Method and device for controlling production equipment of aggregator

Abstract

The invention provides a production equipment control method and device of an aggregator, and relates to the technical field of control. The method comprises the following steps: evaluating the carbon emission of an aggregator in each group to obtain a carbon reduction score of each aggregator in each group; the future emission reduction potential of each aggregator is predicted according to the carbon emission data change value of each aggregator, and the future emission reduction potential score of each aggregator is obtained; and performing carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determining a target aggregator to be optimized according to a carbon performance rating result, and performing energy consumption reduction control on target production equipment of the target aggregator. The device executes the method. According to the method and the device for the aggregator, provided by the embodiment of the invention, the production equipment of the aggregator can be accurately and specifically selected for control, so that energy conservation and emission reduction are realized.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a method and device for controlling production equipment of a polymerizer. Background Art

[0002] Aggregators can specifically be energy aggregators under the management of a certain group. In order to achieve the goal of energy conservation and emission reduction, it is necessary to control the operating status of the aggregator's production equipment.

[0003] The existing method of controlling the production equipment of aggregators is mainly for each aggregator to independently control the operating status of the production equipment. Although the group can also remotely control the production equipment of the aggregators, the aggregators under the group are of different sizes. Aggregators of different sizes have large differences in resources, operational capabilities, etc. It is difficult to accurately reflect their actual carbon performance level by using fixed numerical evaluation, and thus it is impossible to accurately and specifically select the production equipment of the aggregators for control and adjustment. Summary of the invention

[0004] In view of the problems in the prior art, an embodiment of the present invention provides a production equipment control method and device for an aggregator, which can at least partially solve the problems in the prior art.

[0005] In one aspect, the present invention provides a production equipment control method of an aggregator, comprising:

[0006] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0007] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0008] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0009] Each group is obtained according to the scale of carbon emissions of the aggregator determined based on the threshold; accordingly, the database providing corresponding carbon emission index data is allocated to each group, including:

[0010] A first database providing emission reduction rates for the first group of aggregators with a smaller carbon footprint;

[0011] A second database of emission reduction rates and reductions is provided for allocation to a second group of aggregators with an appropriate scale of carbon emissions;

[0012] A third database is provided to allocate emission reductions to third groups with larger carbon emissions than aggregators.

[0013] The carbon emissions of the aggregators in each group are evaluated to obtain the carbon reduction score of each aggregator in each group, including:

[0014] Sorting the aggregators in the first group in descending order of the emission reduction rates, and dividing the aggregators in the first group into a preset number of levels according to the sorting sequence and using a quantile method;

[0015] Determining the carbon reduction scores of the aggregators in the first group according to the preset carbon reduction scores corresponding to each level;

[0016] Sorting the aggregators in the second group in descending order of the emission reduction rates, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method;

[0017] Determining the emission reduction rate and carbon reduction scores of the aggregators in the second group according to the preset emission reduction rate and carbon reduction scores corresponding to each level;

[0018] Sorting the aggregators in the second group in descending order of the emission reduction, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method;

[0019] Determining the emission reduction and carbon reduction scores of the aggregators in the second group according to the preset emission reduction and carbon reduction scores corresponding to each level;

[0020] Calculate the carbon reduction scores of the aggregators in the second group based on the carbon reduction score of the emission reduction rate and its weight and the carbon reduction score of the emission reduction amount and its weight;

[0021] Sorting the aggregators in the third group in descending order of the emission reduction, and dividing the aggregators in the third group into a preset number of levels according to the sorting sequence and using a quantile method;

[0022] The carbon reduction scores of the aggregators in the third group are determined according to the preset carbon reduction scores corresponding to each level.

[0023] The method of predicting the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator includes:

[0024] Based on the preset carbon emission reduction trend prediction model, the carbon emission data change value of each aggregator is processed to obtain the carbon emission reduction trend of each aggregator;

[0025] Wherein, the preset carbon emission reduction trend prediction model is a pre-trained neural network model;

[0026] Rank each aggregator in descending order according to the slope reflecting the carbon emission reduction trend of each aggregator, and divide each aggregator into a preset number of levels according to the ranking sequence and by using the quantile method;

[0027] The future emission reduction potential score of each aggregator is determined based on the preset future emission reduction potential score corresponding to each level.

[0028] Wherein, the production equipment control method of the aggregator further includes:

[0029] The weights and biases in the neural network model are optimized by a particle swarm optimization method to optimize the preset carbon emission reduction trend prediction model.

[0030] The step of optimizing the weights and biases in the neural network model by using a particle swarm optimization method includes:

[0031] Taking each particle as a set of weights and biases of the neural network model, taking the mean square error of the neural network model as the fitness function, and obtaining a velocity update formula and a position update formula;

[0032] The speed of the particle is updated according to the speed update formula, and the position of the particle is updated according to the position update formula, until the respective corresponding maximum number of iterations is reached, and the neural network model at this time is used as the optimized preset carbon emission reduction trend prediction model.

[0033] The step of controlling the target production equipment of the target aggregator to reduce energy consumption includes:

[0034] Monitoring energy consumption data of production equipment of the target aggregator;

[0035] Determine the production equipment corresponding to the production equipment energy consumption data greater than the preset energy consumption threshold as the target production equipment;

[0036] Sending a remote control instruction to control the operating state of the target production equipment, wherein the remote control instruction carries an energy consumption reduction control strategy;

[0037] The energy consumption reduction control strategy includes one or more of reducing the power output of the device, adjusting the operating time of the device, and shutting down the preset device.

[0038] In one aspect, the present invention provides a production equipment control device of an aggregator, comprising:

[0039] an allocation unit, configured to group the aggregators according to their historical carbon emission data, and allocate a database providing corresponding carbon emission indicator data to each group;

[0040] A prediction unit is used to select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0041] A control unit is used to rate the carbon performance of each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating results, and control the target production equipment of the target aggregator to reduce energy consumption.

[0042] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:

[0043] The processor and the memory communicate with each other via the bus;

[0044] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:

[0045] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0046] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0047] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0048] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:

[0049] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:

[0050] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0051] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0052] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0053] The production equipment control method and device for an aggregator provided in an embodiment of the present invention group the aggregators according to their historical carbon emission data, and allocate a database providing corresponding carbon emission index data to each group; select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain a carbon reduction score for each aggregator in each group; and predict the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtain a future emission reduction potential score for each aggregator; perform a carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating result, and control the target production equipment of the target aggregator to reduce energy consumption, so as to accurately and specifically select the production equipment of the aggregator for control, thereby achieving energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0055] Figure 1 It is a flow chart of a production equipment control method of an aggregator provided in one embodiment of the present invention.

[0056] Figure 2 It is a structural schematic diagram of a production equipment control device of an aggregator provided in one embodiment of the present invention.

[0057] Figure 3A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0059] Figure 1 FIG. 1 is a flow chart of a production equipment control method of an aggregator provided by an embodiment of the present invention. Figure 1 As shown, the production equipment control method of the aggregator provided by the embodiment of the present invention includes:

[0060] Step S1: grouping the aggregators according to their historical carbon emission data, and assigning a database providing corresponding carbon emission index data to each group.

[0061] Step S2: Select corresponding carbon emission indicator data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator.

[0062] Step S3: Carry out carbon performance rating for each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating results, and perform energy consumption reduction control on the target production equipment of the target aggregator.

[0063] In the above step S1, the device groups the aggregators according to the historical carbon emission data of the aggregators, and allocates a database providing corresponding carbon emission index data to each group. The device may be a computer device for executing the method, etc. The historical carbon emission data may be obtained on an annual basis, and the grouping of the aggregators according to the historical carbon emission data of the aggregators includes:

[0064] The total historical carbon emissions of the aggregator in the previous year C0 is obtained, and based on the first carbon emission threshold C1 and the second carbon emission threshold C2, the total historical carbon emissions of each aggregator in the previous year is determined:

[0065] When C0<C1, it means that the carbon emission scale of the aggregator is relatively small, and these aggregators are classified into the first group;

[0066] When C1≤C0≤C2, it means that the carbon emission scale of the relevant aggregators is normal, and these aggregators are divided into the second group;

[0067] When C0>C2, it means that the carbon emissions of the relevant aggregators are too large, and these aggregators are classified into the third group.

[0068] Each group is obtained by dividing the carbon emission scale of the aggregator based on the threshold; accordingly, each group is allocated a database providing corresponding carbon emission index data, including:

[0069] A first database providing emission reduction rates for the first group of aggregators with a smaller carbon footprint;

[0070] A second database of emission reduction rates and reductions is provided for allocation to a second group of aggregators with an appropriate scale of carbon emissions;

[0071] A third database is provided to allocate emission reductions to third groups with larger carbon emissions than aggregators.

[0072] Databases are established through MySQL, including an emission reduction rate database, a comprehensive database (including emission reduction rate and emission reduction amount) and an emission reduction amount database. According to the judgment results, aggregators with smaller carbon emission scales are summarized into the emission reduction rate database, aggregators with moderate carbon emission scales are summarized into the comprehensive database, and aggregators with larger carbon emission scales are summarized into the emission reduction amount database.

[0073] It should be noted that the emission reduction rate database will store data related to aggregators with smaller carbon emission scales, the comprehensive database will store data related to aggregators with medium carbon emission scales, and the emission reduction database will store data related to aggregators with larger carbon emission scales. At the same time, each database will establish files based on the corporate information of the aggregators. The files include the company name, establishment time, company location, etc., and different aggregators are summarized based on different scales to avoid one-sided single rules for carbon performance assessment of aggregators. The carbon emission thresholds C1 and C2 are set based on the carbon emission rules within the industry, or are set through empirical methods in combination with experts in related fields.

[0074] In the above step S2, the device selects corresponding carbon emission index data from each database, evaluates the carbon emissions of the aggregators in each group, and obtains the carbon reduction score of each aggregator in each group; and predicts the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtains the future emission reduction potential score of each aggregator. Carbon emissions can be evaluated as a quarterly cycle, and the carbon emission values ​​C of the aggregators in the current quarter are collected. a And the carbon emissions value C in the previous quarter b , respectively calculate the carbon emission reduction rate C减排率 and emission reduction C 减排量

[0075] C can be calculated according to the following formula 减排率 :

[0076]

[0077] C can be calculated according to the following formula 减排量 :

[0078] C 减排量 =C b -C a

[0079] It should be noted that based on the aggregator’s current quarterly carbon emissions value C a And the carbon emissions value C in the previous quarter b , calculate the carbon emission reduction rate and carbon emission reduction amount respectively, and evaluate the carbon emission reduction rate and carbon emission reduction amount of the aggregator in the current quarter according to the carbon emission scale of different aggregators according to different rules. Aggregators with a smaller carbon emission scale focus on improving the reduction rate, aggregators with a moderate carbon emission scale comprehensively evaluate the reduction rate and emission reduction amount, and aggregators with a larger carbon emission scale focus on the actual reduction in emission reduction amount.

[0080] The carbon emissions of the aggregators in each group are evaluated to obtain the carbon reduction score of each aggregator in each group, including:

[0081] The aggregators in the first group are sorted in descending order of the emission reduction rate, and the aggregators in the first group are divided into a preset number of levels according to the sorting sequence and by the quantile method; the preset number can be set independently according to the actual situation and can be selected as 10.

[0082] Determine the carbon reduction score of the aggregators in the first group according to the preset carbon reduction score corresponding to each level; 减排率 Sort the emission reduction rates from high to low and classify the aggregators into 10 levels using the quantile method, where the first level has the top 10% emission reduction rate, the second level has the top 10% to 20% emission reduction rate, the third level has the top 20% to 30% emission reduction rate, and so on;

[0083] Among them, the preset carbon reduction score for the first level is 100, the preset carbon reduction score for the second level is 90, the preset carbon reduction score for the third level is 80, and so on.

[0084] Sorting the aggregators in the second group in descending order of the emission reduction rates, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method;

[0085] Determine the emission reduction rate and carbon reduction score of the aggregators in the second group according to the preset emission reduction rate and carbon reduction score corresponding to each level; 减排率 Sort the emission reduction rates from high to low and classify the aggregators into 10 levels using the quantile method, where the first level has the top 10% emission reduction rate, the second level has the top 10% to 20% emission reduction rate, the third level has the top 20% to 30% emission reduction rate, and so on;

[0086] Among them, the preset emission reduction rate and carbon reduction score of the first level is 100, the preset emission reduction rate and carbon reduction score of the second level is 90, the preset emission reduction rate and carbon reduction score of the third level is 80, and so on.

[0087] Sorting the aggregators in the second group in descending order of the emission reduction, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method;

[0088] Determine the emission reduction and carbon reduction scores of the aggregators in the second group according to the preset emission reduction and carbon reduction scores corresponding to each level; 减排量 Sort the emission reductions from high to low and classify the aggregators into 10 levels using the quantile method, where the first level emission reductions are in the top 10%, the second level emission reductions are in the top 10% to 20%, the third level emission reductions are in the top 20% to 30%, and so on;

[0089] Among them, the preset emission reduction and carbon reduction score of the first level is 100, the preset emission reduction and carbon reduction score of the second level is 90, the preset emission reduction and carbon reduction score of the third level is 80, and so on. The carbon reduction score G1 of the aggregator in the first group can be calculated according to the above correspondence.

[0090] The carbon reduction score of the aggregator in the second group is calculated based on the carbon reduction score of the emission reduction rate and its weight and the carbon reduction score of the emission reduction amount and its weight; the carbon reduction score G2 of the aggregator in the second group is calculated based on the following formula:

[0091] G2=β1×G 减排量 +β2×G 减排率

[0092] Among them, G 减排量 is the emission reduction and carbon reduction score of the aggregator in the second group, G 减排率 is the carbon reduction score of the aggregator in the second group, β1 is the weight corresponding to the carbon reduction score of the emission reduction, and β2 is the weight corresponding to the carbon reduction score of the emission reduction rate.

[0093] Sorting the aggregators in the third group in descending order of the emission reduction, and dividing the aggregators in the third group into a preset number of levels according to the sorting sequence and using a quantile method;

[0094] The carbon reduction scores of the aggregators in the third group are determined according to the preset carbon reduction scores corresponding to each level. 减排量 Sort the emission reductions from high to low and classify the aggregators into 10 levels using the quantile method, where the first level emission reductions are in the top 10%, the second level emission reductions are in the top 10% to 20%, the third level emission reductions are in the top 20% to 30%, and so on;

[0095] Among them, the preset emission reduction and carbon reduction score of the first level is 100, the preset emission reduction and carbon reduction score of the second level is 90, and the preset emission reduction and carbon reduction score of the third level is 80. And so on, the carbon reduction score G3 of the aggregator in the third group can be calculated according to the above correspondence. It can be understood that the carbon reduction score G of each aggregator is G = {G1, G2, G3}.

[0096] It should be noted that different databases have different carbon reduction score calculation rules to target aggregators of different sizes to ensure the accuracy of the aggregator’s final carbon performance score. β1 and β2 can be set to 50% and 50% respectively, or they can be flexibly set according to specific circumstances by consulting experts in related fields.

[0097] The future emission reduction potential can be predicted by taking the interval of i days as a cycle, so as to predict the future emission reduction potential of the aggregator in the quarter. Collect the carbon emission value A of each i-day in the current quarter of the aggregator n , the carbon emission value A of the aggregator on each i-day in the current quarter in the next cycle n+1 , calculate the change value of carbon emissions data of the aggregator every i days ΔA n , and its algorithm formula is:

[0098] ΔA n =A n -A n+1

[0099] The method of predicting the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator includes:

[0100] The carbon emission data change values ​​of each aggregator are processed based on the preset carbon emission reduction trend prediction model to obtain the carbon emission reduction trend of each aggregator; the carbon emission data change values ​​of each aggregator can be input into the preset carbon emission reduction trend prediction model, and the output result of the preset carbon emission reduction trend prediction model is used as the carbon emission reduction trend of each aggregator, specifically the carbon emission reduction trend in future months.

[0101] Wherein, the preset carbon emission reduction trend prediction model is a pre-trained neural network model;

[0102] Rank each aggregator in descending order according to the slope reflecting the carbon emission reduction trend of each aggregator, and divide each aggregator into a preset number of levels according to the ranking sequence and by using the quantile method;

[0103] The future emission reduction potential score of each aggregator is determined based on the preset future emission reduction potential score corresponding to each level.

[0104] The production equipment control method of the aggregator further includes:

[0105] The weights and biases in the neural network model are optimized by a particle swarm optimization method to optimize the preset carbon emission reduction trend prediction model.

[0106] The optimizing the weights and biases in the neural network model by using a particle swarm optimization method includes:

[0107] Taking each particle as a set of weights and biases of the neural network model, taking the mean square error of the neural network model as the fitness function, and obtaining a velocity update formula and a position update formula;

[0108] The speed of the particle is updated according to the speed update formula, and the position of the particle is updated according to the position update formula, until the respective corresponding maximum number of iterations is reached, and the neural network model at this time is used as the optimized preset carbon emission reduction trend prediction model.

[0109] The neural network model is specifically a multi-layer perceptron neural network, and aggregates the carbon emission data change value ΔA every i days n As the input layer, through the ReLU activation function as the hidden layer, the output layer is the emission reduction trend of each month in the future. The speed update formula is as follows:

[0110] v i+1 =ω×v i +c1×r1×(p i -x i )+c2×r2×(gx i )

[0111] Among them, v i+1 is the velocity of the particle after iteration, v i is the velocity of the particle before iteration, ω is the inertia weight, c1 is the first acceleration coefficient, c2 is the second acceleration coefficient, r1 is the first random number, r2 is the second random number, p i is the individual optimal position of the particle, g is the global optimal position of the particle, x iis the position of the particle before iteration.

[0112] The velocity update formula is as follows:

[0113] x i+1 =x i +v i+1

[0114] Among them, x i+1 The particle position and speed are randomly initialized for the iterated particle position. In each iteration, the particle speed and position are updated according to the above formula until the maximum number of iterations is reached. The future monthly emission reduction trend is predicted by using the optimized neural network model.

[0115] The slope of the carbon reduction trend of each aggregator is ranked from large to small, and the aggregators are divided into 10 levels by the quantile method, where the first level slope is in the top 10%, the second level slope is in the top 10% to 20%, the third level slope is in the top 20% to 30%, and so on;

[0116] Among them, the preset future emission reduction potential score of the first level is 100, the preset future emission reduction potential score of the second level is 90, the preset future emission reduction potential score of the third level is 80, and so on.

[0117] It should be noted that when sorting according to the slope of the emission reduction trend, the larger the slope, the more obvious the emission reduction trend and the greater the emission reduction potential. When sorting according to the size of the slope, the larger the slope, the higher the ranking. When predicting the emission reduction trend based on the particle swarm optimization and neural network fusion model, the model outputs the trend of changes in the carbon emissions of the aggregator in the future period of time. Specifically, this trend reflects the direction and magnitude of changes in the carbon emissions of the aggregator in the future months, that is, the rate and magnitude of reduction or increase in carbon emissions.

[0118] In the above step S3, the device performs carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determines the target aggregator to be optimized according to the carbon performance rating result, and performs energy consumption reduction control on the target production equipment of the target aggregator. The carbon performance rating of each aggregator according to the carbon reduction score and the future emission reduction potential score includes:

[0119] The carbon performance score of each aggregator is calculated according to the following formula:

[0120] Z=α1×G+α2×F

[0121] Among them, Z is the carbon performance score, G is the carbon reduction score, F is the future emission reduction potential score, α1 is the weight corresponding to the carbon reduction score, and α2 is the weight corresponding to the future emission reduction potential score.

[0122] It should be noted that α1 and α2 can be set to 0.7 and 0.3 respectively, and the above setting values ​​can also be flexibly adjusted according to the specific use environment. The carbon performance score can be directly used as the carbon performance rating result.

[0123] The target aggregators to be optimized are determined according to the carbon performance rating results, including:

[0124] According to the preset statistical period, sort and number each aggregator in descending order according to the carbon performance score, and record the adjustment score of the last aggregator. n =1, the adjustment points of the remaining aggregators are C n = 0. The preset statistical period can be set according to the actual situation and can be selected as weekly.

[0125] When the preset period statistical number is reached, the sum of each adjustment score of all aggregators is counted and judged. The preset period statistical number can be selected as 3. n +C n+1 +C n+2 =3 (each time statistics are performed, the value of the subscript n is increased by 1) and the corresponding aggregator is determined as the target aggregator.

[0126] The controlling of reducing energy consumption of the target production equipment of the target aggregator includes:

[0127] Monitor the energy consumption data of the production equipment of the target aggregator; there is no specific limitation on the type of the energy consumption data of the production equipment.

[0128] The production equipment corresponding to the production equipment energy consumption data greater than the preset energy consumption threshold is determined as the target production equipment; the preset energy consumption threshold can be independently set according to actual conditions.

[0129] Sending a remote control instruction to control the operating state of the target production equipment, wherein the remote control instruction carries an energy consumption reduction control strategy;

[0130] The energy consumption reduction control strategy includes one or more of reducing the power output of the device, adjusting the operating time of the device, and shutting down the preset device. The preset device may refer to a device that is not related to the production of the product and is not specifically limited.

[0131] Referring to the above example, C n +C n+1 +C n+2=3, the energy consumption management system is used to control the production equipment of the aggregator, including real-time monitoring of the energy consumption data of the aggregator's production equipment, timely detection and marking of abnormally high energy consumption equipment, and feedback to the relevant aggregators, and sending remote control instructions to the controller to control the operating status of the target production equipment. The control method can adopt the above-mentioned energy consumption reduction control strategy, or after the controller receives the remote control instruction, the remote control instruction can be made effective after manual confirmation to achieve control of the operating status of the target production equipment.

[0132] The production equipment control method for an aggregator provided in an embodiment of the present invention groups the aggregators according to their historical carbon emission data, and allocates a database providing corresponding carbon emission index data to each group; selects corresponding carbon emission index data from each database, evaluates the carbon emissions of the aggregators in each group, and obtains a carbon reduction score for each aggregator in each group; and predicts the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtains a future emission reduction potential score for each aggregator; performs a carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determines a target aggregator to be optimized according to the carbon performance rating result, and performs energy consumption reduction control on the target production equipment of the target aggregator, so as to accurately and specifically select the production equipment of the aggregator for control, thereby achieving energy conservation and emission reduction.

[0133] Furthermore, each group is obtained according to the scale of carbon emissions of the aggregator determined based on the threshold; accordingly, the database providing corresponding carbon emission index data is allocated to each group, including:

[0134] A first database of emission reduction rates is allocated to a first group with a smaller carbon emission scale than the aggregator; the description may refer to the above embodiment and will not be repeated here.

[0135] A second database of emission reduction rates and emission reduction amounts is provided for allocation to a second group with a moderate scale of carbon emissions as the aggregator; the above-mentioned embodiments can be referred to for description and will not be described in detail.

[0136] A third database for providing emission reductions is allocated to a third group with a larger carbon emission scale than the aggregator. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0137] Furthermore, the carbon emissions of the aggregators in each group are evaluated to obtain the carbon reduction score of each aggregator in each group, including:

[0138] The aggregators in the first group are sorted in descending order of the emission reduction rate, and the aggregators in the first group are divided into a preset number of levels according to the sorting sequence and by the quantile method; the description can be made with reference to the above embodiment and will not be repeated here.

[0139] The carbon reduction scores of the aggregators in the first group are determined according to the preset carbon reduction scores corresponding to each level; please refer to the above-mentioned embodiment for description and will not be repeated here.

[0140] The aggregators in the second group are sorted in descending order of the emission reduction rate, and the aggregators in the second group are divided into a preset number of levels according to the sorting sequence and by the quantile method; the description can be made with reference to the above embodiment and will not be repeated here.

[0141] The emission reduction rate and carbon reduction scores of the aggregators in the second group are determined according to the preset emission reduction rate and carbon reduction scores corresponding to each level; please refer to the above-mentioned embodiment for description and will not be repeated here.

[0142] The aggregators in the second group are sorted in descending order of the emission reduction amount, and the aggregators in the second group are divided into a preset number of levels according to the sorting sequence and by the quantile method; the description can be made with reference to the above embodiment and will not be repeated here.

[0143] The emission reduction and carbon reduction scores of the aggregators in the second group are determined according to the preset emission reduction and carbon reduction scores corresponding to each level; please refer to the above-mentioned embodiment for description and will not be repeated here.

[0144] The carbon reduction scores of the aggregators in the second group are calculated based on the emission reduction rate carbon reduction score and its weight and the emission reduction amount carbon reduction score and its weight; the above-mentioned embodiments can be referred to for description and will not be repeated here.

[0145] The aggregators in the third group are sorted in descending order of the emission reduction amount, and the aggregators in the third group are divided into a preset number of levels according to the sorting sequence and by the quantile method; the description can be made with reference to the above embodiment and will not be repeated here.

[0146] The carbon reduction scores of the aggregators in the third group are determined according to the preset carbon reduction scores corresponding to each level. The above embodiments can be referred to for explanation and will not be described in detail.

[0147] Furthermore, the future emission reduction potential of each aggregator is predicted according to the change value of the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator, including:

[0148] Based on the preset carbon emission reduction trend prediction model, the carbon emission data change value of each aggregator is processed to obtain the carbon emission reduction trend of each aggregator; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0149] Among them, the preset carbon emission reduction trend prediction model is a pre-trained neural network model; it can be described with reference to the above embodiment and will not be repeated here.

[0150] Each aggregator is sorted in descending order according to the slope reflecting the carbon emission reduction trend of each aggregator, and each aggregator is divided into a preset number of levels according to the sorting sequence and by the quantile method; please refer to the above embodiment for description and will not be repeated here.

[0151] The future emission reduction potential score of each aggregator is determined according to the preset future emission reduction potential score corresponding to each level. The above description can be referred to in the above embodiment and will not be repeated here.

[0152] Furthermore, the production equipment control method of the aggregator further includes:

[0153] The weights and biases in the neural network model are optimized by a particle swarm optimization method to optimize the preset carbon emission reduction trend prediction model. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0154] Furthermore, the optimizing the weights and biases in the neural network model by using a particle swarm optimization method includes:

[0155] Each particle is used as a set of weights and biases of the neural network model, and the mean square error of the neural network model is used as the fitness function to obtain the speed update formula and the position update formula; the above embodiment can be referred to for description and will not be repeated here.

[0156] The particle speed is updated according to the speed update formula, and the particle position is updated according to the position update formula, until the respective corresponding maximum number of iterations is reached, and the neural network model at this time is used as the optimized preset carbon emission reduction trend prediction model. The above-mentioned embodiment can be referred to for explanation, and no further description is given.

[0157] Furthermore, the controlling of reducing energy consumption of the target production equipment of the target aggregator includes:

[0158] Monitor the energy consumption data of the production equipment of the target aggregator; refer to the above-mentioned embodiment for description and will not be repeated here.

[0159] The production equipment corresponding to the production equipment energy consumption data greater than the preset energy consumption threshold is determined as the target production equipment; the description can be made with reference to the above embodiment and will not be repeated here.

[0160] Send a remote control instruction to control the operating state of the target production equipment, and the remote control instruction carries a control strategy for reducing energy consumption; the above-mentioned embodiment can be referred to for description and will not be repeated here.

[0161] The energy consumption reduction control strategy includes one or more of reducing the power output of the device, adjusting the running time of the device, and shutting down the preset device. The above embodiments can be referred to for explanation and will not be described in detail.

[0162] Figure 2 is a schematic diagram of the structure of a production equipment control device of an aggregater provided by an embodiment of the present invention, such as Figure 2 As shown, the production equipment control device of the aggregator provided in the embodiment of the present invention includes an allocation unit 201, a prediction unit 202 and a control unit 203, wherein:

[0163] The allocation unit 201 is used to group the aggregators according to their historical carbon emission data, and allocate a database that provides corresponding carbon emission index data to each group; the prediction unit 202 is used to select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator; the control unit 203 is used to perform carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating result, and control the target production equipment of the target aggregator to reduce energy consumption.

[0164] Specifically, the allocation unit 201 in the device is used to group the aggregators according to their historical carbon emission data, and allocate a database that provides corresponding carbon emission index data to each group; the prediction unit 202 is used to select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator; the control unit 203 is used to perform carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating result, and control the target production equipment of the target aggregator to reduce energy consumption.

[0165] The production equipment control device for an aggregator provided in an embodiment of the present invention groups the aggregators according to their historical carbon emission data, and allocates a database providing corresponding carbon emission index data to each group; selects corresponding carbon emission index data from each database, evaluates the carbon emissions of the aggregators in each group, and obtains a carbon reduction score for each aggregator in each group; and predicts the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtains a future emission reduction potential score for each aggregator; performs a carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determines a target aggregator to be optimized according to the carbon performance rating result, and performs energy consumption reduction control on the target production equipment of the target aggregator, so as to accurately and specifically select the production equipment of the aggregator for control, thereby achieving energy conservation and emission reduction.

[0166] The embodiment of the present invention provides an embodiment of the production equipment control device of the aggregator, which can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not repeated here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0167] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device includes: a processor (processor) 301, a memory (memory) 302 and a bus 303;

[0168] The processor 301 and the memory 302 communicate with each other via a bus 303;

[0169] The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above method embodiments, for example, including:

[0170] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0171] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0172] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0173] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:

[0174] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0175] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0176] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0177] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:

[0178] Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data;

[0179] Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator;

[0180] A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

[0181] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0185] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0186] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A production equipment control method for an aggregator, characterized in that: include: Grouping the aggregators according to their historical carbon emissions data, and assigning each group a database that provides corresponding carbon emissions indicator data; Select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator; A carbon performance rating is performed on each aggregator based on the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined based on the carbon performance rating results, and energy consumption reduction control is performed on the target production equipment of the target aggregator.

2. The production equipment control method of an aggregator according to claim 1, characterized in that: Each group is obtained by dividing the carbon emission scale of the aggregator based on the threshold; accordingly, each group is allocated a database providing corresponding carbon emission index data, including: A first database providing emission reduction rates for the first group of aggregators with a smaller carbon footprint; A second database of emission reduction rates and reductions is provided for allocation to a second group of aggregators with an appropriate scale of carbon emissions; A third database is provided to allocate emission reductions to third groups with larger carbon emissions than aggregators.

3. The production equipment control method of an aggregator according to claim 2, characterized in that: The carbon emissions of the aggregators in each group are evaluated to obtain the carbon reduction score of each aggregator in each group, including: Sorting the aggregators in the first group in descending order of the emission reduction rates, and dividing the aggregators in the first group into a preset number of levels according to the sorting sequence and using a quantile method; Determining the carbon reduction scores of the aggregators in the first group according to the preset carbon reduction scores corresponding to each level; Sorting the aggregators in the second group in descending order of the emission reduction rates, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method; Determining the emission reduction rate and carbon reduction scores of the aggregators in the second group according to the preset emission reduction rate and carbon reduction scores corresponding to each level; Sorting the aggregators in the second group in descending order of the emission reduction, and dividing the aggregators in the second group into a preset number of levels according to the sorting sequence and using a quantile method; Determining the emission reduction and carbon reduction scores of the aggregators in the second group according to the preset emission reduction and carbon reduction scores corresponding to each level; Calculate the carbon reduction scores of the aggregators in the second group based on the carbon reduction score of the emission reduction rate and its weight and the carbon reduction score of the emission reduction amount and its weight; Sorting the aggregators in the third group in descending order of the emission reduction, and dividing the aggregators in the third group into a preset number of levels according to the sorting sequence and using a quantile method; The carbon reduction scores of the aggregators in the third group are determined according to the preset carbon reduction scores corresponding to each level.

4. The production equipment control method of an aggregator according to claim 1, characterized in that: The method of predicting the future emission reduction potential of each aggregator based on the change value of the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator includes: Based on the preset carbon emission reduction trend prediction model, the carbon emission data change value of each aggregator is processed to obtain the carbon emission reduction trend of each aggregator; Wherein, the preset carbon emission reduction trend prediction model is a pre-trained neural network model; Rank each aggregator in descending order according to the slope reflecting the carbon emission reduction trend of each aggregator, and divide each aggregator into a preset number of levels according to the ranking sequence and by using the quantile method; The future emission reduction potential score of each aggregator is determined based on the preset future emission reduction potential score corresponding to each level.

5. The production equipment control method of an aggregator according to claim 4, characterized in that: The production equipment control method of the aggregator also includes: The weights and biases in the neural network model are optimized by a particle swarm optimization method to optimize the preset carbon emission reduction trend prediction model.

6. The production equipment control method of an aggregator according to claim 5, characterized in that: The optimizing the weights and biases in the neural network model by using a particle swarm optimization method includes: Taking each particle as a set of weights and biases of the neural network model, taking the mean square error of the neural network model as the fitness function, and obtaining a velocity update formula and a position update formula; The speed of the particle is updated according to the speed update formula, and the position of the particle is updated according to the position update formula, until the respective corresponding maximum number of iterations is reached, and the neural network model at this time is used as the optimized preset carbon emission reduction trend prediction model.

7. The production equipment control method of an aggregator according to any one of claims 1 to 6, characterized in that: The controlling of reducing energy consumption of the target production equipment of the target aggregator includes: Monitoring energy consumption data of production equipment of the target aggregator; Determine the production equipment corresponding to the production equipment energy consumption data greater than the preset energy consumption threshold as the target production equipment; Sending a remote control instruction to control the operating state of the target production equipment, wherein the remote control instruction carries an energy consumption reduction control strategy; The energy consumption reduction control strategy includes one or more of reducing the power output of the device, adjusting the operating time of the device, and shutting down the preset device.

8. A production equipment control device for an aggregator, characterized in that: include: an allocation unit, configured to group the aggregators according to their historical carbon emission data, and allocate a database providing corresponding carbon emission indicator data to each group; A prediction unit is used to select corresponding carbon emission index data from each database, evaluate the carbon emissions of the aggregators in each group, and obtain the carbon reduction score of each aggregator in each group; and predict the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator, and obtain the future emission reduction potential score of each aggregator; A control unit is used to rate the carbon performance of each aggregator according to the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized according to the carbon performance rating results, and control the target production equipment of the target aggregator to reduce energy consumption.

9. An electronic device 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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