A digital energy management and control platform and an energy consumption management method

Abstract

The present invention relates to the technical field of data prediction, and particularly relates to a digital energy management and control platform and an energy consumption management method. The method obtains the predicted steam consumption of the current production process according to the steam consumption in the historical production process; in a production process, obtains the fuel consumption of the boiler according to the actual steam output of the boiler, the energy consumption of fuel for generating a unit of steam, and the thermal efficiency of the boiler; constructs an objective function according to the fuel consumption, start-stop operation cost, fuel unit price, fuel pollution discharge coefficient, pollutant treatment efficiency, and unit pollutant treatment cost; and obtains the fuel scheduling strategy of the boiler in the current production process according to the predicted steam consumption of the current production process and the objective function. By predicting the fuel scheduling strategy of the boiler in the current production process, the present invention enables reasonable distribution of fuel, avoids waste of resources, and effectively reduces the production cost of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a digital energy management and control platform and an energy consumption management method. Background Art

[0002] Energy is the core driving force of social development. It is a limited resource, so it needs to be reasonably allocated. For industrial enterprises, energy cost is an important part of business operations and is related to the profitability of the enterprise. Therefore, it is necessary to reasonably allocate and utilize energy under the premise of the lowest possible energy cost.

[0003] At present, in the manufacturing and production process of industrial enterprises, due to the backward method of collecting energy consumption data, the collection and analysis of energy consumption data is difficult to synchronize with the production process, which makes it difficult to adjust the production equipment and production plans in advance, resulting in unreasonable energy allocation and waste of resources; and the existing energy management and control platform cannot achieve the balance of enterprise production energy in real time, and cannot automatically achieve energy consumption benchmarking, which also leads to unreasonable energy allocation and waste of resources. Summary of the invention

[0004] In order to solve the technical problem of unreasonable energy distribution and waste of resources, the purpose of the present invention is to provide a digital energy management and control platform and an energy consumption management method. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a digital energy consumption management method, the method comprising the following steps:

[0006] According to the steam consumption during the process production start and stop period and the steam consumption during the process production stable operation period in the historical production process, the first steam consumption during the process production start and stop period and the second steam consumption during the process production stable operation period of the current production process are predicted to determine the predicted steam consumption of the current production process;

[0007] In a production process, the fuel consumption of each boiler is obtained based on the actual amount of steam generated by each boiler, the fuel consumption energy per unit of steam generated, and the thermal efficiency of the boiler; the energy consumption cost is obtained based on the fuel consumption of each boiler, the start-up and shutdown operation cost, and the unit price of fuel;

[0008] The environmental protection cost is obtained based on the fuel consumption, fuel emission coefficient, pollutant treatment efficiency and unit pollutant treatment cost of each boiler in a production process; among them, the fuel consumption, fuel emission coefficient and unit pollutant treatment cost are all positively correlated with the environmental protection cost, and the pollutant treatment efficiency is negatively correlated with the environmental protection cost;

[0009] The function formed by adding energy consumption cost and environmental protection cost is used as the objective function;

[0010] Obtain the fuel scheduling strategy of the boiler in the current production process according to the predicted steam consumption and the objective function of the current production process.

[0011] Furthermore, the method for obtaining the first steam consumption is as follows:

[0012] Take the process production start and stop time periods of the first preset number of historical production processes adjacent to the current production process as historical process production start and stop time periods;

[0013] Arrange the steam consumption of the historical process production start and stop time periods in the production order of the corresponding historical production processes from front to back to obtain the historical steam consumption sequence of the process production start and stop time periods;

[0014] According to the historical steam consumption sequence, construct a steam consumption prediction model for the process production start and stop time periods through the ARIMA model, and predict the first steam consumption of the process production start and stop time periods of the current production process.

[0015] Furthermore, the method for obtaining the second steam consumption is as follows:

[0016] Obtain a trend reference value according to the steam consumption of the process production stable operation time periods of the second preset number of historical production processes adjacent to the current production process;

[0017] Take the quarter in which the current production process is located as the target quarter, and obtain a seasonal reference value according to the steam consumption of the process production stable operation time periods in the third preset number of historical target quarters adjacent to the current production process;

[0018] Take the normalized result of the sum of the trend reference value and the seasonal reference value as the time factor characteristic value of the process production stable operation time period;

[0019] Take the time factor characteristic value, external environment factor characteristic value, and process factor characteristic value of the process production stable operation time period as the input of the long short-term memory network to predict the second steam consumption of the process production stable operation time period of the current production process.

[0020] Furthermore, the method for obtaining the trend reference value is as follows:

[0021] Construct the steam consumption of the process production stable operation time periods of the second preset number of historical production processes adjacent to the current production process into a row matrix as the trend matrix;

[0022] Obtain the mean value of all steam consumption in the trend matrix as the first reference value;

[0023] The result of negatively correlating and normalizing the difference between each steam consumption in the trend matrix and the first reference value is used as the first weight corresponding to the steam consumption.

[0024] Arrange the first weights vertically according to the order of the corresponding steam consumptions from left to right in the trend matrix to construct a column matrix, which is used as the trend weight matrix.

[0025] Use the product of the trend weight matrix and the trend matrix as the trend reference value.

[0026] The method for obtaining the seasonal reference value is as follows:

[0027] For any historical target quarter, obtain the sum of the steam consumptions during all stable operation periods of the process production in that historical target quarter as the target steam consumption of that historical target quarter.

[0028] Construct a row matrix with the target steam consumptions of the third preset number of historical target quarters adjacent to the current production process as the seasonal matrix.

[0029] Obtain the mean value of all target steam consumptions in the seasonal matrix as the second reference value.

[0030] The result of negatively correlating and normalizing the difference between each target steam consumption in the seasonal matrix and the second reference value is used as the second weight corresponding to the target steam consumption.

[0031] Arrange the second weights vertically according to the order of the corresponding target steam consumptions from left to right in the seasonal matrix to construct a column matrix, which is used as the seasonal weight matrix.

[0032] Use the product of the seasonal weight matrix and the seasonal matrix as the seasonal reference value.

[0033] Furthermore, the method for obtaining the fuel consumption is as follows:

[0034] Obtain the difference between the steam enthalpy and the feed water enthalpy as the enthalpy difference.

[0035] Use the ratio of the enthalpy difference to the fuel unit calorific value as the fuel consumption energy for generating unit steam.

[0036] For any boiler, use the ratio of the fuel consumption energy for generating unit steam to the thermal efficiency of the boiler as the actual fuel consumption energy for generating unit steam of the boiler.

[0037] Use the product of the actual steam output of the boiler and the actual fuel consumption energy for generating unit steam as the fuel consumption of the boiler.

[0038] Furthermore, the method for obtaining the energy consumption cost is as follows:

[0039] For any boiler, the product of the fuel consumption of the boiler and the unit price of the fuel corresponding to the fuel of the boiler is used as the first fuel cost of the boiler;

[0040] The sum of the first fuel cost and the start-stop operation cost of the boiler is used as the energy consumption reference cost of the boiler;

[0041] The relevant result of the energy consumption reference costs of all boilers in a production process is used as the energy consumption cost.

[0042] Furthermore, the method for obtaining the environmental protection cost is as follows:

[0043] The sum of the fuel consumption of each boiler in a production process is used as the overall fuel consumption;

[0044] The negative correlation result of the pollutant treatment efficiency is used as the first result, and the product of the overall fuel consumption, the fuel pollution discharge coefficient, and the first result is used as the pollutant discharge amount;

[0045] The product of the pollutant discharge amount and the unit treatment cost of the pollutant is used as the environmental protection cost.

[0046] Furthermore, the method for obtaining the fuel scheduling strategy of the boilers in the current production process according to the predicted steam consumption and the objective function of the current production process is as follows:

[0047] The constraint condition of the objective function is set as: the sum of the actual steam amounts generated by all boilers in the current production process is greater than or equal to the predicted steam consumption of the current production process;

[0048] The environmental protection cost is less than or equal to the preset maximum environmental protection cost;

[0049] On the premise of satisfying the constraint conditions, when the objective function is the smallest, the fuel corresponding to each boiler at this time is used as the fuel scheduling strategy of each boiler in the current production process.

[0050] Furthermore, the method for obtaining the predicted steam consumption is as follows:

[0051] The sum of the first steam consumption and the second steam consumption is used as the predicted steam consumption of the current production process.

[0052] In a second aspect, another embodiment of the present invention provides a digital energy management and control platform, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0053] The present invention has the following beneficial effects:

[0054] Based on the steam consumption in the historical production process, the present invention obtains the predicted steam consumption of the current production process, sets a reference amount for the steam consumption of the current production process, which is beneficial to the subsequent reasonable allocation of energy in the current production process; in order to reasonably analyze the fuel scheduling strategy of the boilers in the production process, avoid resource waste and reduce the production cost of the enterprise, and then in a production process, according to the actual steam output of each boiler, the fuel consumption energy per unit of steam production, and the thermal efficiency of the boiler, the fuel consumption of each boiler is obtained, accurately reflecting the energy consumption of each boiler; further, according to the fuel consumption of each boiler, the start-stop operation cost, the fuel unit price, the fuel pollution discharge coefficient, the pollutant treatment efficiency, and the unit pollutant treatment cost, an objective function is constructed to determine the method for obtaining the cost in the production process, which is beneficial to the subsequent reasonable allocation of the fuel scheduling strategy of the boilers in the production process; in order to avoid the situation of insufficient steam production in the predicted fuel scheduling strategy, and then according to the predicted steam consumption of the current production process and the objective function, the fuel scheduling strategy of the boilers in the current production process is accurately obtained, targeting the prediction and control of the fuel in the manufacturing production operation process of the enterprise, avoiding resource waste, and effectively reducing the production cost of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 Schematic flowchart of a digital energy consumption management method provided by an embodiment of the present invention;

[0057] Figure 2 Flowchart of a method for obtaining predicted steam consumption provided by an embodiment of the present invention;

[0058] Figure 3 Flowchart of a method for obtaining a second steam consumption provided by an embodiment of the present invention;

[0059] Figure 4 Structural diagram of a digital energy consumption management system provided by an embodiment of the present invention;

[0060] Figure 5 Schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a digital energy management and control platform and an energy consumption management method proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0063] The following specifically describes the specific solutions of a digital energy management and control platform and an energy consumption management method provided by the present invention with reference to the accompanying drawings.

[0064] Embodiment 1:

[0065] The present invention proposes a digital energy consumption management method. Please refer to Figure 1 , which shows a schematic flowchart of a digital energy consumption management method provided by an embodiment of the present invention. The method includes the following steps:

[0066] Step S1: Based on the steam consumption during the start-stop period of the process production and the steam consumption during the stable operation period of the process production in the historical production process, predict the first steam consumption during the start-stop period of the process production and the second steam consumption during the stable operation period of the process production in the current production process, and then determine the predicted steam consumption of the current production process.

[0067] The digital energy and carbon management center for industrial enterprises and parks is established based on technologies such as intelligent algorithms, industrial Internet, and Internet of Things to meet the energy consumption and carbon emission management needs of industrial enterprises and parks. It is an information system with functions of energy consumption and carbon emission data collection, analysis, prediction, early warning, and management. Its core goal is to save energy, reduce carbon emissions, and increase efficiency and reduce costs for industrial enterprise parks. According to the requirements of relevant industry standards, the system architecture of the energy and carbon management center for industrial enterprises and parks is designed, which includes six major modules: infrastructure layer, data collection layer, data architecture layer, model component layer, business application layer, and interactive display layer.

[0068] Among them, the infrastructure layer includes: Industrial enterprises and parks can provide a stable and secure operating environment for the energy and carbon management center through their own servers or cloud servers. The entire operating environment includes servers, storage, networks, operating systems, and databases, ensuring that the energy and carbon management center responds quickly and has characteristics such as maintainability and scalability.

[0069] The data acquisition layer includes: The energy and carbon management center can complete the acquisition and upload of relevant data such as energy consumption and production and operation through acquisition methods such as docking with the existing database, manual entry, and on-site measurement by sensors. And the data is cleaned to remove the interference of redundant data, making the subsequent analysis and processing of the data more concise and efficient.

[0070] The data architecture layer includes: storing various types of data through the original data architecture such as enterprise and park industrial Internet platforms, or by building basic databases, acquisition databases, business databases, statistical databases, etc. Among them, data from different sources has different data types. For example, business data, equipment operation parameters, etc. belong to relational data; historical energy consumption data, etc. belong to time-series data; real-time energy consumption trend data, etc. belong to streaming data.

[0071] The model component layer includes models such as the enterprise carbon emission accounting model, industrial energy efficiency benchmarking model, and energy consumption prediction and early warning model. Among them, the enterprise carbon emission accounting model realizes the accounting and analysis of the enterprise's carbon emissions according to the enterprise application scenario and various requirements; the industrial energy efficiency benchmarking model realizes the energy efficiency benchmarking of the enterprise's main products and equipment according to the energy consumption limit standards stipulated by relevant requirements; the energy consumption prediction and early warning model accurately predicts the different energy consumption amounts at different stages in the enterprise production process based on historical data, combines the prediction results with the production plan, and issues an alarm when the actual energy consumption exceeds the planned energy consumption.

[0072] The business application layer includes: The business functions of industrial enterprises include energy and carbon query, energy consumption structure analysis, energy consumption benchmarking, energy prediction and early warning, energy efficiency optimization and balance, etc. The energy and carbon management center of the industrial park provides digital support and public service capabilities such as energy consumption and carbon emission management for the settled enterprises, and at the same time, conducts diversified analysis from different dimensions such as the park, region, building, and enterprises within the park according to the actual situation of the park.

[0073] The interactive display layer: Based on the actual needs of industrial enterprises and parks, provides access entrances for industrial enterprises and parks through large screens, PC terminals or mobile terminals, and constructs the visualization capabilities of relevant data and businesses.

[0074] Therefore, through the energy and carbon management platform, it is possible to automatically collect and analyze the energy consumption data of enterprises in the production process, and generate a production plan that meets the expected interests of the enterprise and the specific working conditions of each energy-consuming equipment and production capacity equipment based on the energy consumption early warning module and the energy efficiency balance module, that is, complete the complete process of collecting - diagnosing - solving the enterprise's energy consumption data. When enterprise-related personnel use the energy and carbon management platform, they can intuitively see the energy consumption early warning situation in the enterprise production, the energy consumption diagnosis report of the current production process, and the corresponding solution, that is, the regulation plan for production equipment and production plan.

[0075] This embodiment takes a large manufacturing enterprise as an example for analysis and describes the core modules in the energy and carbon management platform. This manufacturing enterprise includes three major workshops, namely the manufacturing workshop, the wrapping workshop, and the logistics storage area. Among them, the manufacturing workshop is divided into a production area and a storage cabinet area, mainly used for material production; the wrapping workshop is mainly used for product production and packaging; the logistics storage area is mainly used for semi-finished product storage, finished product storage, auxiliary material storage, finished product inbound and outbound, raw material turnover, etc. The energy consumption of the three major workshops is mainly energy such as natural gas, fuel oil, and electricity. Among them, natural gas and fuel oil support the operation of boiler equipment, and the steam generated by the boiler is used for the operation of the central air conditioner and process production; the operation of the central air conditioner equipment relies on electricity and steam, and its function is to regulate the material storage environment and process production environment; process production requires steam, electricity, compressed air, and soft water to manufacture products.

[0076] In order to accurately analyze the energy consumption situation in the production process, sensors are set on different equipment and units in this embodiment to collect data in various production processes in each workshop and production line in real time, such as data in various production processes such as steam consumption, steam generation, electricity consumption, and temperature data. It should be noted that in this embodiment, each production process is a complete and uninterrupted production process.

[0077] For a manufacturing enterprise, the direct purpose of optimizing energy consumption management is to reduce costs. In the production process, steam is the main production power, and steam is generated by burning fuel in a boiler. Therefore, this embodiment analyzes the steam consumption in the production process and the actual steam generation amount of the boiler, and then reasonably allocates the fuel burned by the boiler that generates steam to reduce the production cost of the manufacturing enterprise. In order to conduct better analysis, this embodiment constructs a steam intelligent prediction model and a steam production balance optimization model. Among them, the steam intelligent prediction model predicts the steam consumption in different stages of the production process to support the calculation of the subsequent energy scheduling model; the goal of the steam production balance optimization model is to conduct fuel scheduling for equipment such as boilers that produce steam on the premise of meeting energy consumption benchmarking and production consumption, so as to minimize the fuel cost and environmental protection cost of the enterprise's steam production.

[0078] In the production process, the consumption of process steam has strong periodicity. The periods of steam consumption are the start-stop period of process production and the stable operation period of process production. In order to reasonably set the fuel scheduling of the boiler in the production process, this embodiment first constructs a steam intelligent prediction model. Therefore, according to the steam consumption in the start-stop period of process production and the steam consumption in the stable operation period of process production in the historical production process, the first steam consumption in the start-stop period of the current production process and the second steam consumption in the stable operation period of the current production process are predicted, and then the predicted steam consumption of the current production process is determined, which is beneficial to the subsequent reasonable fuel scheduling of the boiler.

[0079] Preferably, in an implementable manner of this embodiment, for the method of obtaining the predicted steam consumption, please refer to Figure 2 , which shows a flowchart of a method for obtaining the predicted steam consumption provided in this embodiment. The method includes the following steps:

[0080] Step S201: Predict the first steam consumption during the process production start-stop period of the current production process.

[0081] The process production start-stop period is affected by many factors such as the real-time state of equipment and manual operations. The steam consumption during the process production start-stop period cannot be quantitatively analyzed by a mechanism model. Usually, the market demand for the products manufactured by manufacturing enterprises has a certain regularity. Therefore, in this embodiment, based on the historical steam consumption during the process production start-stop period, the first steam consumption during the process production start-stop period of the current production process is predicted.

[0082] Preferably, in an implementable manner of this embodiment, the method for obtaining the first steam consumption is as follows: The process production start-stop periods of the first preset number of historical production processes adjacent to the current production process are all used as historical process production start-stop periods; in this embodiment, the first preset number is set to 50, and the implementer can set the size of the first preset number according to the actual situation, which is not limited herein. If the number of historical production processes before the current production process is less than 50, only the process production start-stop periods of the existing historical production processes are analyzed. Arrange the steam consumption during the historical process production start-stop periods in the production order of the corresponding historical production processes from front to back to obtain a historical steam consumption sequence of the process production start-stop periods; according to the historical steam consumption sequence, construct a steam consumption prediction model for the process production start-stop periods through an autoregressive integrated moving average (ARIMA) model to predict the first steam consumption during the process production start-stop period of the current production process. Among them, the autoregressive integrated moving average (ARIMA) model is a well-known technology and will not be elaborated here.

[0083] The ARIMA model is a statistical model used for time series analysis. The ARIMA model is used to describe the relationships between data points in a time series for prediction or understanding of the data. The ARIMA model consists of three main components: autoregressive (AR), differencing (I), and moving average (MA). Among them, the first part, autoregression, refers to the relationship between the current value of a variable and its past values. Mathematically, it is a regression model where the target variable and the predictive variable for regression are both the steam consumption during the process start-stop periods of the historical steam consumption series in different production processes. The second part, differencing, means subtracting consecutive data points in the historical steam consumption series to stabilize the time series. The third part, moving average, focuses on the dependence relationships among the error terms of the historical steam consumption series. The formula for the steam consumption prediction model during the process start-stop periods can be expressed as: ; where, is the steam consumption during the process start-stop periods; t is the production order of the production process corresponding to the process start-stop periods; to are the autoregressive coefficients; to are the moving average coefficients; is the error term; is a constant term, which can be set by the implementer according to the actual situation and is not limited here; p is the number of autoregressive terms in the AR model; q is the number of moving average terms in the MA model; d is the number of differencing terms.

[0084] Furthermore, the specific prediction method for the steam consumption during the process start-stop periods is as follows: First, obtain the steam consumption during the historical process start-stop periods as the original series of the ARIMA model (p, d, q); then use the autocorrelation plot and partial autocorrelation plot to determine the orders of AR and MA (p and q); and then determine the parameters and of the ARIMA model through maximum likelihood estimation; finally, use the steam consumption prediction model during the process start-stop periods to predict the steam consumption during the process start-stop periods of the current production process, and predict the first steam consumption during the process start-stop periods of the current production process.

[0085] Step S202: Predict the second steam consumption during the stable operation period of the process of the current production process.

[0086] The steam consumption during the stable operation period of the process production is jointly affected by external environmental factors, process factors, and time factors. Among them, external environmental factors include workshop environmental temperature, humidity, air moisture, etc.; process factors include process parameters, production scheduling plans, etc.; time factors are manifested as the influence of the steam consumption during the historical process production stable operation period. Among them, process factors and external environmental factors are determined. The core of predicting the steam consumption during the stable operation period of the process production lies in the time factor with the steam consumption during the historical process production stable operation period as a reference. It is known that the production process has obvious trends and seasons. Therefore, in this embodiment, the time factor is divided into a trend mode and a season mode.

[0087] In order to analyze the trend mode of the steam consumption during the stable operation period of the process production, this embodiment analyzes the steam consumption during the stable operation period of the process production of the historical production processes adjacent to the current production process, and obtains the trend change of the steam consumption during the stable operation period of the process production, which is beneficial to accurately predict the steam consumption during the stable operation period of the current production process.

[0088] In order to analyze the seasonal mode of the steam consumption during the stable operation period of the process production, this embodiment analyzes the steam consumption during the stable operation period of the process production in a preset number of historical quarters in the same quarter as the current quarter, and obtains the seasonal change of the steam consumption during the stable operation period of the process production, and then more accurately predicts the steam consumption during the stable operation period of the current production process. Therefore, this embodiment obtains the time factor characteristic value of the stable operation period of the process production according to the steam consumption during the stable operation period of the process production in the historical production process, and then predicts the second steam consumption during the stable operation period of the current production process based on the time factor characteristic value, external environmental factor characteristic value, and process factor characteristic value of the stable operation period of the process production.

[0089] Preferably, in an implementable manner of this embodiment, for the method of obtaining the second steam consumption, please refer to Figure 3 which shows a flowchart of a method for obtaining the second steam consumption provided by this embodiment. The method includes the following steps:

[0090] Step S301: Obtain a trend reference value according to the steam consumption during the stable operation period of the process production of the second preset number of historical production processes adjacent to the current production process.

[0091] The trend reference value reflects the degree to which the steam consumption during the stable operation period of the process production is affected by the time trend. The larger the trend reference value, the greater the degree to which the steam consumption during the stable operation period of the process production is affected by the time factor.

[0092] Among them, the method for obtaining the trend reference value is as follows: The steam consumption during the stable operation period of the process production of the second preset number of historical production processes adjacent to the current production process is constructed into a row matrix as the trend matrix. In this embodiment, the second preset number is set to 30, and the implementer can set the size of the second preset number according to the actual situation, which is not limited here. Obtain the average value of all steam consumptions in the trend matrix as the first reference value; The result of making the difference between each steam consumption in the trend matrix and the first reference value negatively correlated and normalized is used as the first weight corresponding to the steam consumption. The larger the first weight, the more reasonable the corresponding steam consumption. In this embodiment, by performing negative correlation and normalization processing on the absolute value of the difference between each steam consumption in the trend matrix and the first reference value, where exp is the exponential function with the natural constant as the base; is the first unknown, which refers to the absolute value of the difference between each steam consumption in the trend matrix and the first reference value in this embodiment. Arrange the first weights vertically in the order from left to right of the corresponding steam consumption in the trend matrix to construct a column matrix as the trend weight matrix; The product of the trend weight matrix and the trend matrix is used as the trend reference value.

[0093] Step S302: Take the quarter in which the current production process is located as the target quarter, and obtain the seasonal reference value according to the steam consumption during the stable operation period of the process production in the third preset number of historical target quarters adjacent to the current production process.

[0094] The seasonal reference value reflects the degree to which the steam consumption during the stable operation period of the process production is affected by the quarterly trend. The larger the seasonal reference value, the greater the degree to which the steam consumption during the stable operation period of the process production is affected by time factors.

[0095] Among them, the method for obtaining the seasonal reference value is as follows: For any historical target quarter, obtain the sum of the steam consumptions during all stable operation periods of the process production in this historical target quarter as the target steam consumption of this historical target quarter; The target steam consumptions of the third preset number of historical target quarters adjacent to the current production process are constructed into a row matrix as the season matrix. In this embodiment, the third preset number is set to 10, and the implementer can set the size of the third preset number according to the actual situation, which is not limited here. Obtain the average value of all target steam consumptions in the season matrix as the second reference value; The result of making the difference between each target steam consumption in the season matrix and the second reference value negatively correlated and normalized is used as the second weight corresponding to the target steam consumption. The larger the second weight, the more reasonable the corresponding target steam consumption. In this embodiment, by For each absolute value of the difference between the target steam consumption in the season matrix and the second reference value, perform a negatively correlated and normalized process, where exp is the exponential function with the natural constant as the base; is the second unknown, which in this embodiment refers to the absolute value of the difference between each target steam consumption in the season matrix and the second reference value. Arrange the second weights vertically according to the order of the corresponding target steam consumption from left to right in the season matrix to construct a column matrix as the season weight matrix; take the product of the season weight matrix and the season matrix as the season reference value.

[0096] Step S303: Take the normalized result of the sum of the trend reference value and the season reference value as the time factor eigenvalue for the stable operation period of the process production.

[0097] In this embodiment, the sigmoid function is used to normalize the sum of the trend reference value and the season reference value. Among them, the larger the time factor eigenvalue, the stronger the correlation between the time factor and the prediction of steam consumption.

[0098] Step S304: Take the time factor eigenvalue, external environment factor eigenvalue, and process factor eigenvalue of the stable operation period of the process production as the input of the long short-term memory network to predict the second steam consumption of the stable operation period of the current production process.

[0099] It is known that the external environment factor eigenvalue and the process factor eigenvalue can be directly obtained during the stable operation period of the process production. Furthermore, the time factor eigenvalue, external environment factor eigenvalue, and process factor eigenvalue are fused to predict the steam consumption during the stable operation period of the process production. In this embodiment, the long short-term memory network is used to predict the steam consumption during the stable operation period of the process production, that is, the time factor eigenvalue, external environment factor eigenvalue, and process factor eigenvalue of the stable operation period of the process production are used as the input of the long short-term memory network. Since there are the above three factor eigenvalues in the prediction process of steam consumption, which is a non-linear problem, therefore, in this embodiment, the hyperbolic tangent function is used to non-linearly fuse the time factor eigenvalue, external environment factor eigenvalue, and process factor eigenvalue. Among them, the activation function of the long short-term memory network is: ; In the formula, is the predicted steam consumption during the stable operation period of the process production, is the time factor eigenvalue, is the external environment factor eigenvalue, is the process factor eigenvalue; is the hyperbolic tangent function. Among them, the long short-term memory network is a well-known technology and will not be elaborated here.

[0100] Thus, the second steam consumption during the stable operation period of the current production process is predicted.

[0101] Step S203: Obtain the predicted steam consumption of the current production process.

[0102] It is known that a complete production process includes a process start-stop period and a process stable operation period. Therefore, in this embodiment, the sum of the first steam consumption and the second steam consumption is used as the predicted steam consumption of the current production process.

[0103] Step S2: In a production process, obtain the fuel consumption of each boiler according to the actual steam output of each boiler, the fuel consumption energy per unit of steam production, and the thermal efficiency of the boiler; obtain the energy consumption cost according to the fuel consumption of each boiler, the start-stop operation cost, and the unit price of the fuel.

[0104] For steam production, the costs can be divided into two aspects: energy consumption cost and environmental protection cost. The energy consumption cost of boiler steam production includes the combustion cost of fuel and the start-stop operation cost of the boiler; the environmental protection cost refers to the environmental protection fees paid for the pollutant emissions generated by steam production. In order to analyze the energy consumption cost of boiler steam production in a production process, this embodiment constructs a method for obtaining the energy consumption cost. Thus, in a production process, according to the actual steam output of each boiler, the fuel consumption energy per unit of steam production, and the thermal efficiency of the boiler, obtain the fuel consumption of each boiler. It should be noted that the fuels in different boilers can be different, but the fuel in each boiler is a single type of fuel. Then, obtain the energy consumption cost according to the fuel consumption of each boiler, the start-stop operation cost, and the unit price of the fuel.

[0105] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the fuel consumption is as follows: Obtain the difference between the steam enthalpy and the feed water enthalpy as the enthalpy difference; take the ratio of the enthalpy difference to the fuel unit calorific value as the fuel consumption energy per unit of steam production; considering the thermal efficiency of the boiler, for any boiler, take the ratio of the fuel consumption energy per unit of steam production to the thermal efficiency of the boiler as the actual fuel consumption energy per unit of steam production of the boiler; then, take the product of the actual steam output of the boiler and the actual fuel consumption energy per unit of steam production as the fuel consumption of the boiler. It should be noted that the steam enthalpy, feed water enthalpy, fuel unit calorific value, and thermal efficiency of the boiler are all known data and can be directly obtained for use. Thus, the fuel consumption of each boiler in the production process is obtained.

[0106] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the energy consumption cost is as follows: for any boiler, the product of the fuel consumption of the boiler and the unit price of the fuel corresponding to the boiler is used as the first fuel cost of the boiler; among them, the unit price of each type of fuel is known and can be directly used. The known energy consumption cost also includes the start-up and shutdown operation cost of the boiler. Among them, the start-up and shutdown operation cost of the boiler can be directly obtained through experience. Then, the sum of the first fuel cost and the start-up and shutdown operation cost of the boiler is used as the energy consumption reference cost of the boiler; in order to determine the energy consumption cost of the entire production process, the relevant results of the energy consumption reference costs of all boilers in a production process are used as the energy consumption cost. The greater the energy consumption cost, the more energy is consumed in the corresponding production process.

[0107] Step S3: Obtain the environmental protection cost according to the fuel consumption, fuel pollution discharge coefficient, pollutant treatment efficiency, and unit pollutant treatment cost of each boiler in a production process.

[0108] It is known that pollutants are emitted during the process of a boiler burning fuel to generate steam. Therefore, in this embodiment, the environmental protection cost is obtained according to the fuel consumption, fuel pollution discharge coefficient, pollutant treatment efficiency, and unit pollutant treatment cost of each boiler in a production process.

[0109] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the environmental protection cost is as follows: the sum of the fuel consumption of each boiler in a production process is used as the overall fuel consumption to determine the total fuel consumption corresponding to a production process; in this embodiment, it is set that the fuel pollution discharge coefficients of all types of fuel are the same. Then, according to the product of the overall fuel consumption and the fuel pollution discharge coefficient, the pollutant emission amount can be determined. Considering that a manufacturing enterprise has a pollutant treatment system to treat the pollutants generated by the fuel during the production process, in this embodiment, the negative correlation result of the pollutant treatment efficiency is used as the first result. Then, the product of the overall fuel consumption, the fuel pollution discharge coefficient, and the first result is used as the pollutant emission amount; among them, the calculation formula for the pollutant emission amount is: ; where P is the pollutant emission amount; F is the overall fuel consumption; is the fuel pollution discharge coefficient; is the pollutant treatment efficiency; is the first result. It should be noted that both the fuel pollution discharge coefficient and the pollutant treatment efficiency are known data and can be directly used. Then, the product of the pollutant emission amount and the unit pollutant treatment cost is used as the environmental protection cost. Thus, the environmental protection cost of the production process is obtained. It should be noted that the standards for the unit pollutant treatment cost vary in different regions, and the implementer can set it according to the actual situation and is not limited here.

[0110] Step S4: Use the function formed by adding the energy consumption cost and the environmental protection cost as the objective function.

[0111] The goal of steam production optimization strategy scheduling is to minimize the energy consumption cost and the environmental protection cost as much as possible by regulating the operating parameters of the boiler units under the condition of meeting the steam supply. Therefore, the goal of obtaining the objective function in this embodiment is to construct a mathematical model. Under the condition of meeting the steam supply in a production process, the more reasonable the fuel corresponding to each boiler is when the corresponding objective function is smaller.

[0112] Step S5: Obtain the fuel scheduling strategy of the boilers in the current production process according to the predicted steam consumption and the objective function of the current production process.

[0113] In order to analyze the fuel scheduling strategy of the boilers in the current production process to make the production cost of the current production process lower and at the same time meet the overall steam consumption, this embodiment obtains the fuel scheduling strategy of the boilers in the current production process according to the predicted steam consumption and the objective function of the current production process.

[0114] In order to ensure the reasonableness of the fuel scheduling strategy of the boilers in the current production process, this embodiment sets the constraint conditions of the objective function as follows: (1) The total steam output actually generated by all boilers in the current production process is greater than or equal to the predicted steam consumption of the current production process; to achieve energy consumption benchmarking; (2) The environmental protection cost is less than or equal to the preset maximum environmental protection cost; to ensure that the environmental protection cost is within a controllable range. Among them, the preset maximum environmental protection cost can be set according to the actual situation of the manufacturing enterprise and will not be limited here.

[0115] This embodiment predicts the steam consumption in the current production process based on the steam consumption in the historical production process to construct a steam intelligent prediction model; with the goal of ensuring energy consumption benchmarking and environmental protection cost control, a steam production balance optimization model is constructed. By solving the objective function of the steam production balance optimization model, the fuel scheduling strategy of the boiler units that minimizes the total cost under the premise of ensuring production requirements can be obtained, that is, the control schemes of each boiler unit.

[0116] Before finally implementing the fuel scheduling strategy, in order to further ensure the reasonableness of the fuel scheduling strategy, it is also necessary for expert consultants in related fields to conduct diagnostic analysis on the obtained fuel scheduling strategy. Specifically, by analyzing the above-mentioned steam consumption prediction and steam production, it can be found that except for the prediction data, the rest of the data and parameters are all determined quantities. Therefore, this embodiment conducts a credibility test on the fuel scheduling strategy, and the specific method is as follows:

[0117] The process of predicting steam consumption is packaged as a scheduling node, and a fourth preset number of scheduling nodes is constructed under the premise of satisfying the constraints of the objective function; in this embodiment, the fourth preset number is set to 60, and the implementer can set the size of the fourth preset number according to the actual situation, which is not limited here. Since the offset of the predicted data of the scheduling node will be different, the secondary node is constructed to perform consistency check on the predicted steam consumption corresponding to the scheduling node to ensure the feasibility of the steam production balance optimization model. Furthermore, in this embodiment, according to the predicted steam consumption corresponding to each scheduling node, the scheduling node is divided by a hierarchical clustering algorithm to obtain the node category. Among them, the hierarchical clustering algorithm is a well-known technology and will not be repeated here. According to the predicted steam consumption of each scheduling node in each node category, the inter-class variance between all node categories is obtained. When the inter-class variance is smaller, the steam production balance optimization model is more reasonable. Then, in this embodiment, the preset inter-class variance threshold is set to 0.3, and the implementer can set the size of the preset inter-class variance threshold according to the actual situation, which is not limited here.

[0118] When the inter-class variance is less than the preset inter-class variance threshold, the steam production balance optimization model is determined to be reasonable, and then the variance of the predicted steam consumption of all scheduling nodes in each node category is obtained as the first variance; the fuel scheduling strategy corresponding to the scheduling node corresponding to the center point of the node category corresponding to the minimum first variance is used as the final fuel scheduling strategy. It should be noted that if there are multiple node categories corresponding to the minimum first variance, the fuel scheduling strategy corresponding to the scheduling node corresponding to the center point of any node category is selected as the final fuel scheduling strategy.

[0119] It should be noted that the clustering parameters and other data in the above consistency test are selected by experts based on actual needs in order to achieve diagnostic results with clear goals.

[0120] At this point, fuel consumption has been rationally managed, resource waste has been avoided, and the production costs of manufacturing companies have been effectively reduced.

[0121] In summary, this embodiment obtains the predicted steam consumption of the current production process based on the steam consumption in the historical production process; obtains the fuel consumption of the boiler in a production process based on the actual amount of steam generated by the boiler and the fuel consumption energy for generating unit steam, as well as the thermal efficiency of the boiler; constructs an objective function based on fuel consumption, start-stop operation cost, fuel unit price, fuel emission coefficient, pollutant treatment efficiency and pollutant unit treatment cost; obtains the fuel scheduling strategy of the boiler in the current production process based on the predicted steam consumption of the current production process and the objective function. The present invention predicts the fuel scheduling strategy of the boiler in the current production process, so that the fuel is reasonably allocated, avoiding waste of resources while effectively reducing the production cost of the enterprise.

[0122] Example 2:

[0123] The present invention also provides a digital energy management and control platform, which includes a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute a digital energy consumption management method provided by an embodiment of the present application. The device may specifically be a chip, a component or a module. The chip may include a connected processor and a memory. Among them, the memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a digital energy consumption management method provided by the above embodiment.

[0124] Example 3:

[0125] The present invention also provides a digital energy consumption management system. Please refer to Figure 4 , which shows a structural diagram of a digital energy consumption management system provided by an embodiment of the present invention. The system includes: a predicted steam consumption acquisition module 10, an energy consumption cost acquisition module 20, an environmental protection cost acquisition module 30, an objective function acquisition module 40, and a fuel scheduling strategy acquisition module 50.

[0126] The predicted steam consumption acquisition module 10 is used to predict the first steam consumption during the process start-stop period and the second steam consumption during the stable operation period of the current production process based on the steam consumption during the process start-stop period and the steam consumption during the stable operation period of the process in the historical production process, and then determine the predicted steam consumption of the current production process.

[0127] The energy consumption cost acquisition module 20 is used to obtain the fuel consumption of each boiler according to the actual steam output of each boiler, the fuel consumption energy per unit of steam generated, and the thermal efficiency of the boiler in a production process; and obtain the energy consumption cost according to the fuel consumption of each boiler, the start-stop operation cost, and the fuel unit price.

[0128] The environmental protection cost acquisition module 30 is used to obtain the environmental protection cost according to the fuel consumption of each boiler, the fuel pollution discharge coefficient, the pollutant treatment efficiency, and the unit pollutant treatment cost in a production process. Among them, the fuel consumption, the fuel pollution discharge coefficient, and the unit pollutant treatment cost are all positively correlated with the environmental protection cost, and the pollutant treatment efficiency is negatively correlated with the environmental protection cost.

[0129] The objective function acquisition module 40 is used to take the function formed by adding the energy consumption cost and the environmental protection cost as the objective function.

[0130] The fuel scheduling strategy acquisition module 50 is used to obtain the fuel scheduling strategy of the boiler in the current production process according to the predicted steam consumption of the current production process and the objective function.

[0131] It should be noted that: For the system provided in the above embodiments, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a digital energy consumption management system and an embodiment of a digital energy consumption management method provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0132] Embodiment 4:

[0133] The present invention also provides a computer device. Please refer to Figure 5 , the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. Among them, when the processor 402 executes the computer program 403, the computer device can execute any one of the digital energy consumption management methods introduced above.

[0134] Embodiment 5:

[0135] This embodiment also provides a computer-readable storage medium. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer, the computer is enabled to execute the above-related method steps to implement a digital energy consumption management method provided in the above embodiments.

[0136] Embodiment 6:

[0137] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is enabled to execute the above-related steps to implement a digital energy consumption management method provided in the above embodiments.

[0138] Among them, the device, computer-readable storage medium, computer program product, or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above and will not be elaborated here.

[0139] It should be noted that: The above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

Claims

1. A digital energy consumption management method, characterized in that, The method includes the following steps: Predict the first steam consumption during the start-stop period of the process production and the second steam consumption during the stable operation period of the process production in the current production process based on the steam consumption during the start-stop period of the process production and the steam consumption during the stable operation period of the process production in the historical production process, and then determine the predicted steam consumption of the current production process; In a production process, obtain the fuel consumption of each boiler based on the actual steam production of each boiler, the fuel consumption energy for generating unit steam, and the thermal efficiency of the boiler; obtain the energy consumption cost based on the fuel consumption of each boiler, the start-stop operation cost, and the unit price of the fuel; Obtain the environmental protection cost based on the fuel consumption, fuel pollution discharge coefficient, pollutant treatment efficiency, and unit pollutant treatment cost of each boiler in a production process; among them, the fuel consumption, fuel pollution discharge coefficient, and unit pollutant treatment cost are all positively correlated with the environmental protection cost, and the pollutant treatment efficiency is negatively correlated with the environmental protection cost; Take the function formed by adding the energy consumption cost and the environmental protection cost as the objective function; Obtain the fuel scheduling strategy of the boiler in the current production process according to the predicted steam consumption of the current production process and the objective function; The method for obtaining the second steam consumption is as follows: Construct a row matrix of the steam consumption during the stable operation period of the process production of the second preset number of historical production processes adjacent to the current production process as a trend matrix; Obtain the mean value of all steam consumptions in the trend matrix as the first reference value; Take the result of negatively correlating and normalizing the difference between each steam consumption in the trend matrix and the first reference value as the first weight corresponding to the steam consumption; Vertically arrange the first weights in the order from left to right according to the corresponding steam consumption in the trend matrix to construct a column matrix as the trend weight matrix; Take the product of the trend weight matrix and the trend matrix as the trend reference value; Take the quarter in which the current production process is located as the target quarter, and construct a row matrix of the target steam consumption of the third preset number of historical target quarters adjacent to the current production process as a season matrix; among them, for any historical target quarter, obtain the sum of the steam consumptions during all stable operation periods of the process production in the historical target quarter as the target steam consumption of the historical target quarter; Obtain the mean value of all target steam consumptions in the season matrix as the second reference value; Take the result of negatively correlating and normalizing the difference between each target steam consumption in the season matrix and the second reference value as the second weight corresponding to the target steam consumption; Vertically arrange the second weights in the order from left to right according to the corresponding target steam consumption in the season matrix to construct a column matrix as the season weight matrix; Take the product of the season weight matrix and the season matrix as the season reference value; Take the result of normalizing the sum of the trend reference value and the season reference value as the time factor eigenvalue during the stable operation period of the process production; Take the time factor eigenvalue, external environment factor eigenvalue, and process factor eigenvalue during the stable operation period of the process production as the input of the long short-term memory network to predict the second steam consumption during the stable operation period of the process production in the current production process.

2. The digital energy consumption management method according to claim 1, wherein The method for obtaining the first steam consumption is as follows: Take the process production start-stop periods of the first preset number of historical production processes adjacent to the current production process as the historical process production start-stop periods; Arrange the steam consumption during the historical process production start-stop periods in the production order of the corresponding historical production processes from front to back to obtain the historical steam consumption sequence during the process production start-stop periods; According to the historical steam consumption sequence, construct a steam consumption prediction model for the process production start-stop periods through the ARIMA model to predict the first steam consumption during the process production start-stop periods of the current production process.

3. The digital energy consumption management method according to claim 1, characterized in that The method for obtaining the fuel consumption is as follows: Obtain the difference between the steam enthalpy and the feed water enthalpy as the enthalpy difference; Take the ratio of the enthalpy difference to the fuel unit calorific value as the fuel consumption energy for generating unit steam; For any boiler, take the ratio of the fuel consumption energy for generating unit steam to the thermal efficiency of the boiler as the actual fuel consumption energy for generating unit steam of the boiler; Take the product of the actual steam output of the boiler and the actual fuel consumption energy for generating unit steam as the fuel consumption of the boiler.

4. The digital energy consumption management method according to claim 1, characterized in that The method for obtaining the energy consumption cost is as follows: For any boiler, take the product of the fuel consumption of the boiler and the fuel unit price corresponding to the fuel of the boiler as the first fuel cost of the boiler; Take the sum of the first fuel cost and the start-stop operation cost of the boiler as the energy consumption reference cost of the boiler; Take the relevant result of the energy consumption reference costs of all boilers in a production process as the energy consumption cost.

5. A digital energy consumption management method according to claim 1, characterized in that, The method for obtaining the environmental protection cost is as follows: Take the sum of the fuel consumption of each boiler in a production process as the overall fuel consumption; Take the negative correlation result of the pollutant treatment efficiency as the first result, and take the product of the overall fuel consumption, the fuel pollutant discharge coefficient, and the first result as the pollutant discharge amount; Take the product of the pollutant discharge amount and the unit pollutant treatment cost as the environmental protection cost.

6. The digital energy consumption management method according to claim 1, characterized in that The method for obtaining the fuel scheduling strategy of the boilers in the current production process according to the predicted steam consumption and the objective function of the current production process is as follows: Set the constraint conditions of the objective function as: the sum of the actual steam outputs of all boilers in the current production process is greater than or equal to the predicted steam consumption of the current production process; The environmental protection cost is less than or equal to the preset maximum environmental protection cost; When the objective function is minimized under the premise of satisfying the constraint conditions, take the fuel corresponding to each boiler at this time as the fuel scheduling strategy of each boiler in the current production process.

7. A digital energy consumption management method according to claim 1, characterized in that, The method for obtaining the predicted steam consumption is as follows: Take the sum of the first steam consumption and the second steam consumption as the predicted steam consumption of the current production process.

8. A digital energy management and control platform, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the digital energy consumption management method described in any one of claims 1-7 above.

Images