CN116933970A - Intelligent power supply and distribution system of generator for coordinating power consumption requirement of cooling circulation system - Google Patents

Abstract

The invention provides an intelligent power supply and distribution system for generators that coordinates the power demand of the cooling cycle system, including: a power consumption record acquisition module extracting historical power consumption data of the heat treatment furnace and the cooling cycle system; a power consumption inertia analysis module for The first power consumption data and the second power consumption data are subjected to multi-dimensional correlation periodic analysis to obtain multi-dimensional power consumption inertia data; the personalized inertia analysis module analyzes the multi-dimensional personalized power consumption inertia based on the multi-dimensional power consumption inertia data and the corresponding investment situation. data; the power supply and distribution plan generation module generates a generator coordinated power supply and distribution plan based on the target input of the current production instance; the intelligent power supply and distribution module is used to supply and distribute power based on the generator coordinated power supply and distribution plan to achieve power generation The intelligent and flexible power supply and distribution of the machine-to-heat treatment furnace and cooling circulation system can meet the corresponding power demand when the heat treatment furnace and cooling circulation system are put into different situations, thereby reducing the unnecessary occupation of generators and reducing waste.

Description

Generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system

Technical field

The present invention relates to the technical field of generator power supply and distribution, and in particular to an intelligent generator power supply and distribution system that coordinates the power demand of a cooling cycle system.

Background technique

At present, graphene thermal conductive film will undergo two steps of heat treatment during the production process: carbonization treatment and graphitization treatment. During the heat treatment process, the temperature inside the heat treatment furnace is raised, and the cooling circulation system is used to ensure that the temperature outside the furnace is at a safe temperature. Below, the normal operation of the heat treatment furnace and the cooling cycle system requires the support of sufficient power supply. If the power supply to the heat treatment furnace is insufficient, the temperature in the furnace will decrease, causing air to enter the heat treatment furnace, which will lead to Processed products are oxidized at high temperatures. If the power supply to the cooling circulation system is insufficient, the cooling effect of the cooling circulation system will be reduced, resulting in extremely high temperatures in the workshop, leading to serious safety accidents. Therefore, in order to avoid the above situation, the existing heat treatment workshop of graphene thermal conductive film will prepare multiple generators to ensure the normal operation of the heat treatment furnace and cooling circulation system.

However, since different heat treatment furnaces and cooling circulation systems have different electricity requirements in different production instances, it is difficult to calculate accurately. Therefore, a large number of generators used as backup power sources are redundantly occupied in actual production, resulting in a waste of resources and an increase in production costs.

Therefore, the present invention proposes a generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system.

Contents of the invention

The present invention provides an intelligent power supply and distribution system for generators that coordinates the power demand of a cooling cycle system based on multi-dimensional correlation periodicity of historical power consumption data of each heat treatment furnace and cooling cycle system in each production instance. Analyze and determine the multi-dimensional power consumption inertia data of the heat treatment furnace and cooling cycle system without investment during the generation process, and further generate a reasonable power supply and distribution plan for the generator based on the target investment situation and multi-dimensional power consumption inertia data. , realize intelligent and flexible power supply and distribution based on generators to meet the corresponding power demand when the heat treatment furnace and cooling circulation system are put into different situations, thereby reducing the unnecessary occupation of generators and reducing waste.

The invention provides a generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system, including:

The power consumption record acquisition module is used to extract the first historical power consumption data of each heat treatment furnace and the second historical power consumption data of each cooling cycle system from the historical power consumption data of each production instance;

The power consumption inertia analysis module is used to perform multi-dimensional correlation periodic analysis on the first power consumption data and the second power consumption data of each production instance to obtain multi-dimensional power consumption inertia data;

The personalized inertia analysis module is used to analyze the multi-dimensional personalized power inertia of each complete cooling and heating equipment investment combination based on multi-dimensional power inertia data and the investment in heat treatment furnaces and cooling circulation systems in each production instance. data;

The power supply and distribution plan generation module is used to generate a generator coordinated power supply and distribution plan based on the target input of the current production instance;

The intelligent power supply and distribution module is used to supply and distribute power based on the generator's coordinated power supply and distribution plan and obtain intelligent power supply and distribution results.

Preferably, the electrical inertia analysis module includes:

The period division sub-module is used to divide the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system used in combination in the same production instance into associated periods, and obtain the first single cycle of multiple cycles of the heat treatment furnace. Power consumption data and the second single-cycle power consumption data of the cooling cycle system;

The inertia analysis sub-module is used to average all single-cycle power consumption data of the same power consumption object in the same production instance to obtain the power consumption inertia data of each power consumption object in each production instance;

The correlation analysis submodule is used to treat the linear relationship between the first single-cycle power consumption data of the combined heat treatment furnace and the second single-cycle power consumption data of the cooling cycle system in each production instance as a combined heat treatment Coordination of associated inertia data for power consumption of furnaces and cooling circuit systems in corresponding production instances;

Among them, electricity consumption objects include: heat treatment furnace and cooling circulation system;

The single-cycle power consumption data includes: the first single-cycle power consumption data and the second single-cycle power consumption data;

Multi-dimensional power consumption inertia data includes: power consumption inertia data and power consumption coordination related inertia data.

Preferably, the periodization submodule includes:

An equipment association unit is used to associate heat treatment furnaces and cooling circulation systems used in combination in the same production instance to obtain at least one set of hot and cold equipment groups;

A periodic analysis unit, used to perform periodic analysis on the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system included in the cold and hot equipment group, and determine the first power consumption cycle and cooling cycle of the heat treatment furnace. The second power cycle of the system;

The comprehensive cycle determination unit is used to regard the least common multiple of the first power cycle and the second power cycle as the comprehensive power cycle of the corresponding hot and cold equipment group in the corresponding production instance.

Preferably, the cycle analysis unit includes:

The target determination subunit is used to use the power consumption change curve corresponding to the first power consumption data of the heat treatment furnace included in the cooling and heating equipment group and the power consumption change curve corresponding to the second power consumption data of the cooling cycle system as targets. curve;

The accuracy determination subunit is used to determine the first interval time of all adjacent peaks and the second interval time of all adjacent valleys in the target curve, perform correlation denoising on the first interval time and the second interval time, and obtain multiple A comprehensive interval time, and the local division interval is determined based on the mean value and analysis accuracy of all comprehensive interval times in the target curve;

The period determination subunit is used to locally divide the target curve based on the local division interval and perform adjacent cluster analysis to determine the first power cycle of the heat treatment furnace or the second power cycle of the cooling cycle system.

Preferably, the accuracy determination subunit includes:

The change point detection end is used to determine the first mutation point in all the first intervals of the target curve and the second mutation point in all the second intervals of the target curve based on the change point detection algorithm;

The ordinal determination end is used to determine the first ordinal number of the later peak among all the peaks of the target curve among the adjacent peaks corresponding to the first mutation point, and at the same time, determine the first ordinal number among the adjacent valleys corresponding to the second mutation point. The lower-order valley is the second-order number among all the valleys in the target curve;

The associated denoising end is used to determine whether the ordinal difference between the first ordinal number and the second ordinal number that is greater than the first ordinal number and has the smallest difference with the first ordinal number is within the ordinal difference threshold. If so, the corresponding first ordinal number is The first interval time corresponding to the mutation point and the second gradient time corresponding to the second mutation point are deleted at the same time, and all remaining first interval times and second interval times are regarded as all comprehensive interval times;

The sequence generation end is used for sequence generation to determine the common factors of the average of all comprehensive interval times and the duration of all target curves, and sort the common factors from large to small to obtain a common factor sequence;

The interval determination end is used to regard the common factor in the common factor sequence whose sorting order is the same as the preset minimum number of cluster analysis times as the maximum duration, and the ratio of the mean value of the comprehensive interval time to the duration corresponding to the analysis accuracy as the minimum Duration, the local division interval is determined based on the minimum duration and maximum interval time.

Preferably, the period determination subunit includes:

The local analysis end is used to locally divide the target curve based on the local division interval, obtain multiple sub-target curves and corresponding local division intervals, and calculate the first-order derivative integral value of each sub-target curve in the corresponding local division interval;

The cluster analysis end is used to perform adjacent cluster analysis on the first-order derivative integral values of all sub-target curves in the target curve to obtain the adjacent cluster division results;

The period determination end is used to connect the local division intervals of the sub-target curves corresponding to the first-order derivative integral values belonging to the same cluster in the adjacent clustering division results to obtain the single-period division interval, and divide the single-period division of each target curve The duration of the interval is regarded as corresponding to the first power cycle of the heat treatment furnace or the second power cycle of the cooling cycle system.

Preferably, the cluster analysis end includes:

The sequential summary sub-terminal is used to summarize the first-order derivative integral values of all sub-target curves in the target curve in sequence to obtain a sequence of integral values;

The cluster analysis sub-end is used to divide the integral value sequence sequentially based on the division multiples in the preset gradually increasing division multiple sequence to obtain multiple integral value clusters for each division multiple. At the same time, calculate the The similarity of all integrated value clusters, until the similarity obtained by this division process is less than the similarity obtained by the previous division process, then the integrated value cluster obtained by the previous division process will be regarded as the adjacent cluster division result.

Preferably, the personalized inertial analysis module includes:

The first analysis sub-module is used to analyze the first power consumption inertia data of each heat treatment furnace in each production instance when the complete hot and cold equipment corresponding to the corresponding production instance is put into combination. Personalized power consumption inertia data;

The second analysis sub-module is used to analyze the performance of each cooling cycle system object when the complete cooling and heating equipment corresponding to the corresponding production instance is put into combination based on the second power inertia data of each cooling cycle system in each production instance. Second personalized power consumption inertia data;

The third analysis sub-module is used to analyze the personalized usage of each cooling and heating equipment group in each complete cooling and heating equipment investment combination based on the power coordination and associated inertia data of each cooling and heating equipment group in the corresponding production instance. Electrical coordination correlates inertial data;

Among them, multi-dimensional personalized power consumption inertia data includes: first personalized power consumption inertia data, second personalized power consumption inertia data, and personalized power consumption coordination related inertia data.

Preferably, the power supply and distribution plan generation module includes:

The investment situation determination sub-module is used to determine the target complete hot and cold equipment investment combination for the target investment situation of the current production instance;

The first power consumption prediction sub-module is used to retrieve the first personalized power consumption inertia data of each heat treatment furnace object in the heat treatment furnace input combination that is consistent with the target heat treatment furnace input combination from the multi-dimensional personalized power consumption inertia data. , as the first predicted power consumption data corresponding to the target heat treatment furnace object;

The second power consumption prediction sub-module is used to retrieve the second personalized power consumption of each cooling cycle system object in the cooling cycle system input combination that is consistent with the target cooling cycle system input combination from the multi-dimensional personalized power consumption inertia data. Inertial data is used as the second predicted power consumption data corresponding to the target cooling cycle system;

The bidirectional correction submodule is used to compare the first predicted power consumption data and the personalized power consumption coordination and correlation inertia data of each cooling and heating equipment group in the target complete cooling and heating equipment input combination in the multi-dimensional personalized power consumption inertia data. The second predicted power consumption data is subjected to two-way correction to obtain the final predicted power consumption data of each target heat treatment furnace and target cooling cycle system;

The power supply and distribution plan generation submodule is used to generate generator coordinated power supply and distribution plans based on the final forecast power consumption data.

Preferably, the bidirectional syndrome module includes:

The first calculation unit is used to substitute the first predicted power consumption data of each target heat treatment furnace into the linear relationship corresponding to the personalized power consumption coordination associated inertia data of the corresponding heating and cooling equipment group in the target complete cooling and heating equipment input combination, Obtain the third predicted power consumption data corresponding to the target cooling cycle system;

The second calculation unit is used to substitute the second predicted power consumption data corresponding to each target cooling cycle system into the linear linear power consumption coordination correlation inertia data corresponding to the personalized power consumption coordination of the corresponding cooling and heating equipment group in the target complete cooling and heating equipment input combination. relationship to obtain the fourth predicted power consumption data corresponding to the target cooling cycle system;

The final prediction unit is used to regard the larger value of the first predicted power consumption data and the fourth predicted power consumption data of each target heat treatment furnace as the final predicted power consumption data of the corresponding target heat treatment furnace. At the same time, each cooling The larger value of the second predicted power consumption data and the third predicted power consumption data of the circulation system is regarded as the final predicted power consumption data corresponding to the cooling circulation system.

The beneficial effects of the present invention that are different from the prior art are: based on the multi-dimensional correlation periodic analysis of the historical power consumption data of each heat treatment furnace and cooling circulation system in each production instance, it is determined that the heat treatment furnace and cooling circulation system are Multi-dimensional power consumption inertia data under different input conditions during the generation process, and further analysis of the multi-dimensional power consumption inertia data based on the complete cooling and heating equipment investment combination corresponding to the same input situation, to obtain different complete heat treatment furnaces and cooling circulation systems. Personalized power consumption inertia data when cooling and heating equipment is put into the combination (i.e., multi-dimensional personalized power consumption inertia data), and further generates a reasonable power supply and distribution plan for the generator based on the target investment situation and multi-dimensional personalized power consumption inertia data , realize the intelligent and flexible power supply and distribution of the heat treatment furnace and cooling cycle system by the generator to meet the corresponding power demand when the heat treatment furnace and cooling cycle system are put into different situations, thereby reducing the unnecessary occupation of the generator and reducing waste.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention will be further described in detail below through the accompanying drawings and examples.

Description of the drawings

The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. In the attached picture:

Figure 1 is a schematic diagram of a generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system in an embodiment of the present invention;

Figure 2 is a schematic diagram of another generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system in an embodiment of the present invention;

Figure 3 is a schematic diagram of yet another generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system in the embodiment of the present invention.

Detailed ways

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

Example 1:

The present invention provides an intelligent power supply and distribution system for generators that coordinates the power demand of the cooling cycle system. Referring to Figure 1, it includes:

The power consumption record acquisition module is used to extract the first historical power consumption data of each heat treatment furnace and the second historical power consumption data of each cooling cycle system from the historical power consumption data of each production instance;

The power consumption inertia analysis module is used to perform multi-dimensional correlation periodic analysis on the first power consumption data and the second power consumption data of each production instance to obtain multi-dimensional power consumption inertia data;

Personalized inertia analysis module, which is used based on multi-dimensional power inertia data and the investment status of heat treatment furnaces and cooling circulation systems in each production instance (that is, the heat treatment furnace objects and cooling circulation systems that are put into the production process in each production instance Object, because there are differences in electricity demand between heat treatment furnaces and cooling cycle systems, so the heat treatment furnace object and the cooling cycle system object represent a specific heat treatment furnace and cooling cycle system), Analyze the multi-dimensional personalized power inertia data of each complete hot and cold equipment input combination (that is, the combination of all heat treatment furnaces and cooling circulation systems put into the production process in a single production instance);

The power supply and distribution plan generation module is used to generate a generator coordinated power supply and distribution plan based on the target input of the current production instance;

The intelligent power supply and distribution module is used to supply and distribute power based on the generator's coordinated power supply and distribution plan and obtain intelligent power supply and distribution results.

In this embodiment, the production workshop of the graphene thermal conductive film of the present invention contains multiple heating power heat treatment furnaces, and during the production process, each heat treatment furnace needs to be equipped with a cooling circulation system to cool the outer surface temperature of the furnace. The production needs of each production instance are different, resulting in different heat treatment furnaces and cooling circulation systems put into each production instance. Among them, the cooling circulation system can use cooling circulation methods such as circulating cooling water.

In this embodiment, the dimensions in the multi-dimensional correlation periodic analysis include: the dimension of the heat treatment furnace, the dimension of the cooling cycle system, the dimension of the correlation relationship between the dimension of the heat treatment furnace and the dimension of the cooling cycle system;

Correlation periodicity analysis is to align the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system, consider the periodicity of both, and determine the periodic change characteristics of both of them. cycle, and use the changing characteristics of the first power consumption data and the second power consumption data within a single cycle as well as the changing characteristics of the linear relationship between the first power consumption data and the second power consumption data within a single cycle as multi-dimensional Electrical inertia data.

In this embodiment, the production instance is an instance record containing relevant data of the production plan of the entire graphene thermal conductive film. The completed production instance will record the power consumption data of each heat treatment furnace and each cooling circulation system. The completed production instance does not have the power consumption data of each heat treatment furnace and each cooling cycle system, but there are heat treatment furnace objects and cooling cycle system objects that need to be put into production in this production instance.

In this embodiment, the multi-dimensional personalized power consumption inertia data includes: the power consumption inertia data corresponding to each heat treatment furnace under the input conditions corresponding to each complete combination of cooling and heating equipment, and the corresponding power consumption inertia data for each cooling cycle system under each complete input combination. The corresponding power inertia data under the input conditions corresponding to the input combination of cold and hot equipment, and the linear relationship between the heat treatment furnace and cooling circulation system used in each combination correspond to the input conditions corresponding to each complete combination of cold and heating equipment. inertia data;

In this embodiment, the inertial data is the average of all original sample data of the corresponding type, for example:

The corresponding power inertia data of each heat treatment furnace under the input conditions corresponding to each complete combination of cooling and heating equipment is: all the power consumption data of each heat treatment furnace under the input conditions corresponding to each complete combination of cooling and heating equipment. Curve data obtained after aligning and averaging the power consumption curves corresponding to the first historical power consumption data.

In this embodiment, the current production instance needs to use the steps in this embodiment to generate its generator coordinated power supply and distribution plan in advance, and then perform power supply and distribution based on the generator coordinated power supply and distribution plan to complete the current production instance.

In this embodiment, the target investment situation is based on the heat treatment furnace objects and cooling circulation system objects that need to be put into production to complete the current production instance determined in the current production instance.

In this embodiment, the generator coordinated power supply and distribution plan includes a real-time change curve of the power that needs to be provided for each heat treatment furnace and each cooling cycle system included in the target input situation during the completion of the current production instance.

In this embodiment, the intelligent power supply and distribution result is the result of power supply and distribution for each heat treatment furnace and each cooling cycle system included in the target investment situation based on the coordinated power supply and distribution plan of the generator.

The beneficial effect of the above technology is: based on the multi-dimensional correlation periodic analysis of the historical power consumption data of each heat treatment furnace and cooling cycle system in each production instance, the differences in the generation process of the heat treatment furnace and cooling cycle system are determined. Multi-dimensional power consumption inertia data under the same investment situation, and further analysis of the multi-dimensional power consumption inertia data based on the complete cooling and heating equipment investment combination corresponding to the same investment situation, to obtain the different complete cooling and heating equipment investment combinations of different heat treatment furnaces and cooling circulation systems Zhongshi's personalized power consumption inertia data (i.e., multi-dimensional personalized power consumption inertia data), and further generates a reasonable power supply and distribution plan for the generator based on the target investment situation and multi-dimensional personalized power consumption inertia data, so as to realize the generator's Intelligent and flexible power supply and distribution of heat treatment furnaces and cooling circulation systems can meet the corresponding power demand when the heat treatment furnaces and cooling circulation systems are put into different situations, thereby reducing redundant occupation of generators and reducing waste.

Example 2:

Based on Embodiment 1, the electrical inertia analysis module, with reference to Figure 2, includes:

The period division submodule is used to divide the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system used in combination in the same production instance (that is, to divide the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system). After the second power consumption data of the cooling cycle system is aligned, the periodicity of both is considered simultaneously, a period that satisfies the periodic change characteristics of both is determined, and based on this period, the first power consumption data and the second power consumption data are aligned. The power consumption data is divided into periods), and the first single-cycle power consumption data of multiple cycles of the heat treatment furnace (the power consumption data obtained after periodic division of the first power consumption data) and the second single-cycle power consumption data of the cooling cycle system are obtained. Electricity data (data obtained after periodic division of the second electricity consumption data);

The first power consumption data and the second power consumption data are divided into related periods, so that the time span of the first single-cycle power consumption data and the second single-cycle power consumption data are consistent, which facilitates subsequent analysis of the linearity between the two. relation;

The inertia analysis sub-module is used to average all single-cycle power consumption data of the same power consumption object in the same production instance (that is, align the abscissa of the power consumption change curves corresponding to all single-cycle power consumption data and then perform the ordinate Add and average to obtain a new power consumption curve), and obtain the power consumption inertia data of each power consumption object in each production instance;

Based on the average results of all single-cycle power consumption data after period division of the same power-consuming object in the same production instance, the inertial distribution characteristics of the power consumption of the corresponding power-consuming object in a single period in the corresponding production instance are realized, where, inertia Distribution characteristics are universal characteristics that can represent changes in electricity consumption over time within a single period;

The correlation analysis submodule is used to compare the linear relationship between the first single-cycle power consumption data of the heat treatment furnace used in each production instance and the second single-cycle power consumption data of the cooling cycle system (expressed by a functional relationship, heat treatment The electricity consumption in the first single-cycle electricity consumption data of the furnace is the independent variable of the function corresponding to the linear relationship, and the electricity consumption in the second single-cycle electricity consumption data of the cooling cycle system is the dependent variable of the function corresponding to the linear relationship) , as the power consumption coordination associated inertia data of the heat treatment furnace and cooling circulation system used in combination in the corresponding production instance;

Among them, electricity consumption objects include: heat treatment furnace and cooling circulation system;

The single-cycle power consumption data includes: the first single-cycle power consumption data and the second single-cycle power consumption data;

Multi-dimensional power consumption inertia data includes: power consumption inertia data and power consumption coordination related inertia data.

The beneficial effect of the above technology is: by aligning and averaging the power consumption data of the heat treatment furnace and the cooling cycle system after the correlation period division, the general characteristics of the power consumption changing with time within a single cycle can be analyzed (i.e. Electricity consumption inertia data) and the linear relationship between the electricity consumption data of the combined heat treatment furnace and cooling circulation system within a single cycle.

Example 3:

Based on Embodiment 2, the period division sub-module, with reference to Figure 3, includes:

The equipment association unit is used to associate the heat treatment furnace and the cooling circulation system used together in the same production instance to obtain at least one set of hot and cold equipment groups (that is, the equipment combination including the heat treatment furnace and the cooling circulation system used together in the production instance) ;

The periodic analysis unit is used to perform periodic analysis on the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling circulation system included in the cooling and heating equipment group (analyzing the power consumption change curve corresponding to the power consumption data). Periodicity), determine the first power consumption cycle of the heat treatment furnace (that is, the cycle obtained after periodic analysis of the first power consumption data) and the second power consumption cycle of the cooling cycle system (that is, the periodic analysis of the second power consumption data the period obtained later);

The comprehensive cycle determination unit is used to regard the least common multiple of the first power consumption cycle and the second power consumption cycle as the comprehensive power consumption cycle of the corresponding hot and cold equipment group in the corresponding production instance (that is, satisfying both the first power consumption data and the second power consumption cycle). 2. The period of the periodic change characteristics of the electricity consumption data).

The beneficial effect of the above technology is that the common multiple of the first power consumption cycle of the first power consumption data and the second power consumption cycle of the second power consumption data is regarded as the comprehensive power consumption cycle, so that the first power consumption cycle is calculated based on the comprehensive power consumption cycle. Both the electricity data and the single-cycle electricity consumption data after period division of the second electricity consumption data satisfy the periodicity of the original electricity consumption data to which they belong.

Example 4:

Based on Embodiment 3, the period analysis unit includes:

The target determination subunit is used to use the power consumption change curve corresponding to the first power consumption data of the heat treatment furnace included in the cooling and heating equipment group and the power consumption change curve corresponding to the second power consumption data of the cooling cycle system as targets. curve;

Accuracy determination subunit, used to determine the first interval time of all adjacent peaks in the target curve (that is, the time interval between adjacent power consumption peaks in the target curve) and the second interval time of all adjacent valleys (i.e., the time interval between adjacent power consumption valleys in the target curve), and perform correlation denoising on the first interval and the second interval (i.e., based on the abnormal peaks and valleys contained in the power consumption data). Time series correlation characteristics, denoising the first power consumption data and the second power consumption data), and obtaining multiple comprehensive interval times (that is, the remaining time after correlating and denoising all the first interval time and the second interval time in the target curve) the first interval time and the second interval time), based on the mean value and analysis accuracy of all comprehensive interval times in the target curve (that is, the preset minimum unit used when performing periodic analysis of the first power consumption data and the second power consumption data Divide the time interval. The smaller the time interval corresponding to the analysis accuracy, the higher the period analysis accuracy of the two, and the higher the period analysis accuracy, and vice versa) to determine the local division interval (finally, the first power consumption data and the duration of the power consumption data of the minimum time unit divided when performing periodic analysis of the second power consumption data);

The periodic determination subunit is used to locally divide the target curve based on the local division interval and perform adjacent cluster analysis (adjacent cluster analysis is based on the existing cluster analysis method, among the clusters obtained during each division process The included objects are all adjacent to each other, that is, there are always other objects adjacent to the objects contained in the cluster in the same cluster), and the first power consumption cycle of the heat treatment furnace or the second power consumption cycle of the cooling cycle system is determined.

In this embodiment, because when abnormal power consumption occurs in the heat treatment furnace, the cooling circulation system used in combination with it will also have a high probability of abnormal power consumption. Therefore, based on the time series correlation characteristics of abnormal peak and valley values contained in its power consumption data, The first power consumption data and the second power consumption data are denoised to ensure the denoising accuracy.

The beneficial effect of the above technology is: based on the correlation denoising of the interval time between adjacent peaks and the interval between adjacent valleys in the first power consumption data and the second power consumption data, the interval time can be realized. Accurate denoising, and because the interval between adjacent peaks or adjacent valleys has a high probability to be within the period obtained by periodic analysis (because if there is a peak or valley in one cycle, it will also exist in the adjacent cycle) peak and valley values, and the timing values of their occurrence are similar), combined with the preset analysis accuracy to determine the local division interval that represents the duration of the power consumption data of the minimum time unit divided during period analysis, ensuring the accuracy of later period analysis and the accuracy of the period division results. Subsequently, based on adjacent cluster analysis, the final period division of the first power consumption data and the second power consumption data can be achieved.

Example 5:

Based on Embodiment 4, the accuracy determination subunit includes:

The change point detection end is used to determine the first mutation point in all first intervals of the target curve based on the change point detection algorithm (for example, using Bayesian Online Changepoint Detection) (that is, all the first mutation points in the target curve). The interval time that suddenly changes suddenly in the sequence generated by the first interval time according to its determined order) and the second mutation point among all the second interval times in the target curve (that is, all the second interval times in the target curve according to its determined order) The interval between sudden changes in the generated sequence);

The ordinal determination end is used to determine the first ordinal number of the later peak among all the peaks of the target curve among the adjacent peaks corresponding to the first mutation point, and at the same time, determine the first ordinal number among the adjacent valleys corresponding to the second mutation point. The lower-order valley is the second-order number among all the valleys in the target curve;

The associated denoising end is used to determine the first ordinal number q1 and the second ordinal number q2 that is greater than the first ordinal number and has the smallest difference with the first ordinal number (where q2>q1, and the difference between q1 and q2 is less than q1 and every other ordinal number) Whether the ordinal difference (that is, the difference between q2 and q1) between the second ordinal numbers) is within the ordinal difference threshold (when screening the first interval time and the corresponding second interval time that need to be denoised and deleted, based on The ordinal difference between the two is within the preset maximum allowed value), if so, the first interval time corresponding to the corresponding first mutation point and the second gradient time corresponding to the corresponding second mutation point will be deleted at the same time, Treat all remaining first and second intervals as all combined intervals;

The sequence generation end is used for sequence generation to determine the common factor of the average of all comprehensive interval times and the duration of all target curves (the common factor determined here essentially represents the duration), and the common factor is changed from large to Small sorting to obtain the common factor sequence;

The interval determination end is used to compare the sorting order in the common factor sequence with the preset minimum number of cluster analysis times (the minimum number of cluster analysis times is preset, and the minimum number of cluster analysis times is determined during the periodic analysis process. The minimum number of division processes that need to be performed before the power cycle) The same common factor is regarded as the maximum duration, and the ratio of the mean value of the comprehensive interval time to the duration corresponding to the analysis accuracy is regarded as the minimum duration. Based on the minimum duration and The maximum interval determines the local division interval.

The beneficial effects of the above technology are: detecting all mutation points in the first interval and all mutation points in the second interval through the change point detection algorithm, and further based on the first interval and the second interval corresponding to the mutation point. The ordinal difference between the second interval time and the second interval time is realized to realize the correlation constraint and denoising of the second interval time, which further ensures the denoising accuracy. The common factor of the mean value of the interval time and the duration of the target curve ensures that subsequent determination is based on this. The results of cluster analysis on the local division intervals can accurately form the duration of a single period, and ensure that the subsequent cluster analysis results of the sub-target curves obtained based on the local division intervals can be guaranteed to be evenly divided by the target curve. The common factor whose sorting order in the factor sequence is the same as the preset minimum number of cluster analysis times is regarded as the maximum duration, ensuring that the final determined local division interval is enough for the target curve to be divided by the minimum number of cluster analysis times. The ratio of the mean value of the comprehensive interval time to the duration corresponding to the analysis accuracy is regarded as the minimum duration, which can ensure that the determined local division interval meets the preset analysis accuracy, and then based on the above steps, the local division interval can be reasonably determined.

Example 6:

Based on Embodiment 4, the period determination subunit includes:

The local analysis end is used to locally divide the target curve based on the local division interval to obtain multiple sub-target curves (that is, obtained by dividing the target curve, the duration of the sub-target curve is the same as the duration of the local division interval) and the corresponding local Divide the interval (that is, the duration of the sub-target curve), and calculate the first-order derivative integral value of each sub-target curve in the corresponding local divided interval (that is, use the function f ( t) represents, where t is the time variable in the sub-objective curve, and the integral value of the first-order derivative function f′(t) in the local divided interval [t1, t2] is calculated. as the first derivative integral value);

The cluster analysis end is used to perform adjacent cluster analysis on the first-order derivative integral values of all sub-target curves in the target curve to obtain the adjacent cluster division results (the bell cluster division results include multiple clusters, each cluster The total number of first-order derivative integral values contained in is the same, and the sub-target curves corresponding to the first-order derivative values contained in each cluster are adjacent in the target curve);

The period determination end is used to connect the local division intervals of the sub-target curves corresponding to the first-order derivative integral values belonging to the same cluster in the adjacent clustering division results to obtain the single-period division interval, and divide the single-period division of each target curve The duration of the interval is regarded as the first power consumption cycle corresponding to the heat treatment furnace or the second power consumption cycle of the cooling cycle system (when the target curve is the power consumption change curve corresponding to the first power consumption data, then the single cycle of the target curve The duration of the divided interval is regarded as the first power consumption cycle of the corresponding heat treatment furnace; when the target curve is the power consumption change curve corresponding to the second power consumption data, the duration of the single-cycle divided interval of the target curve is regarded as the corresponding cooling The second power cycle of the circulating system).

The beneficial effects of the above technology are as follows: the target curve is locally divided based on the local division interval to obtain the sub-target curve, and the sub-target curves in the target curve are adjacent based on the first-order derivative integral value of the sub-target curve at the corresponding local division interval. Cluster analysis makes the final result obtained by summarizing all the sub-target curves contained in different clusters to have a higher degree of similarity in the power consumption change characteristics, which further ensures the final determined power consumption cycle. accuracy.

Example 7:

Based on Example 6, the cluster analysis end includes:

The sequential summary sub-terminal is used to summarize the first-order derivative integral values of all sub-target curves in the target curve in sequence to obtain a sequence of integral values;

The cluster analysis sub-end is used to divide the multiple sequence based on the preset incremental increase (that is, from the first common factor in the common factor sequence in Embodiment 5 to the common factor equal to the preset minimum number of cluster analysis times) The division multiples in the sequence (the sequence of all common factors sorted from small to large) (the values are the common factors included in the previously preset gradually increasing division multiple sequence), and the integral value sequence is divided in sequence (that is, the integral value sequence is divided into individual The number of average equal parts is the same as the division multiple. For example, if the division multiple is 3, then the integral value sequence is divided into 3 integral value clusters containing the same total number of integral values), and multiple integral value clusters for each division multiple are obtained ( The total number of integral values contained in the integral value cluster is equal to the ratio of the total number of integral values contained in the integral value sequence to the division multiple). At the same time, the similarity of all integral value clusters for each division multiple is calculated until the When the similarity is less than the similarity obtained in the last division process, the integrated value cluster obtained in the last division process is regarded as the adjacent cluster division result.

In this embodiment, the similarity of all integrated value clusters for each division multiple is calculated, including:

Determine the two currently calculated integral value clusters among all the integral value clusters of the divided multiples, and calculate the difference between the two first-order derivative integral values with equal numbers in the two integral value clusters and the mean of the two first-order derivative integral values. The ratio is regarded as the deviation degree corresponding to the two first-order derivative integral values. The difference between 1 and the deviation degree is regarded as the similarity corresponding to the two first-order derivative integral values. The average of the similarity of the first-order derivative integral values is regarded as the similarity of the corresponding two integral value clusters, and the average of the similarity of all two integral value clusters contained in all the integral value clusters of the divided multiple is regarded as the current The similarity of all clusters of integral values that divide the multiples.

The beneficial effect of the above technology is: a preset incremental division based on all common factors from the first common factor in the common factor sequence in Embodiment 5 to the common factor that is numerically equal to the preset minimum number of cluster analysis times. Multiple sequence, divide the integral value sequence in turn and calculate the similarity of all the integral value clusters obtained by the division, until the similarity reaches the peak, so that the division result meets the periodicity of the target curve to the greatest extent.

Example 8:

Based on Embodiment 1, the personalized inertial analysis module includes:

The first analysis sub-module is used to analyze the first power consumption inertia data of each heat treatment furnace in each production instance when the complete hot and cold equipment corresponding to the corresponding production instance is put into combination. Personalized power consumption inertia data (that is, aligning the power consumption change curves corresponding to all the first power consumption inertia data of the same heat treatment furnace in the same production instance with the same complete combination of hot and cold equipment, and aligning them at the same moment The maximum power consumption is regarded as the personalized inertia power consumption of the corresponding heat treatment furnace at the corresponding time, and a personalized inertia power consumption change curve is fitted based on the personalized inertia power consumption at all times in a single cycle, as the corresponding The first personalized power inertia data of the heat treatment furnace when the complete cooling and heating equipment is put into combination);

The second analysis sub-module is used to analyze the performance of each cooling cycle system object when the complete cooling and heating equipment corresponding to the corresponding production instance is put into combination based on the second power inertia data of each cooling cycle system in each production instance. The second personalized power consumption inertia data (that is, aligning the power consumption change curves corresponding to all the second power consumption inertia data of the same cooling cycle system in the same production instance with the same complete combination of cooling and heating equipment, and will be in The maximum power consumption at the same time is regarded as the personalized inertia power consumption of the corresponding cooling cycle system at the corresponding time, and a personalized inertia power consumption change curve is fitted based on the personalized inertia power consumption at all times in a single cycle. As the second personalized power inertia data of the corresponding cooling cycle system when the corresponding complete cooling and heating equipment is put into combination);

The third analysis sub-module is used to analyze the personalized usage of each cooling and heating equipment group in each complete cooling and heating equipment investment combination based on the power coordination and associated inertia data of each cooling and heating equipment group in the corresponding production instance. Electricity coordination associated inertia data (that is, aligning the function curves corresponding to the linear relationships of all the electricity coordination associated inertia data of the same cooling and heating equipment group in the same production instance with the same complete cooling and heating equipment input combination, and aligning them in the same The average value of the dependent variable value corresponding to the independent variable value in all function curves is regarded as the final dependent variable value corresponding to the independent variable value, and a new dependent variable value is fitted based on the final dependent variable value corresponding to all independent variable values in a single period. Function curve, and the linear relationship corresponding to the new function curve is regarded as the personalized power coordination associated inertial data of the corresponding cooling and heating equipment group when the corresponding complete cooling and heating equipment is put into combination);

Among them, multi-dimensional personalized power consumption inertia data includes: first personalized power consumption inertia data, second personalized power consumption inertia data, and personalized power consumption coordination related inertia data.

The beneficial effect of the above technology is: by putting the complete cooling and heating equipment into the same production instance, the first power inertia data and the second power inertia data are separately summarized and aligned to take the maximum value, ensuring that the final determined One personalized power consumption inertia data and the second personalized power consumption inertia data can meet all the power consumption needs of the heat treatment furnace, and the power consumption coordination associated inertia data in the production instance with the same complete combination of hot and cold equipment is averaged. The finally obtained personalized power consumption coordination and correlation inertia data can satisfy the correlation constraints of the power consumption of the heat treatment furnace and the cooling circulation system to the greatest extent.

Example 9:

Based on Embodiment 8, the power supply and distribution plan generation module includes:

The investment situation determination sub-module is used to determine the target complete hot and cold equipment investment combination of the target investment situation of the current production instance (that is, based on the heat treatment furnace objects and cooling cycles that need to be put into production to complete the current production instance determined in the current production instance) A combination of system objects);

The first power consumption prediction sub-module is used to retrieve the first personalized power consumption inertia data of each heat treatment furnace object in the heat treatment furnace input combination that is consistent with the target heat treatment furnace input combination from the multi-dimensional personalized power consumption inertia data. , as the first predicted power consumption data corresponding to the target heat treatment furnace object;

The second power consumption prediction sub-module is used to retrieve the second personalized power consumption of each cooling cycle system object in the cooling cycle system input combination that is consistent with the target cooling cycle system input combination from the multi-dimensional personalized power consumption inertia data. Inertial data is used as the second predicted power consumption data corresponding to the target cooling cycle system;

The bidirectional correction submodule is used to compare the first predicted power consumption data and the personalized power consumption coordination and correlation inertia data of each cooling and heating equipment group in the target complete cooling and heating equipment input combination in the multi-dimensional personalized power consumption inertia data. The second predicted power consumption data is subjected to two-way correction to obtain the final predicted power consumption data of each target heat treatment furnace and target cooling cycle system (that is, the predicted power consumption data of each target heat treatment furnace and target cooling cycle system obtained after two-way correction are obtained in a single Electricity consumption data during the period);

The power supply and distribution plan generation sub-module is used to generate a generator coordinated power supply and distribution plan based on the final predicted power consumption data (based on the current production instance, the operation time of each target heat treatment furnace and the target cooling cycle system is determined, based on the operation The ratio of the duration and the comprehensive power consumption cycle determines the number of operating cycles. The power consumption change curve corresponding to the final predicted power consumption data corresponding to the number of operating cycles of the target heat treatment furnace is fitted to obtain the power consumption plan corresponding to the target heat treatment furnace. , generate a generator coordinated power supply and distribution plan based on the power consumption plans of all target heat treatment furnaces and target cooling cycle systems. In the final step, the generator coordinated power supply and distribution plan can be determined by considering power supply and distribution losses).

The beneficial effects of the above technology are: retrieval of multi-dimensional personalized power consumption inertia data based on the target input status of the current production instance to achieve preliminary prediction of power consumption data, and then further combined with the target cooling cycle system input combination to achieve the preliminary forecast of power consumption. The two-way correction of electricity data ensures that the determined final predicted electricity consumption data can reduce redundant occupancy on the premise of meeting its electricity demand, and further ensures the rationality of the subsequently generated generator coordinated power supply and distribution plan, reducing Eliminates standby waste and generator occupancy.

Example 10:

Based on Embodiment 9, the bidirectional syndrome module includes:

The first calculation unit is used to substitute the first predicted power consumption data of each target heat treatment furnace into the linear relationship corresponding to the personalized power consumption coordination associated inertia data of the corresponding heating and cooling equipment group in the target complete cooling and heating equipment input combination, Obtain the third predicted power consumption data corresponding to the target cooling cycle system;

The second calculation unit is used to substitute the second predicted power consumption data corresponding to each target cooling cycle system into the linear linear power consumption coordination correlation inertia data corresponding to the personalized power consumption coordination of the corresponding cooling and heating equipment group in the target complete cooling and heating equipment input combination. relationship to obtain the fourth predicted power consumption data corresponding to the target cooling cycle system;

The final prediction unit is used to regard the larger value of the first predicted power consumption data and the fourth predicted power consumption data of each target heat treatment furnace as the final predicted power consumption data of the corresponding target heat treatment furnace. At the same time, each cooling The larger value of the second predicted power consumption data and the third predicted power consumption data of the circulation system is regarded as the final predicted power consumption data corresponding to the cooling circulation system.

The beneficial effect of the above technology is: by substituting the initially predicted first predicted power consumption data and the second predicted power consumption data into the personalized power consumption coordination correlation inertia of the corresponding cooling and heating equipment group in the target complete cooling and heating equipment investment combination. The new predicted power consumption data is determined by the linear relationship corresponding to the data, and the larger value is taken between it and the initially determined predicted power consumption data, further ensuring that the final predicted power consumption data determined can meet the actual possible attainment. maximum electricity demand.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. In this way, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims (10)

1. The generator intelligent power supply and distribution system that coordinates the power demand of the cooling cycle system is characterized by: The power consumption record acquisition module is used to extract the first historical power consumption data of each heat treatment furnace and the second historical power consumption data of each cooling cycle system from the historical power consumption data of each production instance; The power consumption inertia analysis module is used to perform multi-dimensional correlation periodic analysis on the first power consumption data and the second power consumption data of each production instance to obtain multi-dimensional power consumption inertia data; The personalized inertia analysis module is used to analyze the multi-dimensional personalized power inertia of each complete cooling and heating equipment investment combination based on multi-dimensional power inertia data and the investment in heat treatment furnaces and cooling circulation systems in each production instance. data; The power supply and distribution plan generation module is used to generate a generator coordinated power supply and distribution plan based on the target input of the current production instance; The intelligent power supply and distribution module is used to supply and distribute power based on the generator's coordinated power supply and distribution plan and obtain intelligent power supply and distribution results. 2. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 1, characterized in that the power inertia analysis module includes: The period division sub-module is used to divide the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system used in combination in the same production instance into associated periods, and obtain the first single cycle of multiple cycles of the heat treatment furnace. Power consumption data and the second single-cycle power consumption data of the cooling cycle system; The inertia analysis sub-module is used to average all single-cycle power consumption data of the same power consumption object in the same production instance to obtain the power consumption inertia data of each power consumption object in each production instance; The correlation analysis submodule is used to treat the linear relationship between the first single-cycle power consumption data of the combined heat treatment furnace and the second single-cycle power consumption data of the cooling cycle system in each production instance as a combined heat treatment Coordination of associated inertia data for power consumption of furnaces and cooling circuit systems in corresponding production instances; Among them, electricity consumption objects include: heat treatment furnace and cooling circulation system; The single-cycle power consumption data includes: the first single-cycle power consumption data and the second single-cycle power consumption data; Multi-dimensional power consumption inertia data includes: power consumption inertia data and power consumption coordination related inertia data. 3. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 2, characterized in that the periodic sub-module includes: An equipment association unit is used to associate heat treatment furnaces and cooling circulation systems used in combination in the same production instance to obtain at least one set of hot and cold equipment groups; A periodic analysis unit, used to perform periodic analysis on the first power consumption data of the heat treatment furnace and the second power consumption data of the cooling cycle system included in the cold and hot equipment group, and determine the first power consumption cycle and cooling cycle of the heat treatment furnace. The second power cycle of the system; The comprehensive cycle determination unit is used to regard the least common multiple of the first power cycle and the second power cycle as the comprehensive power cycle of the corresponding hot and cold equipment group in the corresponding production instance. 4. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 3, characterized in that the period analysis unit includes: The target determination subunit is used to use the power consumption change curve corresponding to the first power consumption data of the heat treatment furnace included in the cooling and heating equipment group and the power consumption change curve corresponding to the second power consumption data of the cooling cycle system as targets. curve; The accuracy determination subunit is used to determine the first interval time of all adjacent peaks and the second interval time of all adjacent valleys in the target curve, perform correlation denoising on the first interval time and the second interval time, and obtain multiple A comprehensive interval time, and the local division interval is determined based on the mean value and analysis accuracy of all comprehensive interval times in the target curve; The period determination subunit is used to locally divide the target curve based on the local division interval and perform adjacent cluster analysis to determine the first power cycle of the heat treatment furnace or the second power cycle of the cooling cycle system. 5. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 4, characterized in that the accuracy determination subunit includes: The change point detection end is used to determine the first mutation point in all the first intervals of the target curve and the second mutation point in all the second intervals of the target curve based on the change point detection algorithm; The first ordinal number among all the peaks of the target curve, and at the same time, determine the second ordinal number among all the valley values of the target curve of the later valley value among the adjacent valley values corresponding to the second mutation point; The associated denoising end is used to determine whether the ordinal difference between the first ordinal number and the second ordinal number that is greater than the first ordinal number and has the smallest difference with the first ordinal number is within the ordinal difference threshold. If so, the corresponding first ordinal number is The first interval time corresponding to the mutation point and the second gradient time corresponding to the second mutation point are deleted at the same time, and all remaining first interval times and second interval times are regarded as all comprehensive interval times; The sequence generation end is used for sequence generation to determine the common factors of the average of all comprehensive interval times and the duration of all target curves, and sort the common factors from large to small to obtain a common factor sequence; The interval determination end is used to regard the common factor in the common factor sequence whose sorting order is the same as the preset minimum number of cluster analysis times as the maximum duration, and the ratio of the mean value of the comprehensive interval time to the duration corresponding to the analysis accuracy as the minimum Duration, the local division interval is determined based on the minimum duration and maximum interval time. 6. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 4, characterized in that the period determination subunit includes: The local analysis end is used to locally divide the target curve based on the local division interval, obtain multiple sub-target curves and corresponding local division intervals, and calculate the first-order derivative integral value of each sub-target curve in the corresponding local division interval; The cluster analysis end is used to perform adjacent cluster analysis on the first-order derivative integral values of all sub-target curves in the target curve to obtain the adjacent cluster division results; The period determination end is used to connect the local division intervals of the sub-target curves corresponding to the first-order derivative integral values belonging to the same cluster in the adjacent clustering division results to obtain the single-period division interval, and divide the single-period division of each target curve The duration of the interval is regarded as corresponding to the first power cycle of the heat treatment furnace or the second power cycle of the cooling cycle system. 7. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 6, characterized in that the cluster analysis end includes: The sequential summary sub-terminal is used to summarize the first-order derivative integral values of all sub-target curves in the target curve in sequence to obtain a sequence of integral values; The cluster analysis sub-end is used to divide the integral value sequence sequentially based on the division multiples in the preset gradually increasing division multiple sequence to obtain multiple integral value clusters for each division multiple. At the same time, calculate the The similarity of all integrated value clusters, until the similarity obtained by this division process is less than the similarity obtained by the previous division process, then the integrated value cluster obtained by the previous division process will be regarded as the adjacent cluster division result. 8. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 1, characterized in that the personalized inertia analysis module includes: The first analysis sub-module is used to analyze the first power consumption inertia data of each heat treatment furnace in each production instance when the complete hot and cold equipment corresponding to the corresponding production instance is put into combination. Personalized power consumption inertia data; The second analysis sub-module is used to analyze the performance of each cooling cycle system object when the complete cooling and heating equipment corresponding to the corresponding production instance is put into combination based on the second power inertia data of each cooling cycle system in each production instance. Second personalized power consumption inertia data; The third analysis sub-module is used to analyze the personalized usage of each cooling and heating equipment group in each complete cooling and heating equipment investment combination based on the power coordination and associated inertia data of each cooling and heating equipment group in the corresponding production instance. Electrical coordination correlates inertial data; Among them, multi-dimensional personalized power consumption inertia data includes: first personalized power consumption inertia data, second personalized power consumption inertia data, and personalized power consumption coordination related inertia data. 9. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 8, characterized in that the power supply and distribution plan generation module includes: The investment situation determination sub-module is used to determine the target complete hot and cold equipment investment combination for the target investment situation of the current production instance; The first power consumption prediction sub-module is used to retrieve the first personalized power consumption inertia data of each heat treatment furnace object in the heat treatment furnace input combination that is consistent with the target heat treatment furnace input combination from the multi-dimensional personalized power consumption inertia data. , as the first predicted power consumption data corresponding to the target heat treatment furnace object; The second power consumption prediction sub-module is used to retrieve the second personalized power consumption of each cooling cycle system object in the cooling cycle system input combination that is consistent with the target cooling cycle system input combination from the multi-dimensional personalized power consumption inertia data. Inertial data is used as the second predicted power consumption data corresponding to the target cooling cycle system; The bidirectional correction submodule is used to compare the first predicted power consumption data and the personalized power consumption coordination and correlation inertia data of each cooling and heating equipment group in the target complete cooling and heating equipment input combination in the multi-dimensional personalized power consumption inertia data. The second predicted power consumption data is subjected to two-way correction to obtain the final predicted power consumption data of each target heat treatment furnace and target cooling cycle system; The power supply and distribution plan generation submodule is used to generate generator coordinated power supply and distribution plans based on the final forecast power consumption data. 10. The generator intelligent power supply and distribution system for coordinating the power demand of the cooling cycle system according to claim 9, characterized in that the bidirectional syndrome sub-module includes: The first calculation unit is used to substitute the first predicted power consumption data of each target heat treatment furnace into the linear relationship corresponding to the personalized power consumption coordination associated inertia data of the corresponding heating and cooling equipment group in the target complete cooling and heating equipment input combination, Obtain the third predicted power consumption data corresponding to the target cooling cycle system; The second calculation unit is used to substitute the second predicted power consumption data corresponding to each target cooling cycle system into the linear linear power consumption coordination correlation inertia data corresponding to the personalized power consumption coordination of the corresponding cooling and heating equipment group in the target complete cooling and heating equipment input combination. relationship to obtain the fourth predicted power consumption data corresponding to the target cooling cycle system; The final prediction unit is used to regard the larger value of the first predicted power consumption data and the fourth predicted power consumption data of each target heat treatment furnace as the final predicted power consumption data of the corresponding target heat treatment furnace. At the same time, each cooling The larger value of the second predicted power consumption data and the third predicted power consumption data of the circulation system is regarded as the final predicted power consumption data corresponding to the cooling circulation system.