A method and apparatus for controlling production equipment of a consolidator

Abstract

The application provides a method and device for controlling production equipment of aggregators, and relates to the technical field of control. The method comprises the following steps: evaluating the carbon emission of each aggregator in each group to obtain a carbon reduction score of each aggregator in each group; and predicting the future emission reduction potential of each aggregator according to the carbon emission data change value of each aggregator to obtain a future emission reduction potential score of each aggregator; 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 the carbon performance rating result, and performing energy consumption reduction control on the target production equipment of the target aggregator. The device executes the above method. The method and device for aggregators provided in the embodiments of the application can accurately and specifically select the production equipment of the aggregators to be controlled, and thus energy saving and emission reduction can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control, in particular to a method and device for controlling production equipment of an aggregator. BACKGROUND

[0002] The aggregator can be an energy aggregator managed by a certain group. In order to achieve the goal of energy saving and emission reduction, it is necessary to control the running state of the production equipment of the aggregator.

[0003] The existing method for controlling the production equipment of the aggregator mainly realizes the running state control of the production equipment of each aggregator independently. Although the group can also remotely control the production equipment of the aggregator, the scales of the aggregators under the group are different, and there are great differences in resources and operation capacity between aggregators of different scales. It is difficult to accurately reflect the actual carbon performance level by using fixed values for evaluation, and thus it is also impossible to accurately and targetedly select the production equipment of the aggregator for control and adjustment. SUMMARY

[0004] In view of the problems in the prior art, the embodiments of the present application provide a method and device for controlling the production equipment of an aggregator, which can at least partially solve the problems in the prior art.

[0005] In one aspect, the present application provides a method for controlling the production equipment of an aggregator, comprising:

[0006] grouping the aggregators according to historical carbon emission data of the aggregators, and respectively assigning each group a database providing corresponding carbon emission index data;

[0007] selecting corresponding carbon emission index data from each database, evaluating the carbon emission of each aggregator in each group to obtain a carbon reduction score of each aggregator in each group, and predicting the future emission reduction potential of each aggregator according to the change value of the carbon emission data of each aggregator to obtain a future emission reduction potential score of each aggregator;

[0008] carbon performance rating each aggregator according to the carbon reduction score and the future emission reduction potential score, determining a target aggregator to be optimized according to the carbon performance rating result, and performing energy consumption reduction control on the target production equipment of the target aggregator.

[0009] Accordingly, the assigning of the database providing corresponding carbon emission index data for each group comprises:

[0010] assigning a first database providing an emission reduction rate for a first group with a small carbon emission scale of the aggregator;

[0011] a second database providing the emission reduction rate and the emission reduction amount for the second group of aggregators with a medium scale of carbon emission;

[0012] a third database providing the emission reduction amount for the third group of aggregators with a large scale of carbon emission.

[0013] wherein the evaluation of the carbon emission of each aggregator in each group to obtain the carbon reduction score of each aggregator in each group comprises:

[0014] the aggregators in the first group are sorted according to the emission reduction rate from large to small, 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;

[0015] the carbon reduction score of the aggregators in the first group is determined according to the preset carbon reduction score corresponding to each level;

[0016] the aggregators in the second group are sorted according to the emission reduction rate from large to small, 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;

[0017] the emission reduction rate carbon reduction score of the aggregators in the second group is determined according to the preset emission reduction rate carbon reduction score corresponding to each level;

[0018] the aggregators in the second group are sorted according to the emission reduction amount from large to small, 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;

[0019] the emission reduction amount carbon reduction score of the aggregators in the second group is determined according to the preset emission reduction amount carbon reduction score corresponding to each level;

[0020] the carbon reduction score of the aggregators in the second group is calculated according to the emission reduction rate carbon reduction score and its weight and the emission reduction amount carbon reduction score and its weight;

[0021] the aggregators in the third group are sorted according to the emission reduction amount from large to small, 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;

[0022] the carbon reduction score of the aggregators in the third group is determined according to the preset carbon reduction score corresponding to each level.

[0023] wherein the prediction of the future emission reduction potential of each aggregator according to the carbon emission data change value of each aggregator to obtain the future emission reduction potential score of each aggregator comprises:

[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 based on the ranking sequence and 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] The aggregator's production equipment control method 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 method 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 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 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 controlling of reducing energy consumption of the target production equipment of the target aggregator includes:

[0034] monitoring energy consumption data of the target aggregator’s production equipment;

[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 application provides a production equipment control device of a polymerization merchant, comprising:

[0039] a distribution unit configured to group the polymerization merchants according to historical carbon emission data of the polymerization merchants, and assign each group a database providing corresponding carbon emission index data;

[0040] a prediction unit configured to select corresponding carbon emission index data from each database, evaluate carbon emission of the polymerization merchants in each group to obtain a carbon reduction score of each polymerization merchant in each group, and predict future carbon emission reduction potential of each polymerization merchant according to a carbon emission data change value of each polymerization merchant to obtain a future carbon emission reduction potential score of each polymerization merchant;

[0041] a control unit configured to perform carbon performance rating on each polymerization merchant according to the carbon reduction score and the future carbon emission reduction potential score, determine a target polymerization merchant to be optimized according to a carbon performance rating result, and perform energy consumption reduction control on a target production equipment of the target polymerization merchant.

[0042] In another aspect, the present application provides an electronic device, comprising a processor, a memory and a bus, wherein,

[0043] the processor and the memory complete communication with each other through the bus;

[0044] the memory stores program instructions executable by the processor, and the processor calling the program instructions can execute the following method:

[0045] group the polymerization merchants according to historical carbon emission data of the polymerization merchants, and assign each group a database providing corresponding carbon emission index data;

[0046] select corresponding carbon emission index data from each database, evaluate carbon emission of the polymerization merchants in each group to obtain a carbon reduction score of each polymerization merchant in each group, and predict future carbon emission reduction potential of each polymerization merchant according to a carbon emission data change value of each polymerization merchant to obtain a future carbon emission reduction potential score of each polymerization merchant;

[0047] perform carbon performance rating on each polymerization merchant according to the carbon reduction score and the future carbon emission reduction potential score, determine a target polymerization merchant to be optimized according to a carbon performance rating result, and perform energy consumption reduction control on a target production equipment of the target polymerization merchant.

[0048] The present application provides a non-transitory computer readable storage medium, comprising:

[0049] the non-transitory computer readable storage medium stores computer instructions, and the computer instructions make the computer execute the following method:

[0050] grouping the aggregators according to historical carbon emission data of the aggregators, and respectively assigning a database providing corresponding carbon emission index data to each group;

[0051] selecting corresponding carbon emission index data from each database, evaluating carbon emission of the aggregators in each group to obtain a carbon reduction score of each aggregator in each group, and predicting future emission reduction potential of each aggregator according to a change value of carbon emission data of each aggregator to obtain a future emission reduction potential score of each aggregator;

[0052] 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 result of the carbon performance rating, and performing energy consumption reduction control on a target production device of the target aggregator.

[0053] The production device control method and device for aggregators provided by the embodiments of the present application group the aggregators according to historical carbon emission data of the aggregators, and respectively assign a database providing corresponding carbon emission index data to each group. The corresponding carbon emission index data is selected from each database, carbon emission of the aggregators in each group is evaluated to obtain a carbon reduction score of each aggregator in each group, and future emission reduction potential of each aggregator is predicted according to a change value of carbon emission data of each aggregator to obtain a future emission reduction potential score of each aggregator. Carbon performance rating is performed on each aggregator according to the carbon reduction score and the future emission reduction potential score, a target aggregator to be optimized is determined according to a result of the carbon performance rating, and energy consumption reduction control is performed on a target production device of the target aggregator. The production device control method and device for aggregators can accurately and specifically select a production device of an aggregator to be controlled, and further realize energy saving and emission reduction. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. In the drawings:

[0055] Figure 1 is a flowchart of the production device control method for aggregators provided by an embodiment of the present application.

[0056] Figure 2 is a structural schematic diagram of the production device control device for aggregators provided by an embodiment of the present application.

[0057] Figure 3An electronic device entity structure schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, further detailed description of the embodiments of the present application is made below in combination with the drawings. Herein, the illustrative embodiments of the present application and the description thereof are used to explain the present application, but not as a limitation of the present application. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other at will.

[0059] Figure 1 is a flowchart of a production equipment control method of a polymerization merchant provided by an embodiment of the present application, as shown in Figure 1 The production equipment control method of the polymerization merchant provided by the embodiment of the present application comprises:

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

[0061] Step S2: selecting corresponding carbon emission index data from each database, evaluating the carbon emission of the polymerization merchants in each group, obtaining a carbon reduction score of each polymerization merchant in each group, and predicting the future emission reduction potential of each polymerization merchant according to the carbon emission data change value of each polymerization merchant, obtaining a future emission reduction potential score of each polymerization merchant.

[0062] Step S3: carbon performance rating of each polymerization merchant according to the carbon reduction score and the future emission reduction potential score, determining a target polymerization merchant to be optimized according to the carbon performance rating result, and performing energy consumption reduction control on the target production equipment of the target polymerization merchant.

[0063] In the above step S1, the device groups the polymerization merchants according to the historical carbon emission data of the polymerization merchants, and respectively assigns a database providing corresponding carbon emission index data to each group. The device can be a computer device or the like executing the method. The historical carbon emission data can be obtained in a year as a period, and the grouping of the polymerization merchants according to the historical carbon emission data of the polymerization merchants comprises:

[0064] Obtaining the total value C0 of the historical carbon emission of the polymerization merchants in the last year, and determining the total value of the historical carbon emission of each polymerization merchant in the last year based on a first carbon emission threshold C1 and a second carbon emission threshold C2:

[0065] When C0 < C1, it represents that the carbon emission scale of the polymerization merchant is small, and these polymerization merchants are divided into a first group;

[0066] When C1≤C0≤C2, it means that the carbon emissions of the relevant aggregators are 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 divided into the third group.

[0068] Each group is divided according to the scale of carbon emissions of the aggregator determined based on the threshold; accordingly, each group is allocated a database that provides corresponding carbon emission indicator data, including:

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

[0070] A second database of emission reduction rates and amounts is provided for allocation to a second group of aggregators with a moderate carbon footprint;

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

[0072] Databases are established separately 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 relatively small carbon emission scales, the comprehensive database will store data related to aggregators with moderate carbon emission scales, and the emission reduction database will store data related to aggregators with relatively large 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 to evaluate the carbon performance 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 the 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. The carbon emission evaluation can be performed quarterly as a cycle, and the carbon emission value C of the aggregator in the current quarter is collected. a And the carbon emissions value C in the previous quarter b , calculate the carbon emission reduction rate C减排率 and the reduction amount 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 current quarter carbon emission value C a and the last quarter carbon emission value C b , the carbon emission reduction rate and the carbon emission reduction amount are calculated respectively, and the current quarter carbon emission reduction rate and the carbon emission reduction amount of the aggregators are evaluated according to different rules through the carbon emission scale of different aggregators. The aggregators with small carbon emission scale focus on the improvement of the reduction rate, the aggregators with moderate carbon emission scale comprehensively evaluate the reduction rate and the reduction amount, and the aggregators with large carbon emission scale focus on the actual reduction of the reduction amount.

[0080] The carbon emission of the aggregators in each group is evaluated to obtain the carbon reduction score of each aggregator in each group, which includes:

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

[0082] The carbon reduction score of the aggregators in the first group is determined according to the preset carbon reduction score corresponding to each level; the C 减排率 of the aggregators in the first group is sorted from high to low, and the aggregators are divided into 10 levels through the quantile method, wherein the reduction rate of the first level is in the top 10%, the reduction rate of the second level is in the top 10% to 20%, the reduction rate of the third level is in the top 20% to 30%, and so on.

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

[0084] The aggregators in the second group are sorted in descending order of the reduction rate, and the aggregators in the second group are divided into a preset number of levels according to the sorting sequence and through the quantile method;

[0085] According to the preset carbon reduction score corresponding to the emission reduction rate of each level, the carbon reduction score of the emission reduction rate of the aggregator in the second group is determined; the carbon reduction score of the aggregator in the second group is calculated according to the following formula: 减排率 The emission reduction rates are sorted from high to low, and the aggregators are divided into 10 levels by the quantile method, wherein the first level of the emission reduction rate is in the top 10%, the second level of the emission reduction rate is in the top 10% to 20%, the third level of the emission reduction rate is in the top 20% to 30%, and so on.

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

[0087] The aggregators in the second group are sorted according to the emission reduction amount from large to small, 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;

[0088] According to the preset carbon reduction score corresponding to the emission reduction amount of each level, the carbon reduction score of the emission reduction amount of the aggregator in the second group is determined; the carbon reduction score of the aggregator in the second group is calculated according to the following formula: 减排量 The emission reduction amounts are sorted from high to low, and the aggregators are divided into 10 levels by the quantile method, wherein the first level of the emission reduction amount is in the top 10%, the second level of the emission reduction amount is in the top 10% to 20%, the third level of the emission reduction amount is in the top 20% to 30%, and so on.

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

[0090] According to the carbon reduction score of the emission reduction rate and the weight thereof and the carbon reduction score of the emission reduction amount and the weight thereof, the carbon reduction score of the aggregator in the second group is calculated; the carbon reduction score G2 of the aggregator in the second group is calculated according to the following formula:

[0091] G2 = β1 × G 减排量 + β2 × G 减排率

[0092] G 减排量 is the carbon reduction score of the emission reduction amount of the aggregator in the second group, G 减排率 is the carbon reduction score of the emission reduction amount of the aggregator in the second group, β1 is the weight corresponding to the carbon reduction score of the emission reduction amount, 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 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 based on the preset carbon reduction scores corresponding to each level. 减排量 Ranking the emission reductions from high to low, and using the quantile method to divide the aggregators into 10 tiers, where the first tier emission reductions are in the top 10%, the second tier emission reductions are in the top 10% to 20%, the third tier emission reductions are in the top 20% to 30%, and so on;

[0095] The first level's preset carbon reduction score is 100, the second level's preset carbon reduction score is 90, the third level's preset carbon reduction score is 80, and so on. Based on the above correspondence, the carbon reduction score G3 for the aggregators in the third group can be calculated. It can be understood that the carbon reduction score G for each aggregator is {G1, G2, G3}.

[0096] It should be noted that different databases have different carbon reduction score calculation rules to target aggregators of different sizes and 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 interval i days can be used as a cycle to predict the future emission reduction potential, so as to predict the aggregator's quarterly future emission reduction potential. Collect the carbon emission value A of the aggregator every i days in the current quarter 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 in the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator includes:

[0100] 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 carbon emission data change value 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 each month in the future.

[0101] The preset carbon emission reduction trend prediction model is a neural network model that has been trained in advance.

[0102] Each aggregator is sorted in descending order of the slope reflecting the carbon emission reduction trend of each aggregator, and each aggregator is divided into a preset number of grades according to the sorting sequence and by the quantile method.

[0103] The future emission reduction potential score of each aggregator is determined according to the preset future emission reduction potential score corresponding to each grade.

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

[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 optimization of the weights and biases in the neural network model by the particle swarm optimization method comprises:

[0107] Each particle is taken as a set of weights and biases of the neural network model, and the mean square error of the neural network model is taken as a fitness function to obtain a speed 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 taken as the optimized preset carbon emission reduction trend prediction model.

[0109] The neural network model is specifically a multilayer perceptron neural network, and the carbon emission data change value ΔA n of the aggregator per i days is taken as an input layer, a ReLU activation function is taken as a hidden layer, and an output layer is a future monthly emission reduction trend. The speed update formula is as follows:

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

[0111] where v i+1 is the speed of the particle after iteration, v i is the speed 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, and x iThe position of the particle before iteration.

[0112] The velocity updating formula is as follows:

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

[0114] wherein x i+1 is the position of the particle after iteration, the particle position and velocity are randomly initialized, in each iteration, the velocity and position of the particle are updated according to the above formula, until the maximum iteration number, the optimized neural network model is used to predict the future monthly emission reduction trend.

[0115] The slope of the carbon emission reduction trend of each aggregator is sorted from large to small, and the aggregators are divided into 10 grades by using the quantile method, wherein the slope of the first grade is located in the front 10%, the slope of the second grade is located in the front 10% to 20%, the slope of the third grade is located in the front 20% to 30%, and so on.

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

[0117] It should be noted that according to the sorting of 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, according to the size of the slope, the larger the slope, the higher the ranking, and the emission reduction trend predicted based on the particle swarm optimization and neural network fusion model is the change trend of the carbon emission of the aggregator in the future period of time output by the model, specifically, this trend reflects the change direction and change amplitude of the carbon emission of the aggregator in each month in the future, that is, the rate and magnitude of the decrease or increase of the carbon emission.

[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 comprises:

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

[0120] Z = a1 x G + a2 x F

[0121] Wherein, Z is the carbon performance score, G is the carbon reduction score, F is the future emission reduction potential score, a1 is the weight corresponding to the carbon reduction score, and a2 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 usage environment. The carbon performance score can be directly used as the carbon performance rating result.

[0123] The target aggregators to be optimized are determined based on 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 C of the last aggregator. n =1, the adjustment points of the other numbered aggregators are C n = 0. The preset statistical period can be set according to actual conditions and can be selected as weekly.

[0125] When the preset period statistics number is reached, the sum of each adjustment score of all aggregators is counted and judged. The preset period statistics number can be selected as 3. n +C n+1 +C n+2 =3 (the value of the subscript n is increased by 1 each time statistics are performed) 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 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+2When the number of the target production device is equal to 3, the energy consumption management system is used to control the production device of the aggregator, including monitoring the energy consumption data of the production device of the aggregator in real time, timely finding the device with abnormally high energy consumption and marking it, feeding back to the related aggregator, and sending a remote control instruction to the controller to control the running state of the target production device. The control mode can adopt the above-mentioned energy consumption reduction control strategy, or the remote control instruction can be made effective after being received by the controller and confirmed by the human, so as to control the running state of the target production device.

[0132] The production device control method of the aggregator provided by the embodiment of the application groups the aggregators according to historical carbon emission data of the aggregators, and respectively allocates a database providing corresponding carbon emission index data to each group; selects corresponding carbon emission index data from each database, evaluates the carbon emission of the aggregators in each group, and obtains a carbon reduction score of each aggregator in each group; 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 of each aggregator; performs 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 device of the target aggregator, so that the production device of the aggregator can be accurately and selectively controlled, and energy saving and emission reduction can be realized.

[0133] Further, each group is obtained according to the threshold-based determination of the carbon emission scale of the aggregator. Correspondingly, the database providing corresponding carbon emission index data is respectively allocated to each group, including:

[0134] The first database providing the emission reduction rate is allocated to the first group with a small carbon emission scale of the aggregator. For details, refer to the above embodiment.

[0135] The second database providing the emission reduction rate and the emission reduction amount is allocated to the second group with a moderate carbon emission scale of the aggregator. For details, refer to the above embodiment.

[0136] The third database providing the emission reduction amount is allocated to the third group with a large carbon emission scale of the aggregator. For details, refer to the above embodiment.

[0137] Further, the carbon emission of the aggregators in each group is evaluated to obtain the carbon reduction score of each aggregator in each group, including:

[0138] The aggregators in the first group are sorted according to the order from large to small 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 using the quantile method. For details, refer to the above embodiment.

[0139] The carbon reduction score of the aggregator in the first group is determined according to the preset carbon reduction score corresponding to each level; refer to the above-mentioned embodiments for illustration, and no further description is made.

[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; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0141] The emission reduction rate carbon reduction score of the aggregator in the second group is determined according to the preset emission reduction rate carbon reduction score corresponding to each level; refer to the above-mentioned embodiments for illustration, and no further description is made.

[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; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0143] The emission reduction amount carbon reduction score of the aggregator in the second group is determined according to the preset emission reduction amount carbon reduction score corresponding to each level; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0144] The carbon reduction score of the aggregator in the second group is calculated according to the emission reduction rate carbon reduction score and the weight thereof and the emission reduction amount carbon reduction score and the weight thereof; refer to the above-mentioned embodiments for illustration, and no further description is made.

[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; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0146] The carbon reduction score of the aggregator in the third group is determined according to the preset carbon reduction score corresponding to each level; refer to the above-mentioned embodiments for illustration, and no further description is made.

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

[0148] The carbon emission amount data change value of each aggregator is processed based on a preset carbon emission reduction trend prediction model, to obtain a carbon emission reduction trend of each aggregator; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0149] The preset carbon emission reduction trend prediction model is a neural network model that is pre-trained; refer to the above-mentioned embodiments for illustration, and no further description is made.

[0150] According to the slope reflecting the carbon emission reduction trend of each aggregator from large to small, each aggregator is sorted, and each aggregator is divided into a preset number of grades according to the sorting sequence and by the quantile method; refer to the above embodiment description, which will not be repeated.

[0151] According to the preset future emission reduction potential score corresponding to each grade, the future emission reduction potential score of each aggregator is determined. Refer to the above embodiment description, which will not be repeated.

[0152] Further, the production equipment control method of the aggregator further comprises:

[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. Refer to the above embodiment description, which will not be repeated.

[0154] Further, the optimization of the weights and biases in the neural network model by the particle swarm optimization method comprises:

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

[0156] 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 taken as the optimized preset carbon emission reduction trend prediction model. Refer to the above embodiment description, which will not be repeated.

[0157] Further, the energy consumption reduction control of the target production equipment of the target aggregator comprises:

[0158] The production equipment energy consumption data of the target aggregator is monitored; refer to the above embodiment description, which will not be repeated.

[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; refer to the above embodiment description, which will not be repeated.

[0160] A remote control instruction is sent to control the operating state of the target production equipment, and the remote control instruction carries a energy consumption reduction control strategy; refer to the above embodiment description, which will not be repeated.

[0161] The energy consumption reduction control strategy comprises one or more of reducing the power output of the equipment, adjusting the running time of the equipment, and shutting down the preset equipment. Refer to the above embodiment description, which will not be repeated.

[0162] Figure 2 FIG. 1 is a schematic diagram of a production equipment control device for an aggregator provided by an embodiment of the present invention. Figure 2 As shown, the production equipment control device of the aggregator provided by 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 based on 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 based on the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized based on the carbon performance rating results, 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 based on 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 based on the carbon reduction score and the future emission reduction potential score, determine the target aggregator to be optimized based on the carbon performance rating results, and control the target production equipment of the target aggregator to reduce energy consumption.

[0165] The production equipment control device of the aggregator provided by the embodiment of the present application groups the aggregators according to historical carbon emission data of the aggregators, and respectively allocates a database providing corresponding carbon emission index data to each group; selects corresponding carbon emission index data from each database, evaluates the carbon emission of the aggregators in each group, and obtains a carbon reduction score of each aggregator in each group; 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 of each aggregator; performs 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 that the production equipment of the aggregator can be accurately and targetedly selected for control, and energy saving and emission reduction can be realized.

[0166] The embodiment of the production equipment control device of the aggregator provided by the present application can be specifically used to execute the processing procedure of each method embodiment, and the functions thereof will not be described herein again. For details, refer to the detailed description of the method embodiments.

[0167] Figure 3 The electronic device entity structure diagram provided by the embodiment of the present application is shown in FIG. 1, which includes a processor 301, a memory 302 and a bus 303. Figure 3

[0168] The processor 301, the memory 302 and the bus 303 can communicate with each other through the bus 303.

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

[0170] Group the aggregators according to historical carbon emission data of the aggregators, and respectively allocate a database providing corresponding carbon emission index data to each group;

[0171] Select corresponding carbon emission index data from each database, evaluate the carbon emission of the aggregators in each group, and obtain a 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 a future emission reduction potential score of each aggregator;

[0172] Perform carbon performance rating on each aggregator according to the carbon reduction score and the future emission reduction potential score, determine a target aggregator to be optimized according to the carbon performance rating result, and perform energy consumption reduction control on the target production equipment of the target aggregator.

[0173] ​The embodiment discloses a computer program product, the computer program product comprises a computer program stored on a non-transitory computer readable storage medium, the computer program comprises program instructions, when the program instructions are executed by a computer, the computer can execute the method provided by each method embodiment, for example, comprising:

[0174] According to the historical carbon emission data of the aggregators, the aggregators are grouped, and a database providing corresponding carbon emission index data is allocated to each group respectively;

[0175] The corresponding carbon emission index data is selected from each database respectively, the carbon emission of the aggregators in each group is evaluated, the carbon reduction score of each aggregator in each group is obtained, and the future emission reduction potential of each aggregator is predicted according to the change value of the carbon emission data of each aggregator, and the future emission reduction potential score of each aggregator is obtained;

[0176] According to the carbon performance rating of each aggregator according to the carbon reduction score and the future emission reduction potential score, the target aggregator to be optimized is determined according to the carbon performance rating result, and the target production equipment of the target aggregator is controlled to reduce energy consumption.

[0177] The embodiment provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program enables the computer to execute the method provided by each method embodiment, for example, comprising:

[0178] According to the historical carbon emission data of the aggregators, the aggregators are grouped, and a database providing corresponding carbon emission index data is allocated to each group respectively;

[0179] The corresponding carbon emission index data is selected from each database respectively, the carbon emission of the aggregators in each group is evaluated, the carbon reduction score of each aggregator in each group is obtained, and the future emission reduction potential of each aggregator is predicted according to the change value of the carbon emission data of each aggregator, and the future emission reduction potential score of each aggregator is obtained;

[0180] According to the carbon performance rating of each aggregator according to the carbon reduction score and the future emission reduction potential score, the target aggregator to be optimized is determined according to the carbon performance rating result, and the target production equipment of the target aggregator is controlled to reduce energy consumption.

[0181] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0182] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0183] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0184] The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0185] In the description of the specification, the description of the terms "one embodiment", "one specific embodiment", "some embodiments", "for example", "an example", "a specific example", or "some examples" etc. means 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 application. In the specification, the illustrative expressions 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 any appropriate way in one or more embodiments or examples.

[0186] The specific embodiments described above further illustrate the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

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 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 in the carbon emission data of each aggregator to obtain the future emission reduction potential score of each aggregator; Performing a carbon performance rating on each aggregator based on the carbon reduction score and the future emission reduction potential score, determining a target aggregator to be optimized based on the carbon performance rating results, and performing energy consumption reduction control on the target production equipment of the target aggregator; Each group is divided according to the scale of carbon emissions of the aggregator determined based on the threshold; accordingly, each group is allocated a database that provides corresponding carbon emission indicator data, including: A first database providing emission reduction rates for the first group of aggregators with smaller carbon emissions; A second database of emission reduction rates and amounts is provided for allocation to a second group of aggregators with a moderate carbon footprint; A third database that provides emission reductions for third groups with larger carbon emissions than aggregators; 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 reductions, and dividing the aggregators in the second group into a preset number of levels based on 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 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.

2. 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 in 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 based on the ranking sequence and 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.

3. The production equipment control method of an aggregator according to claim 2, characterized in that: The aggregator's production equipment control method further 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.

4. The production equipment control method of an aggregator according to claim 3, 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 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 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.

5. The production equipment control method of an aggregator according to any one of claims 1 to 4, characterized in that: The controlling of reducing energy consumption of the target production equipment of the target aggregator includes: monitoring energy consumption data of the target aggregator’s production equipment; 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.

6. 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 emissions data, and allocate a database providing corresponding carbon emissions indicator data to each group; a prediction unit configured to select corresponding carbon emission indicator 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 based on the change in the carbon emission data of each aggregator, and obtain a future emission reduction potential score for each aggregator; a control unit, configured to perform a carbon performance rating on each aggregator based on the carbon reduction score and the future emission reduction potential score, determine a target aggregator to be optimized based on the carbon performance rating result, and perform energy consumption reduction control on the target production equipment of the target aggregator; Each group is obtained by dividing the aggregator's carbon emissions based on the threshold; accordingly, the allocation unit is specifically used to: A first database providing emission reduction rates for the first group of aggregators with smaller carbon emissions; A second database of emission reduction rates and amounts is provided for allocation to a second group of aggregators with a moderate carbon footprint; A third database that provides emission reductions for third groups with larger carbon emissions than aggregators; The prediction unit is specifically configured to: 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 reductions, and dividing the aggregators in the second group into a preset number of levels based on 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 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.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. 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 5 are implemented.

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