Liquid cooling energy storage application analysis method and system based on multiple scenes

Abstract

The invention discloses a liquid cooling energy storage application analysis method and system based on multiple scenes, and the method comprises the steps: constructing a three-dimensional visual energy storage model according to the arrangement information of an energy storage region, and carrying out the layout analysis and region division; thirdly, setting a multi-scene energy storage task plan, collecting and analyzing temperature control data of each energy storage analysis area, judging the correlation of temperature control change characteristics through a Pearson coefficient, and screening out a temperature control influence area; then, real-time temperature control data is obtained based on the real-time energy storage plan, and a core high-temperature area is determined; and simulating a heat path in the energy storage model by using an ant colony algorithm, and generating heat prediction information. And finally, performing heat distribution prediction and comprehensive temperature control analysis on the energy storage area based on prediction information, and making a regional temperature control scheme through the liquid cooling temperature control platform to realize effective temperature control management of the energy storage area.

Description

Technical Field

[0001] The present invention relates to the field of energy storage heat dissipation analysis, and more specifically, to a liquid cooling energy storage application analysis method and system based on multiple scenarios. Background Art

[0002] As the demand for energy storage continues to increase, battery energy storage is also developing. Liquid-cooled energy storage technology has attracted widespread attention for its efficient heat dissipation, energy saving and environmental protection. The liquid-cooled energy storage system removes the heat generated by the battery or energy storage element through liquid circulation, effectively improving the performance and reliability of the energy storage system.

[0003] However, in traditional energy storage temperature control analysis, temperature control is often performed based on manual monitoring of temperature control data, which is time-consuming, labor-intensive and inefficient. In addition, there is a lack of prediction of the heat distribution of the energy storage system and efficient liquid cooling control methods, resulting in low efficiency of liquid cooling control and insufficient intelligent monitoring of the energy storage system. Therefore, there is an urgent need for a method and system that can comprehensively, real-timely and accurately analyze and control the temperature of energy storage equipment. Summary of the invention

[0004] The present invention overcomes the defects of the prior art and proposes a liquid cooling energy storage application analysis method and system based on multiple scenarios.

[0005] The first aspect of the present invention provides a liquid cooling energy storage application analysis method based on multiple scenarios, including: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

[0006] In this solution, S1 is specifically: Obtaining energy storage area layout information; The energy storage area layout information includes the energy storage area, area outline, area equipment layout information, and energy storage equipment quantity information; A three-dimensional visualization-based energy storage model is constructed according to the energy storage area layout information.

[0007] In this solution, S2 is specifically: Through the energy storage model, the layout of the energy storage equipment is analyzed, the distribution of the energy storage units is analyzed, multiple energy storage analysis areas are divided in the energy storage model, and it is ensured that each energy storage analysis area includes a preset number of energy storage units.

[0008] In this solution, S3 is specifically: Set up energy storage task plans in multiple scenarios. During the task plan period, collect temperature control data of multiple energy storage analysis areas through the temperature control monitoring platform. The temperature control data includes the temperature value information of the corresponding time period, the operation information of the liquid cooling equipment, and the temperature distribution information; The energy storage task plan includes the discharge and charging task processes of the energy storage equipment; According to the temperature control data, the corresponding temperature values ​​are sorted based on the time dimension, and the value change characteristics are analyzed based on the linear change calculation to generate the temperature control change characteristics.

[0009] In this solution, S4 is specifically: The energy storage analysis area is used as the analysis unit; Marking a temperature control change feature of an energy storage analysis area as first feature data; Marking the temperature control change characteristics corresponding to the plurality of adjacent energy storage analysis areas in the one energy storage analysis area as second characteristic data to obtain a plurality of second characteristic data; Performing a linear correlation analysis based on the Pearson coefficient on the first feature data and each second feature data to obtain a plurality of Pearson coefficients; Determine whether each Pearson coefficient is within a preset coefficient range. If so, it means that the energy storage analysis area has a high influence relationship with the corresponding adjacent energy storage analysis area, and count the number K of adjacent energy storage analysis areas with a high influence relationship; If K is greater than a predetermined number, the one energy storage analysis area is marked as a temperature control influence area.

[0010] In this solution, S5 is specifically: Based on the current real-time energy storage plan, the temperature control monitoring platform collects real-time temperature control data of multiple energy storage analysis areas; Analyze temperature changes based on real-time temperature control data and calculate the temperature growth rate and maximum temperature value; It is determined whether the temperature growth rate and the maximum temperature value of each energy storage analysis area are both greater than a preset range. If so, the energy storage analysis area is marked as a core high temperature area.

[0011] In this solution, S6 is specifically: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to build an ant colony model, which is used to simulate the ant colony operation path; Each energy storage analysis area is used as a moving path point, and each moving path point has multiple direction movement options; A first pheromone and a second pheromone are set, and the value of the first pheromone is smaller than that of the second pheromone; Initialize the pheromone of each moving path point based on the first pheromone; If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone; A preset number of ant colonies is set, and the core high-temperature area is used as the starting point of the ant colony. A preset number of ant colonies is set for each core high-temperature area. The ant colony makes path decisions based on pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition of each path simulation is that the ant colony movement path reaches the preset maximum length. Based on the ant colony model, the heat path simulation is performed, the corresponding movement path is recorded, and the high-frequency path is screened out from the movement path as the heat prediction simulation path; Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0012] In this solution, S7 is specifically: Analyze the distribution and trend of heat in each energy storage analysis area based on heat forecast information; The liquid cooling temperature control platform is used to predict heat distribution and conduct comprehensive temperature control analysis, and set the liquid cooling temperature control priority table and temperature control parameters for each energy storage analysis area; Generate a regional temperature control solution based on the liquid cooling temperature control priority table and temperature control parameters for application.

[0013] The second aspect of the present invention further provides a liquid-cooled energy storage application analysis system based on multiple scenarios, the system comprising: a memory, a processor, the memory comprising a liquid-cooled energy storage application analysis program based on multiple scenarios, the liquid-cooled energy storage application analysis program based on multiple scenarios being executed by the processor to implement the following steps: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

[0014] The third aspect of the present invention also provides a computer-readable storage medium, which includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in any one of the above items are implemented.

[0015] The present invention discloses a liquid-cooled energy storage application analysis method and system based on multiple scenarios. First, a three-dimensional visual energy storage model is constructed according to the energy storage area layout information, and layout analysis and area division are performed. Next, a multi-scenario energy storage task plan is set, and the temperature control data of each energy storage analysis area is collected and analyzed. The correlation of the temperature control change characteristics is determined by the Pearson coefficient, and the temperature control impact area is screened out. Then, real-time temperature control data is obtained based on the real-time energy storage plan to determine the core high-temperature area. The ant colony algorithm is used to simulate the heat path in the energy storage model to generate heat prediction information. Finally, based on the prediction information, the energy storage area is subjected to heat distribution prediction and comprehensive temperature control analysis, and a regional temperature control plan is formulated through a liquid-cooled temperature control platform to achieve effective temperature control management of the energy storage area. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a liquid cooling energy storage application analysis method based on multiple scenarios of the present invention is shown; Figure 2 A block diagram of a liquid-cooled energy storage application analysis system based on multiple scenarios of the present invention is shown. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flow chart of a liquid cooling energy storage application analysis method based on multiple scenarios of the present invention is shown.

[0020] like Figure 1 As shown, the first aspect of the present invention provides a liquid cooling energy storage application analysis method based on multiple scenarios, including: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

[0021] According to an embodiment of the present invention, the S1 is specifically: Obtaining energy storage area layout information; The energy storage area layout information includes the energy storage area, area outline, area equipment layout information, and energy storage equipment quantity information; A three-dimensional visualization-based energy storage model is constructed according to the energy storage area layout information.

[0022] It should be noted that the energy storage area can be a container-type battery energy storage area, which includes a variety of energy storage related equipment, such as battery system devices, temperature control monitoring devices, liquid cooling devices, etc. The energy storage model can be used to visually monitor the energy storage area and conduct regional temperature control analysis.

[0023] According to an embodiment of the present invention, S2 is specifically: Through the energy storage model, the layout of the energy storage equipment is analyzed, the distribution of the energy storage units is analyzed, multiple energy storage analysis areas are divided in the energy storage model, and it is ensured that each energy storage analysis area includes a preset number of energy storage units.

[0024] It should be noted that the energy storage unit is preset information, such as a preset battery energy storage unit. One unit includes a preset number of batteries. The area is divided by the energy storage unit, which can achieve precise temperature control analysis and liquid cooling regulation in the subsequent process.

[0025] According to an embodiment of the present invention, S3 is specifically: Set up energy storage task plans in multiple scenarios. During the task plan period, collect temperature control data of multiple energy storage analysis areas through the temperature control monitoring platform. The temperature control data includes the temperature value information of the corresponding time period, the operation information of the liquid cooling equipment, and the temperature distribution information; The energy storage task plan includes the discharge and charging task processes of the energy storage equipment; According to the temperature control data, the corresponding temperature values ​​are sorted based on the time dimension, and the value change characteristics are analyzed based on the linear change calculation to generate the temperature control change characteristics.

[0026] It should be noted that the energy storage task plan can be a plan list with multi-scenario tasks selected by historical tasks, including multiple energy storage tasks in the plan, and analyzing the temperature changes in multiple scenarios by collecting corresponding temperature control data. Each energy storage analysis area corresponds to a temperature control change feature.

[0027] According to an embodiment of the present invention, S4 is specifically: The energy storage analysis area is used as the analysis unit; Marking a temperature control change feature of an energy storage analysis area as first feature data; Marking the temperature control change characteristics corresponding to the plurality of adjacent energy storage analysis areas in the one energy storage analysis area as second characteristic data to obtain a plurality of second characteristic data; Performing a linear correlation analysis based on the Pearson coefficient on the first feature data and each second feature data to obtain a plurality of Pearson coefficients; Determine whether each Pearson coefficient is within a preset coefficient range. If so, it means that the energy storage analysis area has a high influence relationship with the corresponding adjacent energy storage analysis area, and count the number K of adjacent energy storage analysis areas with a high influence relationship; If K is greater than a predetermined number, the one energy storage analysis area is marked as a temperature control influence area.

[0028] It should be noted that the predetermined number is generally 2. For one energy storage analysis area, the number of calculated Pearson coefficients is equal to the number of adjacent energy storage analysis areas. An energy storage analysis area may have multiple adjacent energy storage analysis areas.

[0029] It can be understood here that the present invention analyzes the change characteristics in the historical temperature control data, analyzes the temperature control change relationship of adjacent areas based on each energy storage analysis area, determines whether there is an influence relationship through linear correlation, and screens out the positions that have a high influence on the adjacent areas, and marks the temperature control influence area. This area has certain temperature control change characteristics and influence characteristics in the energy storage system, and is more likely to affect the heat trend. In the subsequent ant colony algorithm simulation process, corresponding parameter dynamic corrections are required to simulate the actual heat trend.

[0030] According to an embodiment of the present invention, S5 is specifically: Based on the current real-time energy storage plan, the temperature control monitoring platform collects real-time temperature control data of multiple energy storage analysis areas; Analyze temperature changes based on real-time temperature control data and calculate the temperature growth rate and maximum temperature value; It is determined whether the temperature growth rate and the maximum temperature value of each energy storage analysis area are both greater than a preset range. If so, the energy storage analysis area is marked as a core high temperature area.

[0031] It should be noted that the preset range includes a preset maximum temperature and a maximum temperature growth rate.

[0032] According to an embodiment of the present invention, S6 is specifically: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to build an ant colony model, which is used to simulate the ant colony operation path; Each energy storage analysis area is used as a moving path point, and each moving path point has multiple direction movement options; A first pheromone and a second pheromone are set, and the value of the first pheromone is smaller than that of the second pheromone; Initialize the pheromone of each moving path point based on the first pheromone; If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone; A preset number of ant colonies is set, and the core high-temperature area is used as the starting point of the ant colony. A preset number of ant colonies is set for each core high-temperature area. The ant colony makes path decisions based on pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition of each path simulation is that the ant colony movement path reaches the preset maximum length. Based on the ant colony model, the heat path simulation is performed, the corresponding movement path is recorded, and the high-frequency path is screened out from the movement path as the heat prediction simulation path; Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0033] It should be noted that the area, path, and model size of the ant colony model are consistent with those of the energy storage model. After simulating the path through the ant colony model, the corresponding path information can be generated by path mapping from the energy storage model. Each energy storage analysis area corresponds one-to-one to each mobile path point. In the improved ant colony model of the present invention, no end point is set, and multi-directional path selection is performed on the ant colony based on the starting point. For the initial state pheromone, a lower pheromone is set for the non-temperature control affected area, and a higher pheromone is set for the temperature control affected area, so that the improved ant colony model is more in line with the actual heat trend law, and a precise heat distribution model and heat path trend prediction are realized. Regional heat prediction information includes information such as the heat trend, temperature change, and heat distribution in each energy storage analysis area.

[0034] According to an embodiment of the present invention, S7 is specifically: Analyze the distribution and trend of heat in each energy storage analysis area based on heat forecast information; The liquid cooling temperature control platform is used to predict heat distribution and conduct comprehensive temperature control analysis, and set the liquid cooling temperature control priority table and temperature control parameters for each energy storage analysis area; Generate a regional temperature control solution based on the liquid cooling temperature control priority table and temperature control parameters for application.

[0035] It should be noted that the liquid cooling temperature control priority table includes regional priority temperature control information.

[0036] It is worth mentioning here that in traditional energy storage temperature control analysis, control is often based on manual monitoring of temperature control data, which is time-consuming, labor-intensive and inefficient. In addition, there is a lack of heat distribution prediction for the energy storage system and efficient liquid cooling control methods, resulting in low liquid cooling control efficiency and insufficient intelligent monitoring of the energy storage system.

[0037] Based on this, the present invention analyzes the temperature control characteristics of the corresponding energy storage area, predicts the heat trend through the ant colony algorithm, and improves the relevant parameters of the algorithm model based on the existing temperature control characteristics. Different pheromones can be defined based on the characteristics of different regions to simulate a more realistic heat trend. Through heat prediction, the corresponding temperature control scheme is set on the liquid cooling temperature control platform, and the energy storage area is effectively predicted and regulated in an overall and regional manner, realizing the overall and efficient temperature control management application of the energy storage equipment, improving the intelligent temperature control, and realizing the efficient operation and optimized control of the liquid cooling equipment. At the same time, the visual prediction and analysis of the temperature control simulation is realized.

[0038] Liquid cooling equipment generally consists of a circulating pump, a compressor, liquid cooling pipes, a liquid cooling unit, etc.

[0039] According to an embodiment of the present invention, it also includes: In the first energy storage task cycle, obtain the corresponding heat prediction information and actual temperature control data; Based on the heat prediction information and actual temperature control data, the temperature control prediction accuracy is evaluated for each energy storage analysis area to obtain multiple prediction accuracy rates; Calculate the average of multiple prediction accuracy rates to obtain the comprehensive prediction accuracy; If the comprehensive prediction accuracy is higher than the set accuracy, then in the second energy storage task cycle, the ant colony model parameters are dynamically adjusted, the ant colony parameters of the first energy storage task cycle are used, and the preset number of simulations is reduced by percentage; If the comprehensive prediction accuracy is lower than the set accuracy, the temperature control influence area and the core high temperature area are reset, and the first pheromone, the second pheromone, and the starting point parameters of the ant colony model are dynamically adjusted.

[0040] It should be noted that the present invention compares the characteristics of the predicted and actual temperature control data of the completed energy storage task cycle, and dynamically adjusts the ant colony for multiple future cycles through prediction accuracy analysis. If the accuracy meets expectations, the model data can be streamlined, the data analysis efficiency can be improved, and the process optimization of the ant colony model can be achieved. While ensuring the accuracy of heat prediction, the amount of data analyzed by the model is greatly reduced, and the data processing pressure of the server is reduced. If it does not meet expectations, the corresponding model parameters are reset based on the most recent temperature control data, and the ant colony model is dynamically adjusted so that the prediction process can be dynamically adapted based on the actual operation of the energy storage system. At the same time, through the above process, efficient simulation in multiple scenarios can be effectively achieved, the server operation efficiency can be improved, and the system operation efficiency of the temperature control monitoring and liquid cooling platform can be improved.

[0041] The percentage reduction of the preset number of simulations can be set to 10% to 50% to dynamically adjust the efficiency of the ant colony algorithm while ensuring the prediction accuracy.

[0042] Figure 2 A block diagram of a liquid-cooled energy storage application analysis system based on multiple scenarios of the present invention is shown.

[0043] The second aspect of the present invention further provides a liquid-cooled energy storage application analysis system 2 based on multiple scenarios, the system comprising: a memory 21, a processor 22, the memory 21 comprising a liquid-cooled energy storage application analysis program based on multiple scenarios, the liquid-cooled energy storage application analysis program based on multiple scenarios being executed by the processor 22 to implement the following steps: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

[0044] According to an embodiment of the present invention, the S1 is specifically: Obtaining energy storage area layout information; The energy storage area layout information includes the energy storage area, area outline, area equipment layout information, and energy storage equipment quantity information; A three-dimensional visualization-based energy storage model is constructed according to the energy storage area layout information.

[0045] It should be noted that the energy storage area can be a container-type battery energy storage area, which includes a variety of energy storage related equipment, such as battery system devices, temperature control monitoring devices, liquid cooling devices, etc. The energy storage model can be used to visually monitor the energy storage area and conduct regional temperature control analysis.

[0046] According to an embodiment of the present invention, S2 is specifically: Through the energy storage model, the layout of the energy storage equipment is analyzed, the distribution of the energy storage units is analyzed, multiple energy storage analysis areas are divided in the energy storage model, and it is ensured that each energy storage analysis area includes a preset number of energy storage units.

[0047] It should be noted that the energy storage unit is preset information, such as a preset battery energy storage unit. One unit includes a preset number of batteries. The area is divided by the energy storage unit, which can achieve precise temperature control analysis and liquid cooling regulation in the subsequent process.

[0048] According to an embodiment of the present invention, S3 is specifically: Set up energy storage task plans in multiple scenarios. During the task plan period, collect temperature control data of multiple energy storage analysis areas through the temperature control monitoring platform. The temperature control data includes the temperature value information of the corresponding time period, the operation information of the liquid cooling equipment, and the temperature distribution information; The energy storage task plan includes the discharge and charging task processes of the energy storage equipment; According to the temperature control data, the corresponding temperature values ​​are sorted based on the time dimension, and the value change characteristics are analyzed based on the linear change calculation to generate the temperature control change characteristics.

[0049] It should be noted that the energy storage task plan can be a plan list with multi-scenario tasks selected by historical tasks, including multiple energy storage tasks in the plan, and analyzing the temperature changes in multiple scenarios by collecting corresponding temperature control data. Each energy storage analysis area corresponds to a temperature control change feature.

[0050] According to an embodiment of the present invention, S4 is specifically: The energy storage analysis area is used as the analysis unit; Marking a temperature control change feature of an energy storage analysis area as first feature data; Marking the temperature control change characteristics corresponding to the plurality of adjacent energy storage analysis areas in the one energy storage analysis area as second characteristic data to obtain a plurality of second characteristic data; Performing a linear correlation analysis based on the Pearson coefficient on the first feature data and each second feature data to obtain a plurality of Pearson coefficients; Determine whether each Pearson coefficient is within a preset coefficient range. If so, it means that the energy storage analysis area has a high influence relationship with the corresponding adjacent energy storage analysis area, and count the number K of adjacent energy storage analysis areas with a high influence relationship; If K is greater than a predetermined number, the one energy storage analysis area is marked as a temperature control influence area.

[0051] It should be noted that the predetermined number is generally 2. For one energy storage analysis area, the number of calculated Pearson coefficients is equal to the number of adjacent energy storage analysis areas. An energy storage analysis area may have multiple adjacent energy storage analysis areas.

[0052] It can be understood here that the present invention analyzes the change characteristics in the historical temperature control data, analyzes the temperature control change relationship of adjacent areas based on each energy storage analysis area, determines whether there is an influence relationship through linear correlation, and screens out the positions that have a high influence on the adjacent areas, and marks the temperature control influence area. This area has certain temperature control change characteristics and influence characteristics in the energy storage system, and is more likely to affect the heat trend. In the subsequent ant colony algorithm simulation process, corresponding parameter dynamic corrections are required to simulate the actual heat trend.

[0053] According to an embodiment of the present invention, S5 is specifically: Based on the current real-time energy storage plan, the temperature control monitoring platform collects real-time temperature control data of multiple energy storage analysis areas; Analyze temperature changes based on real-time temperature control data and calculate the temperature growth rate and maximum temperature value; It is determined whether the temperature growth rate and the maximum temperature value of each energy storage analysis area are both greater than a preset range. If so, the energy storage analysis area is marked as a core high temperature area.

[0054] It should be noted that the preset range includes a preset maximum temperature and a maximum temperature growth rate.

[0055] According to an embodiment of the present invention, S6 is specifically: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to build an ant colony model, which is used to simulate the ant colony operation path; Each energy storage analysis area is used as a moving path point, and each moving path point has multiple direction movement options; A first pheromone and a second pheromone are set, and the value of the first pheromone is smaller than that of the second pheromone; Initialize the pheromone of each moving path point based on the first pheromone; If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone; A preset number of ant colonies is set, and the core high-temperature area is used as the starting point of the ant colony. A preset number of ant colonies is set for each core high-temperature area. The ant colony makes path decisions based on pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition of each path simulation is that the ant colony movement path reaches the preset maximum length. Based on the ant colony model, the heat path simulation is performed, the corresponding movement path is recorded, and the high-frequency path is screened out from the movement path as the heat prediction simulation path; Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0056] It should be noted that the area, path, and model size of the ant colony model are consistent with those of the energy storage model. After simulating the path through the ant colony model, the corresponding path information can be generated by path mapping from the energy storage model. Each energy storage analysis area corresponds one-to-one to each mobile path point. In the improved ant colony model of the present invention, no end point is set, and multi-directional path selection is performed on the ant colony based on the starting point. For the initial state pheromone, a lower pheromone is set for the non-temperature control affected area, and a higher pheromone is set for the temperature control affected area, so that the improved ant colony model is more in line with the actual heat trend law, and a precise heat distribution model and heat path trend prediction are realized. Regional heat prediction information includes information such as the heat trend, temperature change, and heat distribution in each energy storage analysis area.

[0057] According to an embodiment of the present invention, S7 is specifically: Analyze the distribution and trend of heat in each energy storage analysis area based on heat forecast information; The liquid cooling temperature control platform is used to predict heat distribution and conduct comprehensive temperature control analysis, and set the liquid cooling temperature control priority table and temperature control parameters for each energy storage analysis area; Generate a regional temperature control solution based on the liquid cooling temperature control priority table and temperature control parameters for application.

[0058] It should be noted that the liquid cooling temperature control priority table includes regional priority temperature control information.

[0059] It is worth mentioning here that in traditional energy storage temperature control analysis, control is often based on manual monitoring of temperature control data, which is time-consuming, labor-intensive and inefficient. In addition, there is a lack of heat distribution prediction for the energy storage system and efficient liquid cooling control methods, resulting in low liquid cooling control efficiency and insufficient intelligent monitoring of the energy storage system.

[0060] Based on this, the present invention analyzes the temperature control characteristics of the corresponding energy storage area, predicts the heat trend through the ant colony algorithm, and improves the relevant parameters of the algorithm model based on the existing temperature control characteristics. Different pheromones can be defined based on the characteristics of different regions to simulate a more realistic heat trend. Through heat prediction, the corresponding temperature control scheme is set on the liquid cooling temperature control platform, and the energy storage area is effectively predicted and regulated in an overall and regional manner, realizing the overall and efficient temperature control management application of the energy storage equipment, improving the intelligent temperature control, and realizing the efficient operation and optimized control of the liquid cooling equipment. At the same time, the visual prediction and analysis of the temperature control simulation is realized.

[0061] Liquid cooling equipment generally consists of a circulating pump, a compressor, liquid cooling pipes, a liquid cooling unit, etc.

[0062] The third aspect of the present invention also provides a computer-readable storage medium, which includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in any one of the above items are implemented.

[0063] The present invention discloses a liquid-cooled energy storage application analysis method and system based on multiple scenarios. First, a three-dimensional visual energy storage model is constructed according to the energy storage area layout information, and layout analysis and area division are performed. Next, a multi-scenario energy storage task plan is set, and the temperature control data of each energy storage analysis area is collected and analyzed. The correlation of the temperature control change characteristics is determined by the Pearson coefficient, and the temperature control impact area is screened out. Then, real-time temperature control data is obtained based on the real-time energy storage plan to determine the core high-temperature area. The ant colony algorithm is used to simulate the heat path in the energy storage model to generate heat prediction information. Finally, based on the prediction information, the energy storage area is subjected to heat distribution prediction and comprehensive temperature control analysis, and a regional temperature control plan is formulated through a liquid-cooled temperature control platform to achieve effective temperature control management of the energy storage area.

[0064] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0065] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0066] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0067] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0068] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0069] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A liquid cooling energy storage application analysis method based on multiple scenarios, characterized in that: include: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

2. According to claim 1, a liquid cooling energy storage application analysis method based on multiple scenarios is characterized in that: The S1 is specifically: Obtaining energy storage area layout information; The energy storage area layout information includes the energy storage area, area outline, area equipment layout information, and energy storage equipment quantity information; A three-dimensional visualization-based energy storage model is constructed according to the energy storage area layout information.

3. According to the method for analyzing liquid cooling energy storage applications in multiple scenarios in claim 1, it is characterized in that: The S2 is specifically: Through the energy storage model, the layout of the energy storage equipment is analyzed, the distribution of the energy storage units is analyzed, multiple energy storage analysis areas are divided in the energy storage model, and it is ensured that each energy storage analysis area includes a preset number of energy storage units.

4. According to the method for analyzing liquid cooling energy storage applications in multiple scenarios in claim 1, it is characterized in that: The S3 is specifically: Set up energy storage task plans in multiple scenarios. During the task plan period, collect temperature control data of multiple energy storage analysis areas through the temperature control monitoring platform. The temperature control data includes the temperature value information of the corresponding time period, the operation information of the liquid cooling equipment, and the temperature distribution information; The energy storage task plan includes the discharge and charging task processes of the energy storage equipment; According to the temperature control data, the corresponding temperature values ​​are sorted based on the time dimension, and the value change characteristics are analyzed based on the linear change calculation to generate the temperature control change characteristics.

5. The method for analyzing liquid cooling energy storage applications based on multiple scenarios according to claim 4 is characterized in that: The S4 is specifically: The energy storage analysis area is used as the analysis unit; Marking a temperature control change feature of an energy storage analysis area as first feature data; Marking the temperature control change characteristics corresponding to the plurality of adjacent energy storage analysis areas in the one energy storage analysis area as second characteristic data to obtain a plurality of second characteristic data; Performing a linear correlation analysis based on the Pearson coefficient on the first feature data and each second feature data to obtain a plurality of Pearson coefficients; Determine whether each Pearson coefficient is within a preset coefficient range. If so, it means that the energy storage analysis area has a high influence relationship with the corresponding adjacent energy storage analysis area, and count the number K of adjacent energy storage analysis areas with a high influence relationship; If K is greater than a predetermined number, the one energy storage analysis area is marked as a temperature control influence area.

6. The method for analyzing liquid cooling energy storage applications based on multiple scenarios according to claim 5 is characterized in that: The S5 is specifically: Based on the current real-time energy storage plan, the temperature control monitoring platform collects real-time temperature control data of multiple energy storage analysis areas; Analyze temperature changes based on real-time temperature control data and calculate the temperature growth rate and maximum temperature value; It is determined whether the temperature growth rate and the maximum temperature value of each energy storage analysis area are both greater than a preset range. If so, the energy storage analysis area is marked as a core high temperature area.

7. The method for analyzing liquid cooling energy storage applications based on multiple scenarios according to claim 6 is characterized in that: The S6 is specifically: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to build an ant colony model, which is used to simulate the ant colony operation path; Each energy storage analysis area is used as a moving path point, and each moving path point has multiple direction movement options; A first pheromone and a second pheromone are set, and the value of the first pheromone is smaller than that of the second pheromone; Initialize the pheromone of each moving path point based on the first pheromone; If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone; A preset number of ant colonies is set, and the core high-temperature area is used as the starting point of the ant colony. A preset number of ant colonies is set for each core high-temperature area. The ant colony makes path decisions based on pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition of each path simulation is that the ant colony movement path reaches the preset maximum length. Based on the ant colony model, the heat path simulation is performed, the corresponding movement path is recorded, and the high-frequency path is screened out from the movement path as the heat prediction simulation path; Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

8. The method for analyzing liquid cooling energy storage applications based on multiple scenarios according to claim 1 is characterized in that: The S7 is specifically: Analyze the distribution and trend of heat in each energy storage analysis area based on heat forecast information; The liquid cooling temperature control platform is used to predict heat distribution and conduct comprehensive temperature control analysis, and set the liquid cooling temperature control priority table and temperature control parameters for each energy storage analysis area; Generate a regional temperature control solution based on the liquid cooling temperature control priority table and temperature control parameters for application.

9. A liquid cooling energy storage application analysis system based on multiple scenarios, characterized in that: The system includes: a memory and a processor, wherein the memory includes a liquid cooling energy storage application analysis program based on multiple scenarios, and when the liquid cooling energy storage application analysis program based on multiple scenarios is executed by the processor, the following steps are implemented: S1: Obtaining energy storage area layout information, and constructing an energy storage model based on three-dimensional visualization according to the energy storage area layout information; S2: Analyze the layout of energy storage equipment through the energy storage model, and divide the overall energy storage area into regions to obtain multiple energy storage analysis regions; S3: Setting energy storage task plans under multiple scenarios. Within the task plans, using the temperature control monitoring platform, collecting temperature control data of multiple energy storage analysis areas, performing linear change analysis on the temperature control data, and obtaining temperature control change characteristics; S4: analyzing whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and the adjacent energy storage analysis areas, the correlation judgment is analyzed based on the Pearson coefficient, and the temperature control affected area is screened out from the energy storage analysis area and marked; S5: Based on the real-time energy storage plan, obtain real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out core high-temperature areas; S6: According to the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the moving range to construct an ant colony model, each energy storage analysis area is used as a moving path point, and the pheromone of each moving path point is initialized based on the first pheromone. If the moving path point corresponds to the temperature control influence area, the corresponding moving path point is initialized based on the second pheromone, and the core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path; S7: Based on the heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis on multiple energy storage analysis areas, and generate regional temperature control solutions through the liquid cooling temperature control platform.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios are implemented as described in any one of claims 1 to 8.

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