Intelligent energy consumption management method and system based on multi-dimensional refinement

Abstract

Disclosed in the present invention are an intelligent energy consumption management method and system based on multi-dimensional refinement. The method comprises the following steps: using a machine learning algorithm to perform learning fitting on first data information and second data information to an information data association measurement and calculation model representing an association relationship between the first data information and the second data information; and on the basis of a sequence position of the current first data information in a first data information descending sequence, determining an energy consumption optimization direction for reducing energy consumption of a production line. In the present invention, measurement and calculation of energy consumption of a gypsum board production line are realized, refined management of energy consumption conditions is realized in related links such as energy consumption devices, energy consumption states, and energy consumption environments, an energy consumption optimization direction for reducing energy consumption of a production line is determined, information mining of energy consumption data is realized, and an energy consumption optimization direction mined from the energy consumption data is visually displayed.

Description

A smart energy consumption management method and system based on multi-dimensional refinement Technical Field

[0001] The present invention relates to the technical field of intelligent energy consumption management of gypsum boards, and in particular to a multi-dimensional and refined intelligent energy consumption management method and system. Background Art

[0002] Energy consumption in the gypsum board production process includes electricity consumption, water consumption, gas consumption, and compressed air consumption. Energy consumption management in the gypsum board production process is to manage one or more of electricity consumption, water consumption, gas consumption, and compressed air consumption. Existing energy consumption management technologies usually include energy data collection and analysis. By installing sensors, meters and other equipment, energy data used by buildings or equipment can be collected in real time; energy consumption monitoring can monitor the energy consumption of buildings or equipment in real time, such as electricity, water, and gas consumption; energy report generation can generate various energy reports as needed to display energy consumption.

[0003] Current energy management technology focuses on real-time monitoring and display of energy consumption on production lines. The management is rough. Real-time monitoring will lead to a lag in the discovery of energy consumption problems. There is a lack of refined management of energy consumption in related links such as energy-consuming equipment, energy consumption status, and energy consumption environment. In addition, there is a lack of information mining of energy consumption data, making it difficult to intuitively and visually display the direction of energy consumption optimization.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent energy consumption management method based on multi-dimensional refinement to solve the technical problems in the existing technology that lead to a lag in the discovery of energy consumption problems, lack of refined management of energy consumption in related links such as energy-consuming equipment, energy consumption status, and energy consumption environment, and lack of information mining of energy consumption data, making it difficult to intuitively and visually display the direction of energy consumption optimization.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] A multi-dimensional and refined intelligent energy consumption management method includes the following steps:

[0008] Tracking and monitoring first data information of multiple gypsum board production lines in a production schedule, and second data information having a time-series corresponding relationship with the first data information, wherein the first data information includes energy consumption data, and the second data information includes energy-using equipment data, energy status data, and energy environment data;

[0009] Using a machine learning algorithm to perform learning and fitting on the first data information and the second data information to obtain an information data association measurement model that characterizes the association relationship between the first data information and the second data information, so as to achieve energy consumption measurement of the gypsum board production line;

[0010] Obtaining a data value interval of the second data information in the gypsum board production line, and determining a data value interval of the first data information within the data value interval of the second data information using an information data association calculation model;

[0011] Arranging the first data information in descending order within a data value interval of the first data information to obtain a first data information descending sequence for characterizing a trend of energy consumption reduction, and arranging the second data information in a data value interval of the second data information according to the sequence order of the first data information descending sequence to obtain a second data information trend sequence, wherein the second data information trend sequence is used to characterize an energy-consuming equipment data trend, an energy consumption status data trend, and an energy consumption environment data trend corresponding to the energy consumption reduction trend in the second data information;

[0012] Obtain the current second data information of the gypsum board production line, use the information data association measurement model to obtain the current first data information based on the current second data information, and determine the energy consumption optimization direction for reducing the energy consumption of the production line based on the sequence position of the current first data information in the descending sequence of the first data information.

[0013] As a preferred solution of the present invention, the acquisition of the first data information and the second data information includes:

[0014] Monitoring energy consumption in each gypsum board production line in multiple time periods to obtain energy consumption data, wherein the energy consumption includes at least one of the following: electricity consumption, water consumption, gas consumption, and compressed air consumption;

[0015] Classify and store the energy consumption data according to time periods to obtain the first data information;

[0016] Monitor the energy-consuming equipment, energy-using status and energy-using environment during the time period of the first data information to obtain energy-consuming equipment data, energy-using status data and energy-using environment data.

[0017] The energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are averaged and denoised, and the denoised energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are classified and stored according to time periods to obtain the second data information.

[0018] As a preferred solution of the present invention, the method of using a machine learning algorithm to learn and fit the first data information and the second data information to obtain an information data association calculation model for characterizing the association relationship between the first data information and the second data information includes:

[0019] Using the second data information within the same time period as an input item of the CNN neural network, using the first data information within the same time period as an output item of the CNN neural network, using the CNN neural network to learn and train the input item and the output item of the CNN neural network, and fitting to obtain the information data management and measurement model;

[0020] The model expression of the information data management calculation model is: Data_1 t =CNN(Data_2 t );

[0021] Where Data_1 t is the first data information in time period t, Data_2 t is the second data information within the time period t, CNN is the CNN neural network, and t is the identifier of the time period.

[0022] As a preferred solution of the present invention, the data value range for obtaining the second data information in the gypsum board production line includes:

[0023] Extracting the allowable adjustment range of the energy-consuming equipment data from the equipment manual of the energy-consuming equipment in the gypsum board production line, and using the allowable adjustment range of the energy-consuming equipment data as the data value interval of the energy-consuming equipment data in the second data information;

[0024] extracting a status data threshold of energy usage status data of the energy-consuming equipment based on the production log of the gypsum board production line, using the status data threshold of the energy usage status data as a data value interval threshold of the energy usage status data, and using a data value interval constructed by using the data value interval threshold of the energy usage status data as a data value interval of the energy usage status data in the second data information;

[0025] The allowable range of the energy usage environment data of the energy-consuming equipment is extracted according to the equipment manual of the energy-consuming equipment in the gypsum board production line, and the allowable range of the energy usage environment data is used as the data value range of the energy usage environment data in the second data information.

[0026] As a preferred solution of the present invention, the method of determining the data value interval of the first data information within the data value interval of the second data information by using the information data association calculation model includes:

[0027] Performing full permutation and combination of the energy-consuming device data, the energy-consuming status data, and the energy-consuming environment data in the data value interval of the energy-consuming device data, the data value interval of the energy-consuming status data, and the data value interval of the energy-consuming environment data in the second data information;

[0028] Substituting all permutations and combinations of energy-consuming equipment data, energy-consuming status data, and energy-consuming environment data in the second data information into the information data association calculation model in sequence, and obtaining first data information corresponding to all permutations and combinations;

[0029] The first data information corresponding to all permutation combinations are arranged one by one within the matching interval to obtain the data value interval of the first data information.

[0030] As a preferred solution of the present invention, the method of arranging the second data information in the data value interval of the second data information according to the sequence order of the descending sequence of the first data information to obtain the second data information trend sequence includes:

[0031] Extracting, in sequence, the energy-consuming device data corresponding to each sequence item in the descending sequence of the first data information from the data value interval of the energy-consuming device data in the second data information, and obtaining the energy-consuming device data trend sequence of the second data information based on the arrangement order of the sequence items in the descending sequence of the first data information, so as to characterize the energy-consuming device data trend corresponding to the energy consumption reduction trend;

[0032] In the data value interval of the energy usage status data of the second data information, the energy usage status data corresponding to each sequence item in the descending sequence of the first data information is sequentially extracted, and the energy usage status data corresponding to each sequence item in the descending sequence of the first data information is used to obtain an energy usage status data trend sequence of the second data information according to the arrangement order of the sequence items in the descending sequence of the first data information, so as to characterize the energy usage status data trend corresponding to the energy consumption reduction trend;

[0033] In the data value range of the energy consumption environment data of the second data information, the energy consumption environment data corresponding to each sequence item in the descending sequence of the first data information are extracted in sequence, and the energy consumption environment data corresponding to each sequence item in the descending sequence of the first data information are obtained according to the arrangement order of the sequence items in the descending sequence of the first data information to obtain the energy consumption status data trend sequence of the second data information, so as to characterize the energy consumption environment data trend corresponding to the energy consumption reduction trend.

[0034] As a preferred solution of the present invention, the step of obtaining the current second data information of the gypsum board production line and obtaining the current first data information based on the current second data information using an information data association calculation model includes:

[0035] Monitoring the energy-consuming equipment, energy-using status, and energy-using environment of the production line during a current time period to obtain energy-consuming equipment data, energy-using status data, and energy-using environment data for the current time period, and using the energy-consuming equipment data, energy-using status data, and energy-using environment data for the current time period as current second data information of the gypsum board production line;

[0036] The current second data information of the gypsum board production line is substituted into the information data association calculation model to obtain the current first data information of the gypsum board production line.

[0037] As a preferred solution of the present invention, determining an energy consumption optimization direction for reducing energy consumption of the production line based on the sequence position of the current first data information in the descending sequence of the first data information includes:

[0038] Map the current first data information to the first data information descending sequence, wherein,

[0039] When the current first data information is greater than the final value of the sequence in the descending sequence of the first data information, the second data information corresponding to the final value of the sequence in the descending sequence of the first data information is used as the current second data information in the second data information trend sequence to achieve an energy consumption optimization direction of minimizing the current energy consumption;

[0040] When the current first data information is equal to or less than the final value of the sequence in the descending sequence of the first data information, the current second data information is used as the current second data information to achieve the energy consumption optimization direction of maintaining the lowest current energy consumption.

[0041] As a preferred solution of the present invention, the current second data information realizes an energy consumption optimization direction of minimizing current energy consumption, including:

[0042] The energy-consuming device data in the current second data information is directed toward the energy-consuming device data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as an energy consumption optimization direction for minimizing the current energy consumption based on the energy-consuming device data adjustment;

[0043] The energy consumption status data in the current second data information is adjusted toward the energy consumption status data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as the energy consumption optimization direction for minimizing the current energy consumption based on the energy consumption status data;

[0044] The energy consumption environment data in the current second data information is adjusted toward the energy consumption environment data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as an energy consumption optimization direction for minimizing the current energy consumption based on the energy consumption environment data;

[0045] The current second data information realizes an energy consumption optimization direction in which current energy consumption is maintained at a minimum, including:

[0046] Using the energy-consuming device data in the current second data information as an energy consumption optimization direction to adjust the current energy consumption to the lowest possible level based on the energy-consuming device data;

[0047] Using the energy consumption status data in the current second data information as an energy consumption optimization direction for adjusting the current energy consumption to maintain the lowest energy consumption based on the energy consumption status data;

[0048] The energy consumption environment data in the current second data information is used as an energy consumption optimization direction to adjust the current energy consumption to the lowest level based on the energy consumption environment data.

[0049] As a preferred embodiment of the present invention, the present invention provides a smart energy consumption management system that applies a multi-dimensional and refined smart energy consumption management method, including:

[0050] An information collection unit, used for collecting second data information of the gypsum board production line in real time;

[0051] An information storage unit storing an information data association calculation model for calculating energy consumption of a gypsum board production line;

[0052] An information calculation unit calculates the first data information in real time based on the second data information using an information data association calculation model, and determines an energy consumption optimization direction of the gypsum board production line based on the first data information;

[0053] an information display unit, configured to display the energy consumption of the gypsum board production line based on the first data information, and to display the energy consumption optimization direction of the gypsum board production line, wherein the first data information includes energy consumption data, and the second data information includes energy-consuming equipment data, energy consumption status data, and energy consumption environment data;

[0054] The information operation unit is communicatively connected with the information acquisition unit, the information storage unit and the information display unit respectively.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention uses a machine learning algorithm to learn and fit the first data information and the second data information to obtain an information data association measurement model that characterizes the association relationship between the first data information and the second data information, so as to realize the measurement of the energy consumption of the gypsum board production line, realize the refined management of energy consumption in related links such as energy-consuming equipment, energy-consuming status, and energy-consuming environment, obtain the current second data information of the gypsum board production line, and use the information data association measurement model to obtain the current first data information based on the current second data information. Based on the sequence position of the current first data information in the descending sequence of the first data information, the energy consumption optimization direction for reducing the energy consumption of the production line is determined, thereby realizing information mining of energy consumption data and intuitively and visually displaying the energy consumption optimization direction mined from the energy consumption data. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0058] FIG1 is a flow chart of a multi-dimensional and refined intelligent energy consumption management method provided by an embodiment of the present invention;

[0059] FIG2 is a block diagram of a smart energy management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] The current energy consumption management technology focuses on real-time monitoring and real-time display of energy consumption of the production line. The management is rough, which will lead to a lag in the discovery of energy consumption problems. There is a lack of refined management of energy consumption in related links such as energy-consuming equipment, energy consumption status, and energy consumption environment. In addition, there is a lack of information mining of energy consumption data, making it difficult to intuitively and visually display the direction of energy consumption optimization. Therefore, the present invention provides a smart energy consumption management method based on multi-dimensional refinement, which uses energy-consuming equipment data, energy consumption status data, and energy consumption environment data to construct an information data association measurement model to achieve refined management and analysis of the energy consumption of the gypsum board production line in multiple dimensions of energy-consuming equipment, energy consumption status, and energy consumption environment. That is, the information data association measurement model is used to determine the energy consumption optimization direction for reducing the energy consumption of the production line, and to achieve refined optimization of energy consumption from multiple dimensions of energy-consuming equipment, energy consumption status, and energy consumption environment.

[0062] As shown in FIG1 , the present invention provides a multi-dimensional and refined intelligent energy consumption management method, comprising the following steps:

[0063] Track and monitor the first data information of multiple gypsum board production lines during their production schedules, as well as second data information that has a time-series relationship with the first data information. The first data information includes energy consumption data, and the second data information includes energy-using equipment data, energy usage status data, and energy usage environment data. Energy-using equipment data can include energy-using equipment production performance data (output, accuracy, etc.) and equipment production operating parameters (current, power, etc.); energy usage status data can include energy-using equipment operating status data (underload energy usage status, overload energy usage status, etc.) generated by producing gypsum boards of different specifications. The energy usage environment refers to the environmental data of the equipment (temperature, humidity, dust concentration, etc.). In actual application, select according to your needs.

[0064] Using a machine learning algorithm to perform learning and fitting on the first data information and the second data information to obtain an information data association measurement model that characterizes the association relationship between the first data information and the second data information, so as to achieve energy consumption measurement of the gypsum board production line;

[0065] Obtaining a data value interval of the second data information in the gypsum board production line, and determining a data value interval of the first data information within the data value interval of the second data information using an information data association calculation model;

[0066] Arranging the first data information in descending order within a data value interval of the first data information to obtain a first data information descending sequence for characterizing a trend of energy consumption reduction, and arranging the second data information in a data value interval of the second data information according to the sequence order of the first data information descending sequence to obtain a second data information trend sequence, wherein the second data information trend sequence is used to characterize an energy-consuming equipment data trend, an energy consumption status data trend, and an energy consumption environment data trend corresponding to the energy consumption reduction trend in the second data information;

[0067] Obtain the current second data information of the gypsum board production line, use the information data association measurement model to obtain the current first data information based on the current second data information, and determine the energy consumption optimization direction for reducing the energy consumption of the production line based on the sequence position of the current first data information in the descending sequence of the first data information.

[0068] In order to achieve refined management of the energy consumption of the gypsum board production line, the present application refines the measurement of the energy consumption of the gypsum board production line to multiple dimensions, such as energy-consuming equipment, energy consumption status and energy consumption environment, and uses energy-consuming equipment data, energy consumption status data and energy consumption environment data to refine the energy consumption of the gypsum board production line, and realizes the refined prediction of energy consumption using multi-dimensional data, so that the accuracy of the energy consumption prediction is guaranteed. The energy consumption of the energy-consuming equipment of the gypsum board production line in the energy consumption state and the energy consumption environment can be foreseen in advance based on the energy-consuming equipment data, energy consumption status data and energy consumption environment data. The predictive energy consumption of the gypsum board production line can also predictively discover energy consumption problems and take corresponding measures to solve them. Therefore, the present invention uses energy-consuming equipment data, energy consumption status data and energy consumption environment data to construct an information data association measurement model, so as to predict the energy consumption of the gypsum board production line, and use multi-dimensional data to perform refined prediction of energy consumption, so that the accuracy of the energy consumption prediction is guaranteed. The construction of the information data association measurement model is as follows:

[0069] The acquisition of the first data information and the second data information includes:

[0070] Monitoring energy consumption in each gypsum board production line in multiple time periods to obtain energy consumption data, where the energy consumption includes at least one of the following: electricity consumption, water consumption, gas consumption, and compressed air consumption;

[0071] Classify and store the energy consumption data according to time periods to obtain first data information;

[0072] Monitor the energy-consuming equipment, energy-using status and energy-using environment during the time period of the first data information to obtain energy-consuming equipment data, energy-using status data and energy-using environment data.

[0073] The energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are averaged and denoised, and the denoised energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are classified and stored according to time periods to obtain second data information.

[0074] Using a machine learning algorithm to perform learning and fitting on the first data information and the second data information to obtain an information data association measurement model for characterizing the association relationship between the first data information and the second data information, including:

[0075] The second data information within the same time period is used as an input item of the CNN neural network, and the first data information within the same time period is used as an output item of the CNN neural network. The CNN neural network is used to train the input item and the output item of the CNN neural network, and an information data management and measurement model is obtained by fitting.

[0076] The model expression of the information data management calculation model is: Data_1 t =CNN(Data_2 t );

[0077] Where Data_1 t is the first data information in time period t, Data_2 t is the second data information within the time period t, CNN is the CNN neural network, and t is the identifier of the time period.

[0078] In order to conduct refined information mining on energy consumption and thus obtain energy consumption optimization results, the present invention utilizes an information data management and calculation model to accumulate data intervals of energy consumption for each energy-consuming equipment in each energy consumption state and each energy consumption environment in the gypsum board production line. In the data intervals of energy consumption, the energy consumption optimization direction of the energy-consuming equipment, the energy consumption state and the energy consumption environment is mined, and the energy consumption optimization direction of adjusting the energy-consuming equipment, the energy consumption optimization direction of adjusting the energy consumption state and the energy consumption optimization direction of adjusting the energy consumption environment are respectively obtained, thereby realizing refined energy consumption optimization in the three dimensions of energy-consuming equipment, energy consumption state and energy consumption environment, as follows:

[0079] The data value range of the second data information in the gypsum board production line is obtained, including:

[0080] Extracting the allowable adjustment range of the energy-consuming equipment data from the equipment manual of the energy-consuming equipment in the gypsum board production line, and using the allowable adjustment range of the energy-consuming equipment data as the data value interval of the energy-consuming equipment data in the second data information;

[0081] extracting a status data threshold of energy usage status data of the energy-consuming equipment based on the production log of the gypsum board production line, using the status data threshold of the energy usage status data as a data value interval threshold of the energy usage status data, and using a data value interval constructed by using the data value interval threshold of the energy usage status data as a data value interval of the energy usage status data in the second data information;

[0082] The allowable range of the energy usage environment data of the energy-consuming equipment is extracted according to the equipment manual of the energy-consuming equipment in the gypsum board production line, and the allowable range of the energy usage environment data is used as the data value range of the energy usage environment data in the second data information.

[0083] Determining the data value interval of the first data information within the data value interval of the second data information by using the information data association calculation model includes:

[0084] Performing full permutation and combination of the energy-consuming device data, the energy-consuming status data, and the energy-consuming environment data in the data value interval of the energy-consuming device data, the data value interval of the energy-consuming status data, and the data value interval of the energy-consuming environment data in the second data information;

[0085] Substituting all permutations and combinations of energy-consuming equipment data, energy-consuming status data, and energy-consuming environment data in the second data information into the information data association calculation model in sequence, and obtaining first data information corresponding to all permutations and combinations;

[0086] The first data information corresponding to all permutation combinations are arranged one by one within the matching interval to obtain the data value interval of the first data information.

[0087] Arranging the second data information in a corresponding manner within the data value interval of the second data information according to the sequence order of the descending sequence of the first data information to obtain a second data information trend sequence includes:

[0088] Extracting, in sequence, the energy-consuming device data corresponding to each sequence item in the descending sequence of the first data information from the data value interval of the energy-consuming device data in the second data information, and obtaining the energy-consuming device data trend sequence of the second data information based on the arrangement order of the sequence items in the descending sequence of the first data information, so as to characterize the energy-consuming device data trend corresponding to the energy consumption reduction trend;

[0089] In the data value interval of the energy usage status data of the second data information, the energy usage status data corresponding to each sequence item in the descending sequence of the first data information is sequentially extracted, and the energy usage status data corresponding to each sequence item in the descending sequence of the first data information is used to obtain an energy usage status data trend sequence of the second data information according to the arrangement order of the sequence items in the descending sequence of the first data information, so as to characterize the energy usage status data trend corresponding to the energy consumption reduction trend;

[0090] In the data value range of the energy consumption environment data of the second data information, the energy consumption environment data corresponding to each sequence item in the descending sequence of the first data information are extracted in sequence, and the energy consumption environment data corresponding to each sequence item in the descending sequence of the first data information are obtained according to the arrangement order of the sequence items in the descending sequence of the first data information to obtain the energy consumption status data trend sequence of the second data information, so as to characterize the energy consumption environment data trend corresponding to the energy consumption reduction trend.

[0091] The present invention realizes data accumulation for energy consumption optimization analysis through an information data association measurement model, and utilizes the correlation between the first data information and the second data information in the information data association measurement model, so as to obtain the first data information corresponding to the full permutation and combination of energy-consuming equipment data, energy-consuming status data and energy-consuming environment data in the second data information through modeling measurement. Therefore, in addition to realizing the predictive measurement effect of the information data association measurement model, it can also quickly complete the data accumulation for energy consumption optimization analysis, improve the efficiency of energy consumption optimization analysis, and the sufficient data sample size can ensure the accuracy of energy consumption optimization analysis.

[0092] Obtaining current second data information of the gypsum board production line, and using the information data association calculation model to obtain current first data information based on the current second data information, including:

[0093] Monitoring the energy-consuming equipment, energy-using status, and energy-using environment of the production line during a current time period to obtain energy-consuming equipment data, energy-using status data, and energy-using environment data for the current time period, and using the energy-consuming equipment data, energy-using status data, and energy-using environment data for the current time period as current second data information of the gypsum board production line;

[0094] The current second data information of the gypsum board production line is substituted into the information data association calculation model to obtain the current first data information of the gypsum board production line.

[0095] Determining an energy consumption optimization direction for reducing energy consumption of the production line based on the sequence position of the current first data information in the descending sequence of the first data information includes:

[0096] Map the current first data information to the first data information descending sequence, wherein,

[0097] When the current first data information is greater than the final value of the sequence in the descending sequence of the first data information, the second data information corresponding to the final value of the sequence in the descending sequence of the first data information is used as the current second data information in the second data information trend sequence to achieve an energy consumption optimization direction of minimizing the current energy consumption;

[0098] When the current first data information is equal to or less than the final value of the sequence in the descending sequence of the first data information, the current second data information is used as the current second data information to achieve the energy consumption optimization direction of maintaining the lowest current energy consumption.

[0099] The current first data information is greater than the final value of the sequence in the descending sequence of the first data information, indicating that there is still room for reduction in energy consumption. The current second data information can be adjusted toward the second data information corresponding to the final value of the sequence in the descending sequence of the first data information in the second data information trend sequence to achieve reduction in energy consumption.

[0100] The current first data information is equal to or less than the final value of the sequence in the descending sequence of the first data information, indicating that there is no room for reduction in energy consumption. The current second data information can be maintained to ensure that energy consumption is maintained at the lowest level, and the current first data information can be filled into the descending sequence of the first data information to achieve an update of the descending sequence of the first data information.

[0101] The current second data information realizes the energy consumption optimization direction of minimizing the current energy consumption, including:

[0102] The energy-consuming device data in the current second data information is directed toward the energy-consuming device data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as an energy consumption optimization direction for minimizing the current energy consumption based on the energy-consuming device data adjustment;

[0103] The energy consumption status data in the current second data information is adjusted toward the energy consumption status data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as the energy consumption optimization direction for minimizing the current energy consumption based on the energy consumption status data;

[0104] The energy consumption environment data in the current second data information is adjusted toward the energy consumption environment data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as an energy consumption optimization direction for minimizing the current energy consumption based on the energy consumption environment data;

[0105] The second data information currently realizes the energy consumption optimization direction of maintaining the lowest energy consumption, including:

[0106] Using the energy-consuming device data in the current second data information as an energy consumption optimization direction to adjust the current energy consumption to the lowest possible level based on the energy-consuming device data;

[0107] Using the energy consumption status data in the current second data information as an energy consumption optimization direction for adjusting the current energy consumption to maintain the lowest energy consumption based on the energy consumption status data;

[0108] The energy consumption environment data in the current second data information is used as an energy consumption optimization direction to adjust the current energy consumption to the lowest level based on the energy consumption environment data.

[0109] The present invention obtains the current first data information of the gypsum board production line based on the current second data information of the gypsum board production line through an information data association measurement model. Since the information data association measurement model can predictively obtain the first data information according to the second data information, that is, the current energy consumption is predictably obtained through the current energy-consuming equipment data, the current energy consumption status data and the current energy consumption environment data, and the energy consumption optimization direction of adjusting the energy-consuming equipment data, adjusting the energy consumption status data and adjusting the energy consumption environment data is finely analyzed through the predictive current energy consumption, so that the energy consumption optimization direction has a predictive advantage, and thus it can be achieved that the energy consumption optimization direction is refined according to the predictive current energy-consuming equipment data, the current energy consumption status data and the current energy consumption environment data.

[0110] As shown in FIG2 , the present invention provides a smart energy consumption management system that applies a multi-dimensional and refined smart energy consumption management method, including:

[0111] An information collection unit, used for collecting second data information of the gypsum board production line in real time;

[0112] An information storage unit storing an information data association calculation model for calculating energy consumption of a gypsum board production line;

[0113] An information calculation unit calculates the first data information in real time based on the second data information using an information data association calculation model, and determines an energy consumption optimization direction of the gypsum board production line based on the first data information;

[0114] an information display unit, configured to display the energy consumption of the gypsum board production line based on the first data information, and to display the energy consumption optimization direction of the gypsum board production line, wherein the first data information includes energy consumption data, and the second data information includes energy-consuming equipment data, energy consumption status data, and energy consumption environment data;

[0115] The information operation unit is communicatively connected with the information collection unit, the information storage unit and the information display unit respectively.

[0116] The present invention uses a machine learning algorithm to learn and fit the first data information and the second data information to obtain an information data association measurement model that characterizes the association relationship between the first data information and the second data information, so as to realize the measurement of the energy consumption of the gypsum board production line, realize the refined management of energy consumption in related links such as energy-consuming equipment, energy-consuming status, and energy-consuming environment, obtain the current second data information of the gypsum board production line, and use the information data association measurement model to obtain the current first data information based on the current second data information. Based on the sequence position of the current first data information in the descending sequence of the first data information, the energy consumption optimization direction for reducing the energy consumption of the production line is determined, thereby realizing information mining of energy consumption data and intuitively and visually displaying the energy consumption optimization direction mined from the energy consumption data.

[0117] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. A smart energy consumption management method based on multi-dimensional refinement, characterized by: The following steps are involved: Tracking and monitoring first data information of multiple groups of gypsum board production lines in the production schedule, and second data information having a time-series corresponding relationship with the first data information, wherein the first data information includes energy consumption data, and the second data information includes energy equipment data, energy status data, and energy environment data; Using a machine learning algorithm to learn and fit the first data information and the second data information to obtain an information data association calculation model that characterizes the association relationship between the first data information and the second data information, so as to realize the calculation of energy consumption of the gypsum board production line; Obtaining a data value interval of the second data information in the gypsum board production line, and determining a data value interval of the first data information in the data value interval of the second data information by using an information data association calculation model; Arranging the first data information in descending order in a data value interval of the first data information to obtain a first data information descending sequence for characterizing a trend of energy consumption reduction, and arranging the second data information in a data value interval of the second data information according to the sequence order of the first data information descending sequence to obtain a second data information trend sequence, wherein the second data information trend sequence is used to characterize an energy-consuming equipment data trend, an energy-consuming state data trend, and an energy-consuming environment data trend corresponding to the trend of energy consumption reduction in the second data information; The current second data information of the gypsum board production line is obtained, and the current first data information is obtained based on the current second data information using the information data association measurement model, and the energy consumption optimization direction for reducing the energy consumption of the production line is determined based on the sequence position of the current first data information in the descending sequence of the first data information.

2. According to claim 1, a multi-dimensional and refined intelligent energy consumption management method is characterized by: The acquisition of the first data information and the second data information includes: Monitoring energy consumption in each gypsum board production line in multiple time periods in turn to obtain energy consumption data, wherein the energy consumption includes at least one of the following: electricity consumption, water consumption, gas consumption, and compressed air consumption; Classify and store the energy consumption data according to time periods to obtain the first data information; The energy-consuming equipment, energy-using state and energy-using environment are monitored during the time period of the first data information to obtain energy-consuming equipment data, energy-using state data and energy-using environment data. The energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are averaged and denoised, and the denoised energy-consuming equipment data, energy-consuming status data and energy-consuming environment data are classified and stored according to time periods to obtain the second data information.

3. According to claim 2, a multi-dimensional and refined intelligent energy consumption management method is characterized by: The method of using a machine learning algorithm to learn and fit the first data information and the second data information to obtain an information data association calculation model for characterizing the association relationship between the first data information and the second data information includes: The second data information in the same time period is used as an input item of the CNN neural network, and the first data information in the same time period is used as an output item of the CNN neural network, and the CNN neural network is used to learn and train the input item and the output item of the CNN neural network to obtain the information data management and calculation model by fitting; The model expression of the information data management calculation model is: Data_1 t =CNN(Data_2 t ); Where Data_1 t is the first data information in time period t, Data_2 t is the second data information within time period t, CNN is a CNN neural network, and t is an identifier of the time period.

4. According to claim 1, a multi-dimensional and refined intelligent energy consumption management method is characterized by: The data value range of obtaining the second data information in the gypsum board production line includes: Extracting the allowable adjustment range of the energy-consuming equipment data according to the equipment manual of the energy-consuming equipment in the gypsum board production line, and using the allowable adjustment range of the energy-consuming equipment data as the data value interval of the energy-consuming equipment data in the second data information; The state data threshold of the energy state data of the energy-consuming equipment is extracted according to the production log of the gypsum board production line, and the state data threshold of the energy state data is used as the data value interval threshold of the energy state data, and the data value interval constructed by the data value interval threshold of the energy state data is used as the second The data value range of the energy consumption status data in the data information; The allowable range of the energy usage environment data of the energy usage equipment is extracted according to the equipment manual of the energy usage equipment in the gypsum board production line, and the allowable range of the energy usage environment data is used as the data value interval of the energy usage environment data in the second data information.

5. According to claim 3, a multi-dimensional and refined intelligent energy consumption management method is characterized by: The method of using the information data association calculation model to determine the data value interval of the first data information in the data value interval of the second data information includes: Fully permuting and combining the energy-consuming device data, the energy-using status data and the energy-using environment data in the data value interval of the energy-using device data, the data value interval of the energy-using status data and the data value interval of the energy-using environment data in the second data information; Substituting all permutations and combinations of energy-consuming equipment data, energy-consuming status data, and energy-consuming environment data in the second data information into the information data association calculation model in sequence, and obtaining first data information corresponding to all permutations and combinations one by one; The first data information corresponding to all permutation combinations are arranged one by one in the matching interval to obtain the data value interval of the first data information.

6. The method for intelligent energy consumption management based on multi-dimensional refinement according to claim 5 is characterized in that: The method of arranging the second data information in the data value interval of the second data information according to the sequence order of the descending sequence of the first data information to obtain the second data information trend sequence includes: In the data value interval of the energy-consuming device data of the second data information, the energy-consuming device data corresponding to each sequence item in the descending sequence of the first data information is extracted in sequence, and the energy-consuming device data corresponding to each sequence item in the descending sequence of the first data information is obtained according to the arrangement order of the sequence items in the descending sequence of the first data information to obtain the energy-consuming device data trend sequence of the second data information, so as to characterize the energy-consuming device data trend corresponding to the energy consumption reduction trend; In the data value interval of the energy usage status data of the second data information, the energy usage status data for each sequence item in the descending sequence of the first data information is sequentially extracted, and the energy usage status data for the corresponding Obtaining energy usage status data of each sequence item in the descending sequence of the first data information, and obtaining an energy usage status data trend sequence of the second data information according to the arrangement order of the sequence items in the descending sequence of the first data information, so as to characterize the energy usage status data trend corresponding to the energy consumption reduction trend; In the data value range of the energy environment data of the second data information, the energy environment data corresponding to each sequence item in the descending sequence of the first data information are extracted in turn, and the energy environment data corresponding to each sequence item in the descending sequence of the first data information are obtained according to the arrangement order of the sequence items in the descending sequence of the first data information to obtain the energy status data trend sequence of the second data information, so as to characterize the energy environment data trend corresponding to the trend of reducing energy consumption.

7. The intelligent energy consumption management method based on multi-dimensional refinement according to claim 3 is characterized by: The step of obtaining the current second data information of the gypsum board production line and obtaining the current first data information based on the current second data information by using the information data association calculation model includes: Monitor the energy-consuming equipment, energy-using state and energy-using environment of the production line in the current time period to obtain the energy-consuming equipment data, energy-using state data and energy-using environment data of the current time period, and use the energy-consuming equipment data, energy-using state data and energy-using environment data of the current time period as the current second data information of the gypsum board production line; Substitute the current second data information of the gypsum board production line into the information data association calculation model to obtain the current first data information of the gypsum board production line.

8. The intelligent energy consumption management method based on multi-dimensional refinement according to claim 6 is characterized by: The step of determining the energy consumption optimization direction for reducing the energy consumption of the production line based on the sequence position of the current first data information in the descending sequence of the first data information includes: Map the current first data information to the descending sequence of the first data information, where: When the current first data information is greater than the final value of the sequence in the descending sequence of the first data information, the second data information corresponding to the final value of the sequence in the descending sequence of the first data information is used as the energy consumption optimization direction of the current second data information to minimize the current energy consumption in the second data information trend sequence; When the current first data information is equal to or less than the sequence end value in the descending sequence of the first data information, the current second data information is used as the current second data information to achieve the current energy consumption to maintain the lowest energy consumption The optimization direction is optimized, and the current first data information is filled into the descending sequence of the first data information to realize the update of the descending sequence of the first data information.

9. The intelligent energy consumption management method based on multi-dimensional refinement according to claim 8 is characterized in that: The current second data information realizes the energy consumption optimization direction of minimizing the current energy consumption, including: The energy-consuming device data in the current second data information is directed toward the energy-consuming device data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the trend sequence of the second data information as the energy consumption optimization direction for minimizing the current energy consumption based on the energy-consuming device data adjustment; The energy consumption status data in the current second data information is directed toward the energy consumption status data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as the energy consumption optimization direction for adjusting the current energy consumption to the minimum based on the energy consumption status data; The energy environment data in the current second data information is directed toward the energy environment data in the second data information corresponding to the sequence end value in the descending sequence of the first data information in the second data information trend sequence as an energy consumption optimization direction for minimizing the current energy consumption based on the energy environment data adjustment; The current second data information realizes the energy consumption optimization direction of maintaining the lowest current energy consumption, including: The energy-consuming device data in the current second data information is used as an energy consumption optimization direction to adjust the current energy consumption to the minimum based on the energy-consuming device data; Using the energy consumption status data in the current second data information as an energy consumption optimization direction to adjust the current energy consumption to the minimum based on the energy consumption status data; The energy usage environment data in the current second data information is used as an energy consumption optimization direction to adjust the current energy consumption to the minimum based on the energy usage environment data.

10. A smart energy consumption management system using the multi-dimensional refined smart energy consumption management method according to any one of claims 1 to 9, characterized in that: include: An information collection unit, used for collecting second data information of the gypsum board production line in real time; An information storage unit storing an information data association calculation model for calculating the energy consumption of a gypsum board production line; An information calculation unit, which uses an information data association calculation model to calculate the first data information in real time based on the second data information, and determines the energy consumption optimization direction of the gypsum board production line based on the first data information; An information display unit, used to display the energy consumption of the gypsum board production line based on the first data information, and to display the energy consumption optimization direction of the gypsum board production line, wherein the first data information includes energy consumption data, and the second data information includes energy-consuming equipment data, energy-consuming status data, and energy-consuming environment data; The information operation unit is respectively connected to the information collection unit, the information storage unit and the information display unit for communication.

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