain device, recording as [ determined off state, transition state 1, transition state 2 … …, complete on state]And in turn give numerical labels to different switching degrees from 0 to n-1, denoted as L ═ 0,1,2i,...,n-1]And normalizing the label by adopting a normalization mode:
li *＝(li-lmin)/(lmax-lmin) Formula (11)
Recording some normalized matching completion equipment A in the equipment group iiSewage treatment equipment BiThe label sets in the data window are respectively
Respectively calculating the device opening weights of the two devices in the time window, wherein the formula is as follows:
the larger the omega is, the larger the opening state of the pollution production equipment is in the time window, compared with the opening state of the pollution treatment equipment, the larger the opening state of the pollution production equipment is, namely the pollution production part is far larger than the pollution treatment part in the time period of the enterprise, and the enterprise is in an unreasonable production pollution operation state; and determining whether the production running state of the enterprise is reasonable or not through defining omega.
The electronic equipment comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and is characterized in that the steps of the method for judging the working condition of the industrial enterprise equipment and monitoring the environmental protection condition are realized when the processor executes the program.
And a non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for determining the working condition of the industrial enterprise equipment and monitoring the environmental protection condition as described above.
Compared with the prior art, the invention and the optimal scheme thereof provide a novel industrial enterprise environment-friendly monitoring method. The monitoring method can realize the discrimination of various working states of the equipment by mining the power data of each monitoring point of an enterprise and extracting the power characteristics of the equipment in different running states; by calculating the correlation degree among the monitoring points, the accurate matching of the corresponding relation among the multi-production sewage treatment points can be realized; through the statistical analysis of the working conditions of the production and pollution control equipment of each group, the monitoring of the environmental protection condition of enterprises and the evaluation of the unreasonable production degree can be realized.
The monitoring method has the advantages that more detailed multi-operation state distinguishing can be carried out on the working conditions of each device; the association matching between the devices can improve the accuracy of enterprise environmental protection monitoring and accurately position the abnormal production state device group.
The method specifically comprises the following steps:
through the multi-working-condition identification model of the equipment point, the on-off state of the equipment and various transition states between on and off can be identified.
Aiming at the matching algorithm of each monitoring point of enterprises with more sewage production and sewage treatment points, the accurate matching of one-to-one, one-to-many and many-to-one among the monitoring points can be realized.
Aiming at the matching algorithm of each monitoring point of enterprises with more sewage production and sewage treatment points, the accurate matching of one-to-one, one-to-many and many-to-one among the monitoring points can be realized.
The enterprise environmental protection condition monitoring model is established, the unreasonable production condition of the enterprise can be accurately positioned, and the unreasonable running score of the enterprise in a time period is evaluated.
The device realizes the identification of the working condition of the device and the environmental monitoring of enterprises, and provides technical support for further realizing the accurate environmental management and control.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic flow chart of a method for judging the working condition of industrial enterprise equipment and monitoring the environmental protection condition in the embodiment of the invention.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
fig. 1 is a schematic flow chart of a method for judging the working condition of industrial enterprise equipment and monitoring the environmental protection condition according to the embodiment of the invention, and the steps referring to the chart are as follows:
establishing an equipment point multi-working condition identification model:
firstly, historical electricity utilization data X ═ X of each monitoring point in each enterprise is read1,x2,......,xn]As a sample set, in order to avoid the problem of cluster center shift caused by zero values in the clustering process, a zero value data part is taken to form a set X' ═ X1′,x2′,......,xa′]Taking the non-zero data part to form a set X ═ X1″,x2″,......,xb″]. And calculating the mean value E of the non-zero electricity consumption set X ″xAnd standard deviation s, the formula is as follows:
performing dimensionless data standardization preprocessing on all elements in the non-zero electricity consumption collection X' by adopting a 0-mean standardization mode, and recording the standardized non-zero collection as
The calculation formula is as follows:
secondly, for the part with the electricity consumption as zero value, the equipment at the moment is defined as a determined closing state. Aiming at the part of the set of non-zero electricity consumption, a hybrid clustering algorithm is introduced to classify the opening state and various possible transitional states.
In order to determine various possible running states of the equipment, rough clustering is carried out on a non-zero data set by adopting a Canopy algorithm with strong anti-interference capability, and the number K of initial clustering centers is determined1. And in order to achieve the best distinguishing effect of various running states, the initial clustering number K is adopted1The dynamic K-means clustering mode is used for carrying out non-zero data set X on each monitoring point in each enterprise*And clustering respectively, and calculating the contour coefficient under each initial condition. Recording the corresponding clustering book as k when the contour coefficient is maximumxThe cluster centers under the condition are sorted from small to large and recorded as
Further, the mathematical expectation and standard deviation of each cluster center are calculated by the following formula:
if a certain cluster has two cluster center-to-center distances
And if the number of the data points in the cluster is less than 5 and the 3sigma threshold is less than the 3sigma threshold under the 3sigma principle, combining the two clusters and taking the central mean value thereof as the center of the combined cluster, namely:
recording the total clustering number after processing as k, and the clustering center of each cluster as Cx *＝[c1 *,c2 *,......,ck *]. And completing the identification and classification of the switch states and various possible transition states of each device based on Canopy-dynamic Kmeans.
Establishing an association matching model of each monitoring point:
reading basic information of monitoring points of a simple enterprise with only two monitoring points, and if the two monitoring point devices belong to sewage discharge devices, judging that the enterprise belongs to an unreasonable operation enterprise; if the two monitoring points are respectively sewage disposal equipment and sewage production equipment, the one-to-one corresponding relation is directly formed, and a group of equipment groups is formed.
Reading basic information of monitoring points of complex enterprises with more than two monitoring points, and if one or only one stain is produced, forming a one-to-many equipment group by using the stain producing and treating equipment; if there is only one dirt-treating point, the dirt-producing and dirt-treating equipment forms a group of many-to-one equipment group.
If the number of the sewage production equipment and the number of the sewage treatment equipment in the complex enterprise are both larger than two, the historical electricity utilization data of each equipment point is utilized to calculate the association degree between every two equipment points, and then the two sewage production equipment and the sewage treatment equipment with the association are matched to form an equipment group.
Under an enterprise, a sewage production equipment set A ═ A1,A2,......,An]The sewage treatment equipment collects B ═ B1,B2,......,Bm]Wherein, the sewage producing equipment AiHas a historical electricity consumption data set of PAi＝[pAi1,pAi2,.......,pAin]Sewage treatment equipment BjHas a historical electricity consumption data set of PBj＝[pBj1,pBj2,.......,pBjn]. Calculating each pollution treatment equipment and pollution production equipment AiThe correlation coefficient between the two is calculated according to the following formula:
wherein p isAi(k) Indicating the k moment of the fouling production equipment AiPower data of pBj(k) Device for treating pollution B by indicating k timejPower data of (a);
all power data and product equipment A under all pollution control equipmentiA maximum distance value of all power data;
all power data and product equipment A representing all pollution treatment equipmentiA minimum distance value of all power data;
secondly, calculate the contamination producing equipment AiThe grey correlation coefficient between the sewage treatment equipment and each sewage treatment equipment has the following calculation formula:
therein, ζj(k) Indicating pollution treatment equipment BjWith sewage-producing equipment AiThe correlation coefficient at time k.
And concentrating the scattered correlation coefficients at each moment into a correlation degree, and solving the mean value of the correlation coefficients of the pollution production equipment and the pollution control equipment at each moment as the quantity expression of the correlation degree between the comparative equipment. The association degrees between the pollution control equipment and certain pollution production equipment are arranged according to the size sequence to form an association sequence, and the pollution control equipment with the highest association degree and the product equipment form a group to form an equipment group with a correlation relationship.
And finally, integrating the corresponding conditions of all the devices in the multi-monitoring-point complex enterprise to finish the corresponding matching of the pollution production and the pollution control device of the multi-point enterprise.
Establishing an enterprise environmental protection condition monitoring and evaluating model:
based on the multi-working-condition judgment of the equipment and the matching of the associated equipment in the enterprise with multiple monitoring points, 8 data points are used as data windows, and whether the operation working condition of the enterprise is reasonable or not is judged in a sliding mode. The definite closing state, various transition states and definite switch state of a certain device are n in total and are marked as [ definite closing state, transition state 1, transition state 2 … … and complete opening state]And in turn give numerical labels to different switching degrees from 0 to n-1, denoted as L ═ 0,1,2i,...,n-1]And normalizing the label by adopting a normalization mode.
li *＝(li-lmin)/(lmax-lmin) Formula (11)
Recording some normalized matching completion equipment A in the equipment group iiSewage treatment equipment BiThe label sets in the data window are respectively
Respectively calculating the device opening weights of the two devices in the time window, wherein the formula is as follows:
the larger the omega is, the larger the opening state of the pollution production equipment is in the time window compared with the opening state of the pollution treatment equipment, namely the pollution production part of the enterprise is far larger than the pollution treatment part in the time period, and the enterprise is in an unreasonable production pollution operation state. By defining omega, whether the production running state of an enterprise is reasonable or not can be judged, and necessary early warning is needed for the enterprise working in an unreasonable state for a long time.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, and the scope of protection is still within the scope of the invention.
The above system and method provided by this embodiment can be stored in a computer readable storage medium in a coded form, and implemented in a computer program, and inputs basic parameter information required for calculation through computer hardware, and outputs a calculation result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The present invention is not limited to the above-mentioned preferred embodiments, and any other method for determining the working condition of the industrial equipment and monitoring the environmental protection situation can be obtained according to the teaching of the present invention.
Claims (8)
1. A working condition judgment and environmental protection condition monitoring method for industrial enterprise equipment is characterized by comprising the following steps: firstly, extracting the classification information of the running state of the electric equipment according to the historical electricity utilization information of an enterprise; then, establishing a matching relation of the sewage production and treatment equipment according to the basic information and the data association degree of the sewage production and treatment equipment of the enterprise; and finally, judging whether the equipment state falls in a reasonable interval according to the enterprise electricity utilization information obtained by monitoring.
2. The industrial enterprise equipment working condition distinguishing and environment-friendly condition monitoring method according to claim 1, characterized in that: according to the historical power utilization data of enterprises, the classification information of various running states of the power utilization equipment is extracted, and the construction of an equipment point multi-working condition recognition model is completed:
firstly, historical electricity utilization data X ═ X of each monitoring point in each enterprise is read1,x2,......,xn]As a sample set, in order to avoid the problem of cluster center shift caused by zero values in the clustering process, a zero value data part is taken to form a set X' ═ X1′,x2′,......,xa′]Taking the non-zero data part to form a set X ═ X1″,x2″,......,xb″](ii) a And calculating the mean value E of the non-zero electricity consumption set X ″xAnd standard deviation s, the formula is as follows:
performing dimensionless data standardization preprocessing on all elements in the non-zero electricity consumption collection X' by adopting a 0-mean standardization mode, and recording the standardized non-zero collection as
The calculation formula is as follows:
secondly, aiming at the part with the electricity consumption of zero value, defining the equipment at the moment as a determined closing state; aiming at the part of the set of non-zero electricity consumption, a hybrid clustering algorithm is introduced to classify the opening state and various possible transitional states;
in order to determine various possible running states of the equipment, rough clustering is carried out on a non-zero data set by adopting a Canopy algorithm, and the number K of initial clustering centers is determined1(ii) a Further using the initial clustering number K1The dynamic K-means clustering mode is used for carrying out non-zero data set X on each monitoring point in each enterprise*Clustering is carried out respectively, and contour coefficients under each initial condition are calculated; recording the corresponding clustering book as k when the contour coefficient is maximumxThe cluster centers under the condition are sorted from small to large and marked as Cx＝[c1,c2,......,ckx](ii) a Further, the mathematical expectation and standard deviation of each cluster center are calculated by the following formula:
if a certain cluster has two cluster center-to-center distances
And if the number of the data points in the cluster is less than 5 and the 3sigma threshold is less than the 3sigma threshold under the 3sigma principle, combining the two clusters and taking the central mean value thereof as the center of the combined cluster, namely:
recording the total clustering number after the processing as k, and the clustering center of each cluster as
And completing the identification and classification of the switch states and various possible transition states of each device based on Canopy-dynamic Kmeans.
3. The industrial enterprise equipment working condition distinguishing and environment-friendly condition monitoring method according to claim 2, characterized in that: establishing a matching relation of the sewage production and treatment equipment according to the basic information and the data association degree of the sewage production and treatment equipment of the enterprise specifically comprises the following steps:
reading basic information of monitoring points of a simple enterprise with only two monitoring points, and if the two monitoring point devices belong to sewage discharge devices, judging that the enterprise belongs to an unreasonable operation enterprise; if the two monitoring points are respectively sewage disposal equipment and sewage production equipment, a one-to-one corresponding relation is directly formed to form a group of equipment groups;
reading basic information of monitoring points of complex enterprises with more than two monitoring points, and if one or only one stain is produced, forming a one-to-many equipment group by using the stain producing and treating equipment; if there is only one stain treatment point, the stain production and treatment equipment forms a group of many-to-one equipment group;
if the number of the sewage production equipment and the number of the sewage treatment equipment in the complex enterprise are both larger than two, the historical electricity utilization data of each equipment point is utilized to calculate the association degree between every two equipment points, and then the two sewage production equipment and the sewage treatment equipment with the association are matched to form an equipment group.
4. The industrial enterprise equipment working condition distinguishing and environment-friendly condition monitoring method according to claim 3, characterized in that:
calculating the degree of correlation between every two sewage treatment units, and matching the two correlated sewage production units with the sewage treatment units, wherein the specific process of forming the equipment group comprises the following steps:
under an enterprise, a sewage production equipment set A ═ A1,A2,......,An]The sewage treatment equipment collects B ═ B1,B2,......,Bm]Wherein, the sewage producing equipment AiHas a historical electricity consumption data set of PAi＝[pAi1,pAi2,.......,pAin]Sewage treatment equipment BjHas a historical electricity consumption data set of PBj＝[pBj1,pBj2,.......,pBjn](ii) a Calculating each pollution treatment equipment and pollution production equipment AiThe correlation coefficient between the two is calculated according to the following formula:
wherein p isAi(k) Indicating the k moment of the fouling production equipment AiPower data of pBj(k) Device for treating pollution B by indicating k timejPower data of (a);
all power data and product equipment A under all pollution control equipmentiA maximum distance value of all power data;
all power data and product equipment A representing all pollution treatment equipmentiA minimum distance value of all power data;
secondly, calculate the contamination producing equipment AiThe grey correlation coefficient between the sewage treatment equipment and each sewage treatment equipment has the following calculation formula:
therein, ζj(k) Indicating pollution treatment equipment BjWith sewage-producing equipment AiA correlation coefficient at time k;
concentrating the scattered correlation coefficients at each moment into a correlation degree, and solving the mean value of the correlation coefficients of the pollution production equipment and the pollution control equipment at each moment as the quantity expression of the correlation degree between the comparative equipment; the association degrees between the pollution control equipment and certain pollution production equipment are arranged according to the size sequence to form an association sequence, and the pollution control equipment with the highest association degree and the product equipment form a group to form an equipment group with a correlation relationship.
5. The industrial enterprise equipment working condition distinguishing and environment-friendly condition monitoring method according to claim 4, characterized in that: and integrating the corresponding conditions of all the devices in the multi-monitoring-point complex enterprise to finish the corresponding matching of the pollution production and the pollution control device of the multi-point enterprise.
6. The industrial enterprise equipment working condition distinguishing and environment-friendly condition monitoring method according to claim 3, characterized in that: the method comprises the following steps of judging the power utilization information of the enterprise by establishing an enterprise environmental protection condition monitoring and evaluating model:
based on the equipment multiplex condition is judged and the associated equipment in the multiple monitoring point enterprise is matched to 8 data points are used as data windows, whether the operation condition of the enterprise is reasonable is judged in a sliding mode: recording n kinds of determined off state, multiple transition state and switch state of a certain device, recording as [ determined off state, transition state 1, transition state 2 … …, complete on state]And in turn give numerical labels to different switching degrees from 0 to n-1, denoted as L ═ 0,1,2i,...,n-1]And normalizing the label by adopting a normalization mode:
li *＝(li-lmin)/(lmax-lmin) Formula (11)
Recording some normalized matching completion equipment A in the equipment group iiSewage treatment equipment BiThe label sets in the data window are respectively
Respectively calculating the device opening weights of the two devices in the time window, wherein the formula is as follows:
the larger the omega is, the larger the opening state of the pollution production equipment is in the time window, compared with the opening state of the pollution treatment equipment, the larger the opening state of the pollution production equipment is, namely the pollution production part is far larger than the pollution treatment part in the time period of the enterprise, and the enterprise is in an unreasonable production pollution operation state; and determining whether the production running state of the enterprise is reasonable or not through defining omega.
7. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for condition determination and environmental condition monitoring of industrial enterprise devices as claimed in any one of claims 1 to 6 when executing the program.
8. A non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for condition discrimination and environmental condition monitoring of industrial enterprise equipment according to any one of claims 1-6.
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