Industrial internet green energy management system

Abstract

The invention belongs to the technical field of energy management, and particularly relates to an industrial internet green energy management system, which comprises a production information acquisition and analysis module used for receiving production task information sent by an industrial plant area and performing one-way variable analysis on the production yield, the production period and the production requirements; the energy and loss overall planning and analyzing module is used for collecting and overall planning and analyzing energy consumption information and loss information of production equipment in the production process; and a production plan model comparison module. According to the method, the energy consumption can be reduced while the service quality attribute is met, the method is green, economical and environment-friendly, multiple groups of production plan models are constructed through the obtained labor cost statistics, equipment loss statistics, time-phased power utilization planning, illumination regulation and control power utilization analysis and temperature regulation and control power utilization analysis, and the production plan efficiency is improved. And selecting an optimal production plan module in the constructed production plan model.

Description

Technical Field

[0001] The present invention relates to a green energy management system, and more particularly to an industrial Internet green energy management system. Background Art

[0002] Industrial Internet is a result of the integration of the global industrial system with advanced computing, analysis, sensing technologies, and Internet connectivity. The essence and core of the Industrial Internet is to closely connect and integrate devices, production lines, factories, suppliers, products, and customers through an industrial Internet platform. As a product of the integration of the global industrial system with advanced computing, analysis, sensing technologies, and Internet connectivity, the Industrial Internet is increasingly becoming the key support for the new industrial revolution and an important cornerstone for deepening "Internet + advanced manufacturing". For the production and manufacturing of some industrial products, there are often large quantities of production information. During the initial production stage of an industrial plant area, corresponding production plans are formulated to ensure that the production plan can be completed within the specified period.

[0003] However, with the proposal of the concept of green energy, simply adopting synchronous production by multiple people in multiple workshops can improve the production rate to a certain extent, but the optimal production plan cannot be obtained. Secondly, during the production process, only considering the energy consumption of production equipment is not comprehensive. In the workshop, other electrical equipment such as lighting fixtures and air conditioners are also needed, but the existing related energy management systems have not studied and analyzed other electrical consumption. In view of this, the present invention designs an industrial Internet green energy management system. Summary of the Invention

[0004] The main purpose of the present disclosure is to provide an industrial Internet green energy management system to effectively solve the problems raised by the inventor in the above background art.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An industrial Internet green energy management system, comprising: a production information acquisition and analysis module, which is used to receive the production task information sent by the industrial plant area and perform unidirectional variable analysis on the production output, production period, and production requirements; an energy and loss overall analysis module, which is used to collect and overall analyze the energy consumption information and the loss information of production equipment during the production process; a production plan model comparison module, which formulates different production plan models for comparison based on the collected energy consumption information and equipment loss information to obtain the optimal production plan model; a production plan determination and implementation module, which transmits the obtained optimal production plan model to each corresponding production workshop through the intranet of the industrial plant area and executes the production task according to the production plan.

[0007] Among them, the energy and loss overall analysis module includes manual cost statistics, equipment loss statistics, time - segmented power consumption planning, green energy acquisition analysis, lighting control power consumption analysis, and temperature control power consumption analysis. The manual cost statistics is a stepped statistics of the manual cost required for simultaneous production from the workshop with the least usage to the workshop with the most usage on the premise that the production output and production period are determined. The equipment loss statistics is a stepped statistics of the equipment operation - caused equipment loss required for simultaneous production from the workshop with the least usage to the workshop with the most usage on the premise that the production output and production period are determined. The time - segmented power consumption planning is a stepped statistics of the power consumption during the production process of different shifts in the expected production period. The green energy acquisition analysis is a statistical analysis of the daily green energy power generation in the expected production period. The lighting control power consumption analysis is a statistical analysis of the daily lighting power consumption in the expected production period. The temperature control power consumption analysis is a statistical analysis of the daily temperature control power consumption in the expected production period;

[0008] The production plan model comparison module summarizes the statistical data information of each part of the energy and loss overall analysis module, and constructs multiple groups of production plan models by using the method of single - variable control analysis;

[0009] An acquisition unit, adapted to acquire a service request of a user; a combination determination unit, adapted to acquire a service combination case with the service request from a preset service combination case library, and use the service combination of the service combination case as the optimal service combination for executing the service request;

[0010] When an execution case with the service request cannot be obtained from the service combination case library, determine the optimal service combination for executing the service request based on the service quality attribute and energy consumption attribute of the service request;

[0011] An execution unit, adapted to execute the service request by using the determined optimal service combination.

[0012] Preferably, the combination determination unit is adapted to construct the most - economical control objective function based on the service quality obtained per unit energy consumption;

[0013] Taking response time, cost, and reliability as indicators, construct the service quality objective function of the service request; taking energy cost, energy utilization rate, and pollution cost as indicators, construct the overall energy consumption objective function of the service request;

[0014] Substitute the constructed service quality objective function and overall energy consumption objective function of the service request into the most - economical control objective function to obtain a multi - objective evaluation function;

[0015] The multi-objective dragonfly algorithm is used to perform iterative calculations on the multi-objective evaluation function to obtain the corresponding optimal solution, which is used as the best service combination for executing the service request.

[0016] Preferably, the combination determination unit is adapted to train the energy cost weight coefficient, energy utilization rate weight coefficient, and pollution cost weight coefficient of each sub-service in the service combination corresponding to the current dragonfly position by using the BP neural network in machine learning.

[0017] Preferably, the model construction of the production plan model comparison module specifically includes the following steps:

[0018] Step 1: Add the values obtained from the manual cost statistics, equipment loss statistics, power consumption planning by time period, lighting control power consumption analysis, and temperature control power consumption analysis in sequence, taking a certain part of them as variables. The sum is the total energy consumption of each group of plan models. A values are obtained from the manual cost statistics, B values are obtained from the equipment loss statistics, C values are obtained from the power consumption planning by time period, D values are obtained from the lighting control power consumption analysis, and E values are obtained from the temperature control power consumption analysis. That is, A*B*C*D*E values are obtained after the statistical addition.

[0019] Step 2: Subtract the F values obtained from the green energy acquisition analysis from the total energy consumption of each group of plan models in sequence, that is, obtain the actual energy consumption of A*B*C*D*E*F plan models.

[0020] Step 3: Arrange the actual energy consumption of each model in a stepped manner, and substitute the production plans of each model into the production volume and production period in sequence, and select the planning model that can meet the production volume and production period.

[0021] Step 4: Select the group with the smallest value of the production planning model remaining after the secondary screening, which is the optimal production model.

[0022] Preferably, the green energy acquisition analysis of the energy and loss overall analysis module includes the acquisition of electric energy from solar power generation, the acquisition of electric energy from wind power generation, and the acquisition of electric energy from water power generation.

[0023] Preferably, the lighting control power consumption analysis of the energy and loss overall analysis module sets light sensors at multiple positions in each production workshop to adjust the lighting fixtures with different brightness or turn on different numbers of lighting fixtures in different parts according to the intensity of the light, so as to obtain the required power consumption.

[0024] Preferably, the temperature control power consumption analysis of the energy and loss overall analysis module sets temperature sensors at multiple positions in each production workshop to turn on different parts or different numbers of air conditioners for temperature adjustment according to the real-time temperature in the workshop, so as to obtain the required power consumption.

[0025] Preferably, the green energy acquisition analysis, lighting control power consumption analysis, and temperature control power consumption analysis of the energy and loss overall analysis module are all based on the temperature and sunlight intensity of future weather and meteorological information for simulation and statistical analysis.

[0026] In view of this, compared with the prior art, the beneficial effects of the present invention are:

[0027] (1). In this application, when an execution case with the service request cannot be obtained from the execution case library, based on the service quality attribute and energy consumption attribute of the service request, the best service combination for executing the service request is determined, and the service quality attribute and energy consumption attribute are used as the basis for determining the service combination for executing the service request, rather than simply pursuing the service quality attribute. Therefore, while meeting the service quality attribute, energy consumption can be reduced, which is green, economical, and environmentally friendly.

[0028] (2). In this application, multiple production plan models are constructed through the obtained labor cost statistics, equipment loss statistics, time-of-use power consumption planning, lighting control power consumption analysis, and temperature control power consumption analysis. In addition to the energy consumption of equipment power during the production process, the energy consumption of other electrical equipment such as lighting fixtures and air conditioners in the workshop is also statistically analyzed to select the optimal production plan module from the constructed production plan models. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure shows the overall structural schematic diagram of the industrial Internet green energy management system provided by the present invention;

[0030] Figure 2 The figure shows the structural schematic diagram of the energy and loss overall analysis module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to Figure 1-2 , the present invention provides the following embodiments:

[0033] An industrial Internet green energy management system includes:

[0034] A production information acquisition and analysis module, which is used to receive the production task information sent by the industrial plant area, and perform unidirectional variable analysis on the production output, production period, and production requirements; an energy and loss overall analysis module, which is used to collect and overall analyze the energy consumption information and equipment loss information during the production process; a production plan model comparison module, which formulates different production plan models for comparison based on the collected energy consumption information and equipment loss information to obtain the optimal production plan model; a production plan determination and implementation module, which transmits the obtained optimal production plan model to the corresponding production workshops through the intranet of the industrial plant area and executes the production tasks according to the production plan.

[0035] Among them, the energy and loss overall analysis module includes labor cost statistics, equipment loss statistics, time-of-use power consumption planning, green energy acquisition analysis, lighting control power consumption analysis, and temperature control power consumption analysis. Labor cost statistics is a step-by-step statistics of the labor costs required for simultaneous production from the workshop with the least use to the workshop with the most use on the premise that the production output and production period are determined. Equipment loss statistics is a step-by-step statistics of the equipment operation losses caused by simultaneous production from the workshop with the least use to the workshop with the most use on the premise that the production output and production period are determined. Time-of-use power consumption planning is a step-by-step statistics of the power consumption during different shift productions of the expected production period. Green energy acquisition analysis is a statistical analysis of the daily green energy power generation of the expected production period. Lighting control power consumption analysis is a statistical analysis of the daily lighting power consumption of the expected production period. Temperature control power consumption analysis is a statistical analysis of the daily temperature control power consumption of the expected production period.

[0036] The production plan model comparison module summarizes the data information of each part of the energy and loss overall analysis module and constructs multiple groups of production plan models by using the method of single-variable control analysis.

[0037] An acquisition unit, suitable for acquiring the service request of the user; a combination determination unit, suitable for acquiring the service combination case with the service request from the preset service combination case library and taking the service combination of the service combination case as the best service combination for executing the service request.

[0038] When the execution case with the service request cannot be obtained from the service combination case library, determine the best service combination for executing the service request based on the service quality attribute and energy consumption attribute of the service request.

[0039] An execution unit, suitable for executing the service request by using the determined best service combination.

[0040] Specifically, the combination determination unit is suitable for constructing the most economical control objective function based on the service quality obtained per unit of energy consumption.

[0041] Construct a service quality objective function for service requests with response time, cost, and reliability as indicators; construct an overall energy consumption objective function for service requests with energy cost, energy utilization rate, and pollution cost as indicators;

[0042] Substitute the constructed service quality objective function and overall energy consumption objective function of the service request into the most economical control objective function to obtain a multi-objective evaluation function;

[0043] Use the multi-objective dragonfly algorithm to perform iterative calculations on the multi-objective evaluation function to obtain the corresponding optimal solution, which is used as the best service combination for executing the service request.

[0044] Specifically, the combination determination unit is suitable for training the energy cost weight coefficient, energy utilization rate weight coefficient, and pollution cost weight coefficient of each sub-service in the service combination corresponding to the current dragonfly position by using the BP neural network in machine learning.

[0045] Specifically, the construction of the production plan model comparison module specifically includes the following steps:

[0046] Step 1: Add the values obtained from the manual cost statistics, equipment loss statistics, time-of-use power consumption planning, lighting control power consumption analysis, and temperature control power consumption analysis in sequence with a certain part of them as variables. The sum is the total energy consumption of each group of plan models. A values are obtained from the manual cost statistics, B values are obtained from the equipment loss statistics, C values are obtained from the time-of-use power consumption planning, D values are obtained from the lighting control power consumption analysis, and E values are obtained from the temperature control power consumption analysis. That is, A*B*C*D*E values are obtained after the statistical addition;

[0047] Step 2: Subtract the F values obtained from the green energy acquisition analysis from the total energy consumption of each group of plan models in sequence to obtain the actual energy consumption of A*B*C*D*E*F plan models;

[0048] Step 3: Arrange the actual energy consumption of each model in a stepped manner, and substitute the production plans of each model into the production volume and production period in sequence, and select the plan model that can meet the production volume and production period;

[0049] Step 4: Select the group with the smallest value of the production plan model remaining after the secondary screening, which is the optimal production model.

[0050] Specifically, the green energy acquisition analysis of the energy and loss overall analysis module includes the acquisition of electrical energy from solar power generation, the acquisition of electrical energy from wind power generation, and the acquisition of electrical energy from hydropower generation.

[0051] Specifically, the lighting control power consumption analysis of the energy and loss overall analysis module sets light sensors at multiple positions in each production workshop to adjust lighting fixtures with different brightness levels or turn on different numbers of lighting fixtures in different parts according to the intensity of light, so as to obtain the required power consumption.

[0052] Specifically, the temperature control power consumption analysis of the energy and loss overall analysis module sets temperature sensors at multiple positions in each production workshop to adjust the temperature by turning on air conditioners in different parts or with different numbers according to the real-time temperature in the workshop, so as to obtain the required power consumption.

[0053] Specifically, the green energy acquisition analysis, lighting control power consumption analysis, and temperature control power consumption analysis of the energy and loss overall analysis module are all based on the temperature and sunshine intensity of future weather meteorological information for simulation and statistical analysis.

[0054] The specific implementation manner of this embodiment is as follows: multiple production plan models are constructed through the obtained labor cost statistics, equipment loss statistics, time-of-use power consumption planning, lighting control power consumption analysis, and temperature control power consumption analysis, and the optimal production plan module is selected from the constructed production plan models.

[0055] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0056] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation manners described. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An industrial Internet green energy management system, characterized in that, Including: A production information acquisition and analysis module, which is used to receive the production task information sent by the industrial plant area, and perform unidirectional variable analysis on the production output, production deadline, and production requirements; An energy and loss overall analysis module, which is used to collect and overall analyze the energy consumption information and equipment loss information during the production process; A production plan model comparison module, which formulates different production plan models for comparison based on the collected energy consumption information and equipment loss information to obtain the optimal production plan model; A production plan determination and implementation module, which transmits the obtained optimal production plan model to each corresponding production workshop through the intranet of the industrial plant area and implements the production task according to the production plan; Among them, the energy and loss overall analysis module includes labor cost statistics, equipment loss statistics, time-of-use electricity planning, green energy acquisition analysis, lighting control electricity consumption analysis, and temperature control electricity consumption analysis. The labor cost statistics are a stepped statistics of the labor costs required for simultaneous production from the workshop with the least usage to the workshop with the most usage on the premise that the production output and production deadline are determined. The equipment loss statistics are a stepped statistics of the equipment operation losses caused by simultaneous production from the workshop with the least usage to the workshop with the most usage on the premise that the production output and production deadline are determined. The time-of-use electricity planning is a stepped statistics of the electricity consumption during different shifts of the expected production deadline. The green energy acquisition analysis is a statistical analysis of the daily green energy power generation during the expected production deadline. The lighting control electricity consumption analysis is a statistical analysis of the daily lighting power consumption during the expected production deadline. The temperature control electricity consumption analysis is a statistical analysis of the daily temperature control power consumption during the expected production deadline; The production plan model comparison module summarizes the data information of each part of the energy and loss overall analysis module and constructs multiple groups of production plan models by using the method of single variable control analysis; An acquisition unit, adapted to acquire a service request of a user; A combination determination unit, adapted to acquire a service combination case with the service request from a preset service combination case library, and use the service combination of the service combination case as the best service combination for executing the service request; When a execution case with the service request cannot be acquired from the service combination case library, determine the best service combination for executing the service request based on the service quality attribute and energy consumption attribute of the service request; An execution unit, adapted to execute the service request by using the determined best service combination; 2. An industrial Internet green energy management system according to claim 1, characterized in that: The combination determination unit is adapted to construct the most economical control objective function based on the service quality obtained per unit energy consumption; Construct the service quality objective function of the service request with response time, cost, and reliability as indicators; Construct the total energy consumption objective function of the service request with energy cost, energy utilization rate, and pollution cost as indicators; Substitute the constructed service quality objective function and total energy consumption objective function of the service request into the most economical control objective function to obtain a multi-objective evaluation function; The multi-objective dragonfly algorithm is used to perform iterative calculations on the multi-objective evaluation function to obtain the corresponding optimal solution, which is used as the best service combination for executing the service request.

3. An industrial Internet green energy management system according to claim 2, characterized in that: The combination determination unit is adapted to train the energy cost weight coefficient, energy utilization weight coefficient, and pollution cost weight coefficient of each sub-service in the service combination corresponding to the current dragonfly position by using the BP neural network in machine learning.

4. An industrial Internet green energy management system according to claim 1, characterized in that: The model construction of the production plan model comparison module specifically includes the following steps: Step 1: The values obtained from the manual cost statistics, equipment loss statistics, time-of-use power consumption planning, lighting control power consumption analysis, and temperature control power consumption analysis are added in sequence with a certain part of them as variables. The total energy consumption of each group of plan models is obtained. A values are obtained from the manual cost statistics, B values are obtained from the equipment loss statistics, C values are obtained from the time-of-use power consumption planning, D values are obtained from the lighting control power consumption analysis, and E values are obtained from the temperature control power consumption analysis. That is, A*B*C*D*E values are obtained after statistical addition. Step 2: The F values obtained from the green energy acquisition analysis are subtracted from the total energy consumption of each group of plan models in sequence, and the actual energy consumption of A*B*C*D*E*F plan models is obtained. Step 3: The actual energy consumption of each model is arranged in a stepped manner, and the production plans of each model are substituted into the production volume and production period in sequence. The plan model that can meet the production volume and production period is selected. Step 4: Select the group with the smallest value among the production plan model values remaining after the secondary screening, which is the optimal production model.

5. The industrial Internet green energy management system according to claim 1, wherein: The green energy acquisition analysis of the energy and loss overall analysis module includes the acquisition of electric energy from solar power generation, the acquisition of electric energy from wind power generation, and the acquisition of electric energy from hydropower generation.

6. The industrial Internet green energy management system according to claim 1, wherein: The lighting control power consumption analysis of the energy and loss overall analysis module sets light sensors at multiple positions in each production workshop to adjust the lighting fixtures with different brightness or turn on different numbers of lighting fixtures in different parts according to the light intensity, so as to obtain the required power consumption.

7. An industrial Internet green energy management system according to claim 1, characterized in that: The temperature control power consumption analysis of the energy and loss overall analysis module sets temperature sensors at multiple positions in each production workshop to turn on different parts or different numbers of air conditioners for temperature adjustment according to the real-time temperature in the workshop, so as to obtain the required power consumption.

8. An industrial Internet green energy management system according to claim 1, characterized in that: The green energy acquisition analysis, lighting control power consumption analysis, and temperature control power consumption analysis of the energy and loss overall analysis module are all simulated statistical analyses based on the temperature and sunshine intensity of future weather meteorological information.

Images