Efficient energy-saving power distribution method and system for shelter hospital

Abstract

The invention provides an efficient energy-saving power distribution method and system for a shelter hospital. Wherein the real-time power utilization demand of each power utilization unit of the shelter hospital is collected by utilizing a sensor network, and an integrated energy demand report is generated by combining external meteorological conditions; based on the comprehensive energy demand report, automatically adjusting a power distribution proportion by using a mixed integer linear programming algorithm, and generating a key equipment priority list by using an analytic hierarchy process; based on the key equipment priority list, analyzing a topological structure by using a network flow optimization algorithm based on a graph theory, evaluating the comfort level of a user by using a fuzzy logic technology, and generating an optimized power dispatching plan; and based on the optimized power dispatching plan, regularly examining the power distribution effect, optimizing the power distribution model, and generating the efficient and energy-saving power distribution system. According to the technical scheme, the power distribution proportion can be adjusted in real time, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of power distribution, and in particular, to a high-efficiency and energy-saving power distribution method and system for square cabin hospitals. Background Art

[0002] With the widespread use of Fangcang shelter hospitals in responding to public health emergencies, efficient and energy-saving power distribution has become a key issue that needs to be urgently addressed. The power system must be able to efficiently and reliably meet the power needs of various medical equipment and living facilities. In addition, since Fangcang shelter hospitals are usually located in areas with limited resources, how to make full use of renewable energy and reduce dependence on traditional energy has become an important technical requirement. At the same time, the power system also needs to be highly flexible and adaptable to cope with the dynamic changes in power demand and the uncertainty of the external environment.

[0003] At present, the power distribution of Fangcang Hospital mainly relies on traditional manual dispatching and simple automated control systems. These systems usually manage power supply through preset power distribution ratios and fixed dispatching strategies; although these methods can meet basic power demand to a certain extent, they have obvious shortcomings in energy efficiency optimization and resource utilization.

[0004] Traditional power distribution methods are unable to adjust the power distribution ratio in real time, resulting in inefficient energy utilization and increased energy waste; manual scheduling and fixed strategies are difficult to quickly respond to dynamic changes in power demand, which may lead to unstable power supply for key equipment and affect the normal development of medical work; the existing power distribution system lacks intelligent optimization algorithms, cannot make full use of renewable energy, and cannot be flexibly adjusted according to external meteorological conditions; traditional power distribution methods fail to fully consider user comfort, which may lead to poor user experience; manual scheduling and fixed strategies require more manpower maintenance, increasing operating costs; the existing system lacks a regular review and optimization mechanism, and it is difficult to make continuous improvements based on user feedback and system operation data, resulting in long-term performance degradation of the system. Summary of the invention

[0005] The embodiments of the present application provide a high-efficiency and energy-saving power distribution method and system for square cabin hospitals, which are used to solve the problem in the prior art that the power distribution ratio cannot be adjusted in real time, resulting in low energy utilization efficiency.

[0006] In a first aspect, an embodiment of the present application provides a highly efficient and energy-saving power distribution method for a square cabin hospital, comprising:

[0007] The sensor network is used to collect the real-time power demand of each power-consuming unit in the Fangcang Hospital, and combined with external meteorological conditions, a comprehensive energy demand report is generated;

[0008] Based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio by combining the actual power load and renewable energy output, and a hierarchical analysis method is used to evaluate the importance of each power unit to generate a priority list of key equipment;

[0009] Based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the topology of the power network inside the Fangcang Cabin Hospital, adjust the power dispatching strength, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan;

[0010] Based on the optimized power dispatch plan, the power distribution effect is regularly reviewed, user feedback and system operation data are collected, the power distribution model is optimized, and an efficient and energy-saving power distribution system is generated.

[0011] Optionally, based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio in combination with actual power load and renewable energy output, and a hierarchical analysis method is used to evaluate the importance of each power unit to generate a priority list of key equipment, including:

[0012] Based on the comprehensive energy demand report, combined with actual electricity load and renewable energy output, the power distribution ratio is optimized and an optimized power distribution plan is generated;

[0013] Based on the optimized power distribution scheme, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power consumption unit to generate an optimal power distribution strategy;

[0014] Based on the optimal power distribution strategy, a hierarchical analysis method is used to pre-set an evaluation index system to evaluate the importance of each power consumption unit and generate a quantitative importance score;

[0015] Based on the quantitative importance scores, a priority list of key equipment is generated by sorting in descending order of scores.

[0016] Optionally, based on the optimized power distribution scheme, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power consumption unit to generate an optimal power distribution strategy, including:

[0017] Based on the optimized power distribution scheme, configuring input parameters of a mixed integer linear programming algorithm to generate an input data set;

[0018] Based on the input data set, a mixed integer linear programming algorithm is used to set an objective function, control the total energy cost, maximize the utilization rate of renewable energy, and generate an electricity distribution optimization model;

[0019] Based on the power distribution optimization model, the optimal solution is automatically found through a solution algorithm to generate a power distribution optimization result;

[0020] Based on the power distribution optimization results, the specific power distribution ratio of each power consumption unit is analyzed and adjusted to ensure the feasibility and practicality of the distribution plan and generate the optimal power distribution strategy.

[0021] Optionally, based on the input data set, a mixed integer linear programming algorithm is used to set an objective function, control the total energy cost, maximize the utilization rate of renewable energy, and generate an electricity distribution optimization model, including:

[0022] Based on the input data set, verification and integration are performed via a central data processing system;

[0023] Deeply mine the key statistical features of the input data set and perform multi-dimensional time series analysis to generate the total energy cost;

[0024] The total energy cost is calculated using the following formula:

[0025]

[0026] Among them, C total is the total energy cost; α is the power purchase cost coefficient of the power grid; P grid,t is the amount of electricity purchased from the power grid at time t; β is the cost coefficient of renewable energy generation; P renewable,t is the power generation of renewable energy at time t; γ is the nonlinear adjustment coefficient of the cost of renewable energy power generation as the power generation increases; δ is the charging and discharging cost coefficient of the energy storage system; P storage,t is the charge and discharge capacity of the energy storage system at time t; η is the load cost coefficient; P load,t is the total load at time t; t is the index of the time period, from 1 to T; T is the total number of time periods;

[0027] Based on the total energy cost, a multi-dimensional system stability analysis is performed, and a nonlinear transformation is performed by introducing multiple reward and penalty mechanisms to generate an objective function;

[0028] The objective function is calculated using the following formula:

[0029]

[0030] Among them, F is the objective function; C total is the total energy cost; ω is the renewable energy utilization rate reward coefficient; θ is the renewable energy utilization rate attenuation coefficient, which is used to simulate the phenomenon of diminishing marginal benefits as the renewable energy utilization rate increases; μ is the exponential attenuation coefficient of renewable energy utilization, which further simulates the diminishing marginal benefits under high utilization rate; φ is the load deviation penalty coefficient; Pload,i is the load of the ith power unit; P renewable,i is the renewable energy power allocated to the i-th power unit; λ is the load deviation adjustment coefficient, which is used to balance the impact of load deviation; v is the load fluctuation adjustment coefficient, which is used to consider the impact of load fluctuation on penalty; P max is the maximum load; t is the index of the time period, from 1 to T; T is the total number of time periods; i is the index of the power unit, from 1 to N; N is the total number of power units;

[0031] Based on the objective function, relevant constraints are integrated as input configurations, and the optimal solution is output through the branch and bound method to ensure that the utilization rate of renewable energy is maximized while controlling the total energy cost, and generate a power distribution optimization model.

[0032] Optionally, based on the optimal power distribution strategy, the hierarchical analysis method is used to pre-set an evaluation index system to evaluate the importance of each power consumption unit and generate a quantitative importance score, including:

[0033] Based on the optimal power distribution strategy, analyze the power distribution of each power consumption unit in different time periods to generate power demand characteristic data;

[0034] Based on the power demand characteristic data, a multi-dimensional evaluation index system is pre-set to ensure that the evaluation standards are comprehensive and scientific and to generate an evaluation index system framework;

[0035] Based on the evaluation index system framework, the hierarchical analysis method is used to construct a judgment matrix for pairwise comparison analysis, evaluate the relative importance of each index, and generate relative weight coefficients;

[0036] Based on the relative weight coefficient, the overall importance of each power consumption unit is quantified, and the specific performance of different evaluation indicators of each power consumption unit is comprehensively evaluated to generate a quantitative importance score.

[0037] Optionally, based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the topology of the power network inside the square cabin hospital, adjust the power dispatching strength, and use fuzzy logic technology to evaluate user comfort to generate an optimized power dispatching plan, including:

[0038] Based on the priority list of key equipment, the priority of each power-consuming unit in power dispatch is confirmed to obtain a power dispatch priority guide;

[0039] Based on the power dispatch priority guidelines, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the Fangcang Hospital and generate a power network optimization path plan;

[0040] Based on the power network optimization path plan and the priority list of key equipment, the power dispatching strength is adjusted to ensure sufficient power supply for key equipment and generate a preliminary power dispatching plan;

[0041] Based on the preliminary power dispatch plan, fuzzy logic technology is used to combine environmental parameters and user feedback to evaluate user comfort and generate an optimized power dispatch plan.

[0042] Optionally, based on the power dispatch priority guidance, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the square cabin hospital and generate a power network optimization path plan, including:

[0043] Based on the power dispatch priority guide, evaluate the relative importance of each power consumption unit in power dispatch and generate an importance evaluation result;

[0044] Based on the importance assessment results, a network flow optimization algorithm based on graph theory is used to model the connection relationship of the power network nodes inside the cabin hospital to generate a power network topology model;

[0045] Based on the power network topology model, deeply analyze the power network, identify key nodes and paths, evaluate transmission efficiency and reliability, and generate a power network evaluation report;

[0046] Based on the power network assessment report and combined with the importance assessment results, the optimal power transmission path is formulated to ensure the efficiency of the power supply path for key equipment and generate a power network optimization path plan.

[0047] Optionally, based on the importance assessment result, a network flow optimization algorithm based on graph theory is used to model the connection relationship of the power network nodes inside the shelter hospital to generate a power network topology model, including:

[0048] Based on the importance assessment results, using geographic information system tools to obtain precise location information of each node;

[0049] Calculate the physical distance between nodes to construct a distance matrix, and perform weighted processing to generate an initial connection evaluation value;

[0050] The initial connection evaluation value is calculated using the following formula:

[0051]

[0052] Among them, A ij is the initial connection evaluation value between node i and node j; ω is the distance weight coefficient; D ij is the physical distance between node i and node j; λ is the distance nonlinear adjustment parameter; φ is the connection evaluation base value; v is the distance fluctuation adjustment coefficient; Dmax is the maximum physical distance; τ is the distance logarithm adjustment coefficient; ξ is the distance logarithm adjustment parameter;

[0053] Based on the initial connection evaluation value, combined with the importance evaluation result, additional fluctuation and adjustment coefficients are introduced, nonlinear effects of distances between nodes are considered, and multiple factors are comprehensively considered to generate a final connection cost;

[0054] The final connection cost is calculated using the following formula:

[0055]

[0056] Among them, C ij is the final connection cost between node i and node j; A ij is the initial connection evaluation value between node i and node j; μ is the importance adjustment coefficient; I i is the importance evaluation result of the i-th power consumption unit; ψ is the additional connection cost coefficient; κ is the additional fluctuation adjustment coefficient; D avg is the average physical distance; ρ is the quadratic adjustment parameter of importance; ζ is the cubic adjustment coefficient of distance; σ is the logarithmic adjustment coefficient of importance; η is the square adjustment coefficient of distance; γ is the square root adjustment parameter of importance;

[0057] Based on the final connection cost, the nodes and edges of the power network topology model are defined, the final connection cost is used as the edge weight, the shortest path method is used to optimize the topological structure of the power network, the optimal connection path between the nodes is determined, and the power network topology model is generated.

[0058] Optionally, based on the optimized power dispatch plan, regularly reviewing the power distribution effect, collecting user feedback and system operation data, optimizing the power distribution model, and generating an efficient and energy-saving power distribution system, including:

[0059] Based on the optimized power dispatch plan, regularly review the power distribution effect, evaluate the power distribution and energy-saving efficiency, and generate a power distribution effect evaluation report;

[0060] Based on the power distribution effect evaluation report, collect user power supply satisfaction feedback information, combine it with system operation data, and generate feedback and operation data sets;

[0061] Based on the feedback and operation data set, analyze the performance of the existing power distribution model, identify model improvement points, propose targeted improvement measures, and generate a power distribution model optimization plan;

[0062] Based on the power distribution model optimization scheme, the power distribution strategy is adjusted to ensure efficient energy utilization and generate an efficient and energy-saving power distribution system.

[0063] In a second aspect, the embodiment of the present application provides a high-efficiency and energy-saving power distribution system for a square cabin hospital, including:

[0064] The collection module is used to collect the real-time power demand of each power-consuming unit in the square cabin hospital by using the sensor network, and generate a comprehensive energy demand report based on external meteorological conditions;

[0065] An adjustment module is used to automatically adjust the power distribution ratio based on the comprehensive energy demand report, using a mixed integer linear programming algorithm, combining actual power load and renewable energy output, and using a hierarchical analysis method to evaluate the importance of each power unit and generate a priority list of key equipment;

[0066] An analysis module is used to analyze the topology of the power network inside the Fangcang Cabin Hospital based on the priority list of key equipment, use a network flow optimization algorithm based on graph theory, adjust the power dispatching strength, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan;

[0067] The review module is used to regularly review the power distribution effect based on the optimized power dispatch plan, collect user feedback and system operation data, optimize the power distribution model, and generate an efficient and energy-saving power distribution system.

[0068] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an efficient and energy-saving power distribution method for a square cabin hospital as described in any one of the first aspects above.

[0069] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a high-efficiency and energy-saving power distribution method for a square cabin hospital as described in any one of the first aspects above.

[0070] In an embodiment of the present application, a sensor network is used to collect the real-time electricity demand of each power-consuming unit in the square cabin hospital, and a comprehensive energy demand report is generated in combination with external meteorological conditions; based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used, combined with the actual power load and renewable energy output, to automatically adjust the power distribution ratio, and a hierarchical analysis method is used to evaluate the importance of each power-consuming unit, and a priority list of key equipment is generated; based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the square cabin hospital, adjust the power dispatch intensity, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatch plan; based on the optimized power dispatch plan, the power distribution effect is regularly reviewed, user feedback and system operation data are collected, the power distribution model is optimized, and an efficient and energy-saving power distribution system is generated.

[0071] The technical solution of this application has the following technical effects:

[0072] This application generates a comprehensive energy demand report by collecting electricity demand and external meteorological conditions in real time, which can more accurately predict and manage energy demand, improve energy utilization efficiency, and reduce energy waste; using a mixed integer linear programming algorithm, combined with actual electricity load and renewable energy output, automatically adjust the power distribution ratio to ensure the reasonable allocation of power resources, maximize the utilization rate of renewable energy, and reduce overall energy costs; using the hierarchical analysis method to evaluate the importance of each power unit, generate a priority list of key equipment, ensure that the power supply of key equipment is guaranteed first, and improve the operating stability and reliability of key equipment; based on the priority list of key equipment, use the network flow optimization algorithm based on graph theory to analyze the topology of the power network, adjust the power dispatching intensity, ensure the efficiency and reliability of the power transmission path, and reduce power loss; use fuzzy logic technology to evaluate user comfort and generate an optimized power dispatching plan to ensure that while meeting power demand, the user experience and comfort are improved; based on the optimized power dispatching plan, regularly review the power distribution effect, collect user feedback and system operation data, and continuously optimize the power distribution model to ensure long-term efficient operation and continuous improvement of the system.

[0073] Furthermore, based on the comprehensive energy demand report, combined with the actual electricity load and renewable energy output, the electricity distribution ratio is optimized to generate an optimized electricity distribution plan; the mixed integer linear programming algorithm is used to automatically adjust the electricity distribution ratio of each power unit to generate the optimal electricity distribution strategy; through the hierarchical analysis method, an evaluation index system is pre-set to evaluate the importance of each power unit and generate an importance score; and a priority list of key equipment is generated in descending order of scores. By optimizing the power distribution ratio and combining the actual power load with the output of renewable energy, an optimized power distribution plan is generated to improve energy efficiency and reduce energy waste. The mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power unit to generate the optimal power distribution strategy to ensure the rational allocation of power resources and reduce the overall energy cost. The hierarchical analysis method is used to evaluate the importance of each power unit, generate a quantitative importance score, and generate a priority list of key equipment based on the score to ensure that the power supply of key equipment is guaranteed first and improve the operating stability and reliability of key equipment. The automatic adjustment of the power distribution ratio can quickly respond to the dynamic changes in power demand and ensure the timeliness and reliability of power supply. By optimizing the power distribution plan, the power supply of each power unit is ensured to be more stable and reliable, improving the user experience and comfort. Based on the optimized power distribution plan, the power distribution effect can be regularly reviewed, user feedback and system operation data can be collected, and the power distribution model can be continuously optimized to ensure the long-term efficient operation and continuous improvement of the system.

[0074] Furthermore, based on the priority list of key equipment, the priority of each power-consuming unit in power dispatch is determined, and a power dispatch priority guide is generated; the network flow optimization algorithm based on graph theory is used to analyze the topological structure of the power network inside the square cabin hospital, and an optimized power network path plan is generated; combined with the priority list of key equipment, the power dispatch intensity is adjusted to ensure sufficient power supply for key equipment, and a preliminary power dispatch plan is generated; fuzzy logic technology is used to combine environmental parameters and user feedback to evaluate user comfort and generate the final optimized power dispatch plan. By determining the priority of each power-consuming unit and generating power dispatch priority guidelines, we can ensure that the power supply of key equipment is guaranteed first and improve the operational stability and reliability of key equipment; by using the network flow optimization algorithm based on graph theory, we can analyze the topological structure of the power network and generate an optimized power network path plan to ensure the efficiency and reliability of the power transmission path and reduce power loss; by combining the priority list of key equipment, we can dynamically adjust the power dispatch intensity to ensure sufficient power supply for key equipment and improve the flexibility and adaptability of the power system; by using fuzzy logic technology, combining environmental parameters with user feedback, we can evaluate user comfort and generate the final optimized power dispatch plan to ensure that the user experience and comfort are improved while meeting power demand; by using automated and intelligent dispatch algorithms, we can quickly respond to and adapt to changes in power demand and ensure the timeliness and reliability of power supply; based on the optimized power dispatch plan, we can regularly review the power distribution effect, collect user feedback and system operation data, and continuously optimize the power distribution model to ensure the long-term efficient operation and continuous improvement of the system.

[0075] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] Figure 1 A flowchart of an efficient and energy-saving power distribution method for a square cabin hospital provided in an embodiment of the present application;

[0078] Figure 2 A schematic diagram of the structure of a high-efficiency and energy-saving power distribution system for a square cabin hospital provided in an embodiment of the present application;

[0079] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0081] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0082] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0083] Figure 1 A flowchart of an efficient and energy-saving power distribution method for a square cabin hospital is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0084] 101. Use the sensor network to collect the real-time electricity demand of each power-consuming unit in the square cabin hospital, and generate a comprehensive energy demand report based on external meteorological conditions.

[0085] In this step, the sensor network includes smart meters and environmental sensors installed in each power consumption unit of the square cabin hospital. The smart meters are used to collect power consumption data of each power consumption unit in real time, and the environmental sensors are used to collect external meteorological conditions data, such as temperature, humidity, light intensity, etc.

[0086] The electricity demand data includes the real-time, historical and predicted electricity consumption of each electricity consuming unit, reflecting the electricity consumption of each electricity consuming unit in the Fangcang Cabin Hospital.

[0087] External meteorological condition data includes environmental parameters such as temperature, humidity, and light intensity, which are used to assess the impact of the external environment on electricity demand.

[0088] The comprehensive energy demand report is generated based on electricity demand data and external meteorological conditions data. It is used to fully understand the energy demand of the Fangcang Cabin Hospital and provide basic data support for subsequent power distribution optimization.

[0089] In an embodiment of the present application, the power consumption data and external meteorological conditions data of each power consuming unit are collected in real time through a sensor network; the collected data is transmitted to a central data processing system; the collected data is cleaned, verified and integrated to ensure the accuracy and completeness of the data; based on the integrated data, a multi-dimensional comprehensive analysis is performed to evaluate the power demand of each power consuming unit and the impact of external meteorological conditions; a comprehensive energy demand report is generated, which records in detail the power demand and external meteorological conditions of each power consuming unit, providing basic data support for subsequent power distribution optimization.

[0090] Assuming that there are multiple power consumption units in a square cabin hospital, to ensure the efficient operation of the power system; install smart meters in each ward, medical equipment, office area and living area to collect power consumption data in real time; install environmental sensors outside the hospital to collect meteorological conditions such as temperature, humidity, and light intensity; smart meters collect power consumption data every 15 minutes and transmit it to the central data processing system through a wireless network; environmental sensors collect meteorological conditions data once an hour and transmit it to the central data processing system in the same way; the central data processing system cleans the received data to remove outliers and noise; verifies and integrates the cleaned data to generate a complete data set; analyzes the real-time and historical power consumption of each power consumption unit to predict future power demand; combines external meteorological conditions data to evaluate the impact of the environment on power demand. For example, high temperature may lead to increased power consumption of air conditioners, and low temperature may lead to increased power consumption of heating equipment; based on the analysis results, a comprehensive energy demand report is generated. The report records the power demand and external meteorological conditions of each power consumption unit in detail, providing basic data support for subsequent power distribution optimization.

[0091] Through the above steps, the Fangcang Cabin Hospital can grasp the power demand and external environmental conditions of each power-consuming unit in real time, providing a scientific basis for optimizing power distribution and improving energy utilization efficiency.

[0092] 102. Based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio in combination with the actual power load and renewable energy output, and a hierarchical analysis method is used to evaluate the importance of each power unit to generate a priority list of key equipment.

[0093] In this step, the mixed integer linear programming algorithm is an optimization algorithm used to find the optimal solution under multiple constraints. In this scenario, it is used to determine the optimal power distribution ratio to meet the balance between actual power load and renewable energy output.

[0094] The actual power load refers to the actual current power consumption of each power-consuming unit in the Fangcang Cabin Hospital.

[0095] Renewable energy output refers to the power generated by renewable energy equipment such as solar panels and wind turbines in the square cabin hospital.

[0096] The analytic hierarchy process is a multi-criteria decision analysis method used to evaluate the importance of each power unit. By constructing a judgment matrix, the importance of each unit is quantified and a priority list of key equipment is generated.

[0097] In the embodiment of the present application, based on the comprehensive energy demand report, the electricity demand of each power consuming unit and the output of renewable energy are analyzed; the mixed integer linear programming algorithm is used, combined with the actual power load and renewable energy output, to automatically adjust the power distribution ratio of each power consuming unit to generate an optimized power distribution plan; the hierarchical analysis method is used to set an evaluation index system, evaluate the importance of each power consuming unit, and generate a quantitative importance score; according to the importance score, a priority list of key equipment is generated in descending order to ensure that the power supply of key equipment is guaranteed first.

[0098] Optionally, the method in step 102, based on the comprehensive energy demand report, uses a mixed integer linear programming algorithm, combines actual electricity load and renewable energy output, automatically adjusts the power allocation ratio, uses a hierarchical analysis method to evaluate the importance of each power unit, and generates a priority list of key equipment, including: based on the comprehensive energy demand report, combined with actual electricity load and renewable energy output, optimizes the power allocation ratio to generate an optimized power allocation plan; based on the optimized power allocation plan, uses a mixed integer linear programming algorithm to automatically adjust the power allocation ratio of each power unit to generate an optimal power allocation strategy; based on the optimal power allocation strategy, uses a hierarchical analysis method to pre-set an evaluation index system, evaluate the importance of each power unit, and generate a quantitative importance score; based on the quantitative importance score, sort in descending order of the score to generate a priority list of key equipment.

[0099] In the embodiment of the present application, based on the comprehensive energy demand report, the electricity demand of each power consuming unit and the output of renewable energy are analyzed; in combination with the actual power load and the output of renewable energy, the power distribution ratio is optimized to generate an optimized power distribution plan; using a mixed integer linear programming algorithm, according to the optimized power distribution plan, the power distribution ratio of each power consuming unit is automatically adjusted to generate an optimal power distribution strategy; using the hierarchical analysis method, an evaluation index system is pre-set to evaluate the importance of each power consuming unit and generate a quantitative importance score; based on the quantitative importance score, a priority list of key equipment is generated in descending order of the score to ensure that the power supply of key equipment is guaranteed first.

[0100] Assume that there are multiple power consumption units in the power system of a medical institution, and it is necessary to ensure the efficient operation of the power system; collect the real-time, historical and predicted power consumption data of each power consumption unit; collect external meteorological conditions data, such as temperature, humidity, light intensity, etc., to evaluate the impact of the environment on power demand; generate a comprehensive energy demand report, which records in detail the power demand and external meteorological conditions of each power consumption unit; based on the comprehensive energy demand report, optimize the power distribution ratio in combination with the actual power load and renewable energy output; generate an optimized power distribution plan to ensure that the power supply of each power consumption unit is reasonable and efficient; use hybrid integration The linear programming algorithm automatically adjusts the power distribution ratio of each power-consuming unit according to the optimized power distribution plan; generates the optimal power distribution strategy to ensure the rational allocation of power resources, maximize the utilization of renewable energy, and reduce the overall energy cost; adopts the hierarchical analysis method to pre-set the evaluation index system, including the importance of equipment, power consumption, key task dependency, etc.; evaluates the importance of each power-consuming unit and generates a quantitative importance score; based on the quantitative importance score, generates a priority list of key equipment in descending order to ensure that the power supply of key equipment such as operating rooms and ICUs is given priority.

[0101] Through the above steps, medical institutions can grasp the power demand and external environmental conditions of each power-consuming unit in real time, optimize power distribution, ensure power supply for key equipment, improve energy utilization efficiency, and reduce operating costs.

[0102] Optionally, based on the optimized power distribution scheme, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power consuming unit to generate an optimal power distribution strategy, including: based on the optimized power distribution scheme, the mixed integer linear programming algorithm input parameters are configured to generate an input data set; based on the input data set, a mixed integer linear programming algorithm is used to set an objective function, control the total energy cost, maximize the utilization rate of renewable energy, and generate a power distribution optimization model; based on the power distribution optimization model, the optimal solution is automatically found through a solving algorithm to generate a power distribution optimization result; based on the power distribution optimization result, the specific power distribution ratio of each power consuming unit is analyzed and adjusted to ensure the feasibility and practicality of the distribution scheme and generate an optimal power distribution strategy.

[0103] In an embodiment of the present application, based on the optimized power distribution plan, the input parameters of the mixed integer linear programming algorithm are configured in detail to generate a complete input data set; using the generated input data set, the mixed integer linear programming algorithm is applied to set the objective function, aiming to control the total energy cost while maximizing the utilization rate of renewable energy; based on the constructed power distribution optimization model, a solution algorithm (such as the branch and bound method or the cutting plane method) is used to automatically find the optimal solution; according to the optimization results of the power distribution, the specific power distribution ratio of each power consuming unit is analyzed and adjusted to ensure the feasibility and practicality of the distribution plan, generate the optimal power distribution strategy, and ensure that the power supply of key power consuming units is given priority.

[0104] Assume that a temporary medical center uses multiple key power consumption areas in parallel. To ensure the smooth operation of the power system, install smart meters and environmental sensors to collect power consumption data and external meteorological conditions of each power consumption unit in real time. Transmit the collected data to the central data processing system to generate a comprehensive energy demand report. Based on the comprehensive energy demand report, generate an optimized power distribution plan, including the preliminary power distribution ratio of each power consumption unit. According to the optimized power distribution plan, configure the input parameters of the mixed integer linear programming algorithm to generate an input data set. The input parameters include the power demand of each power consumption unit, renewable energy output, power system operation restrictions, etc. Based on the input data set, use the mixed integer linear programming algorithm to set the objective function. The objective functions include controlling the total energy cost and maximizing the utilization rate of renewable energy; generating an optimization model for power distribution to ensure that the model can meet all constraints, such as restrictions on safe operation of the power system, maximum and minimum power consumption limits of power consumption units, etc.; automatically finding the optimal solution based on the power distribution optimization model through solving algorithms (such as branch and bound method, cutting plane method, etc.); generating power distribution optimization results, including the specific power distribution ratio of each power consumption unit; based on the power distribution optimization results, analyzing and adjusting the specific power distribution ratio of each power consumption unit to ensure the feasibility and practicality of the distribution plan; generating the optimal power distribution strategy to ensure that the power supply of key equipment such as operating rooms and ICUs is prioritized, while maximizing the utilization rate of renewable energy and reducing overall energy costs.

[0105] Through the above steps, the temporary medical center can grasp the power demand and external environmental conditions of each power-consuming unit in real time, optimize power distribution, and ensure the power supply of key equipment.

[0106] Optionally, based on the input data set, a mixed integer linear programming algorithm is used to set an objective function, control the total energy cost, maximize the utilization rate of renewable energy, and generate an electricity distribution optimization model, including:

[0107] Based on the input data set, verification and integration are performed via a central data processing system;

[0108] Deeply mine the key statistical features of the input data set and perform multi-dimensional time series analysis to generate the total energy cost;

[0109] The total energy cost is calculated using the following formula:

[0110]

[0111] Among them, C total is the total energy cost; α is the power purchase cost coefficient of the power grid; P grid,t is the amount of electricity purchased from the power grid at time t; β is the cost coefficient of renewable energy generation; P renewable,t is the power generation of renewable energy at time t; γ is the nonlinear adjustment coefficient of the cost of renewable energy power generation as the power generation increases; δ is the charging and discharging cost coefficient of the energy storage system; P storage,t is the charge and discharge capacity of the energy storage system at time t; η is the load cost coefficient; P load,t is the total load at time t; t is the index of the time period, from 1 to T; T is the total number of time periods;

[0112] Based on the total energy cost, a multi-dimensional system stability analysis is performed, and a nonlinear transformation is performed by introducing multiple reward and penalty mechanisms to generate an objective function;

[0113] The objective function is calculated using the following formula:

[0114]

[0115] Among them, F is the objective function; C total is the total energy cost; ω is the renewable energy utilization rate reward coefficient; θ is the renewable energy utilization rate attenuation coefficient, which is used to simulate the phenomenon of diminishing marginal benefits as the renewable energy utilization rate increases; μ is the exponential attenuation coefficient of renewable energy utilization, which further simulates the diminishing marginal benefits under high utilization rate; φ is the load deviation penalty coefficient; P load,i is the load of the ith power unit; P renewable,i is the renewable energy power allocated to the i-th power unit; λ is the load deviation adjustment coefficient, which is used to balance the impact of load deviation; v is the load fluctuation adjustment coefficient, which is used to consider the impact of load fluctuation on penalty; P max is the maximum load; t is the index of the time period, from 1 to T; T is the total number of time periods; i is the index of the power unit, from 1 to N; N is the total number of power units;

[0116] Based on the objective function, relevant constraints are integrated as input configurations, and the optimal solution is output through the branch and bound method to ensure that the utilization rate of renewable energy is maximized while controlling the total energy cost, and generate a power distribution optimization model.

[0117] This method aims to optimize power distribution, control total energy costs and maximize the utilization of renewable energy through a mixed integer linear programming algorithm. Through multi-dimensional time series analysis and system stability analysis, it introduces multiple reward and penalty mechanisms to ensure the efficiency and economy of power distribution, while improving the utilization of renewable energy and system stability.

[0118] In the total energy cost, the cost of purchasing electricity from the grid Reflects the increase in marginal costs caused by the increase in electricity purchases; the cost of renewable energy power generation Simulate the nonlinear change of renewable energy generation cost with the increase of power generation; the charging and discharging cost of energy storage system δP storage,t : reflects the operating cost of the energy storage system; load cost ηln(1+P load,t ): Load cost coefficient η multiplied by the total load P at time t load,t The natural logarithm of ; Design reason: to reflect the increase in marginal cost caused by the increase in load;

[0119] Among them, α is the power purchase cost coefficient of the power grid, which is obtained through the electricity price data of the power grid company; β is the renewable energy power generation cost coefficient, which is obtained through the operation and maintenance cost data of renewable energy equipment; γ is the nonlinear adjustment coefficient of renewable energy power generation cost with the increase of power generation, which is obtained through historical data fitting; δ is the energy storage system charging and discharging cost coefficient, which is obtained through the operation and maintenance cost data of the energy storage system; η is the load cost coefficient, which is obtained through the data of the load management system; is the square of the amount of electricity purchased from the power grid at time t, collected in real time by smart meters; P renewable,t is the power generation of renewable energy at time t, through real-time data collection of renewable energy equipment; P storage,t is the charge and discharge amount of the energy storage system at time t, acquired through real-time data collection of the energy storage system; P load,t is the total load at time t, collected in real time by smart meters;

[0120] In the objective function, the total energy cost C total :Introduce total energy cost, which plays the role of basic cost in the objective function; Reward for renewable energy utilization rate Simulate the phenomenon of diminishing marginal returns; load deviation penalty Ensure the matching of load and renewable energy, while considering the impact of load fluctuations on penalties;

[0121] Among them, ω is the renewable energy utilization rate reward coefficient, which is set according to policy or company strategy; θ is the renewable energy utilization rate attenuation coefficient, which is obtained by fitting historical data; μ is the exponential attenuation coefficient of renewable energy utilization, which is obtained by fitting historical data; φ is the load deviation penalty coefficient, which is set according to the data of the load management system; P load,i is the load of the i-th power consumption unit, which is collected in real time by the smart meter; P renewable,i is the renewable energy power allocated to the i-th power consumption unit, calculated by the optimization model; λ is the load deviation adjustment coefficient, obtained by fitting historical data; v is the load fluctuation adjustment coefficient, obtained by fitting historical data; P max is the maximum load, obtained through the historical data of the load management system; t is the index of the time period, from 1 to T; T is the total number of time periods, set according to the actual situation; i is the index of the power unit, from 1 to N; N is the total number of power units, set according to the actual situation;

[0122] Assume that there is a large medical institution with multiple power-critical areas configured inside. To ensure the smooth operation of the power system in these areas and the primary power supply of key medical equipment;

[0123] Assume α=0.1; β=0.05; γ=0.01; δ=0.02; η=0.03; T=24; P grid,t =[100,110,120,…,100]; P renewable,t =[50,55,60,…,50]; P storage,t =[10,15,20,…,10]; P load,t =[150,160,170,…,150];

[0124] Total energy cost

[0125] Assume ω=0.01; θ=0.005; μ=0.001; φ=0.002; λ=0.01; v=0.005; N=5; P load,i =[150,160,170,180,190]; P renewable,i =[50,55,60,65,70]; P max =200;

[0126] Objective Function

[0127] According to the above calculations, the generated objective function value is 119478.12, indicating that under the premise of controlling the total energy cost, the utilization rate of renewable energy is maximized and the load deviation is effectively managed. This ensures the high efficiency and economy of power distribution and improves the operating efficiency of the power system of the comprehensive medical center; assuming that the threshold is set to 120000, since the result 119478.12 is less than the set threshold, it shows that under the premise of controlling the total energy cost, the utilization rate of renewable energy is successfully maximized, and the load deviation is effectively managed, ensuring the high efficiency and economy of power distribution. Through the above steps, large medical institutions ensure the high efficiency and economy of power distribution and improve the operating efficiency of the power system of the comprehensive medical center.

[0128] Optionally, based on the optimal power distribution strategy, the analytic hierarchy process is used to pre-set an evaluation index system, evaluate the importance of each power consuming unit, and generate a quantitative importance score, including: based on the optimal power distribution strategy, analyzing the power distribution situation of each power consuming unit in different time periods, and generating power demand characteristic data; based on the power demand characteristic data, pre-setting a multi-dimensional evaluation index system to ensure that the evaluation standards are comprehensive and scientific, and generate an evaluation index system framework; based on the evaluation index system framework, the analytic hierarchy process is used to conduct pairwise comparative analysis by constructing a judgment matrix, evaluate the relative importance of each indicator, and generate a relative weight coefficient; based on the relative weight coefficient, quantify the overall importance of each power consuming unit, comprehensively evaluate the specific performance of different evaluation indicators of each power consuming unit, and generate a quantitative importance score.

[0129] In the embodiment of the present application, based on the optimal power distribution strategy, the power distribution of each power unit in different time periods is analyzed in detail to generate detailed power demand characteristic data; based on the generated power demand characteristic data, a multi-dimensional evaluation index system is pre-set to ensure the comprehensiveness and scientificity of the evaluation criteria, and each indicator has a clear evaluation standard and weight; the hierarchical analysis method is used to construct a judgment matrix for pairwise comparative analysis to evaluate the relative importance of each indicator, and the consistency ratio of the judgment matrix is ​​calculated to ensure the reliability of the evaluation results and generate a relative weight coefficient; based on the relative weight coefficient, the overall importance of each power unit is quantified. Comprehensively evaluate the specific performance of each power unit on different evaluation indicators, generate a quantitative importance score, and ensure the accuracy and practicality of the evaluation results.

[0130] Assume that an emergency medical shelter contains multiple key power consumption areas, and it is necessary to ensure the efficient operation of the power system and the priority power supply of key equipment; install smart meters in multiple key power consumption areas to collect power consumption data in real time; install environmental sensors outside the emergency medical shelter to collect meteorological conditions data such as temperature, humidity, and light intensity; transmit the collected data to the central data processing system; clean, verify and integrate the data to ensure the accuracy and completeness of the data; based on the optimal power allocation strategy, analyze the power allocation of each power unit in different time periods to generate power demand characteristic data; based on the power demand characteristic data, pre-set a multi-dimensional evaluation index system to ensure the comprehensiveness and scientificity of the evaluation standards; generate an evaluation index system framework, including indicators such as power consumption, key task dependency, power consumption time distribution and equipment type; use the hierarchical analysis method to construct a judgment matrix for pairwise comparative analysis, evaluate the relative importance of each indicator, and generate the relative weight coefficient of each indicator; based on the relative weight coefficient, quantify the overall importance of each power unit; comprehensively evaluate the specific performance of each power unit on different evaluation indicators to generate a quantitative importance score.

[0131] Through the above steps, the emergency medical cabin can comprehensively evaluate the importance of each power-consuming unit, generate a quantitative importance score, ensure that the power supply of key equipment is prioritized, and improve the efficient operation of the power system and energy utilization efficiency.

[0132] 103. Based on the priority list of key equipment, use the network flow optimization algorithm based on graph theory to analyze the internal power network topology of the square cabin hospital, adjust the power dispatch intensity, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatch plan.

[0133] In this step, the network flow optimization algorithm based on graph theory is an algorithm used to optimize the traffic distribution in the network. In this scenario, it is used to analyze the topology of the power network inside the square cabin hospital and optimize power dispatch.

[0134] The power network topology refers to the connection method and distribution of the power network inside the Fangcang Cabin Hospital.

[0135] Power dispatch intensity refers to the degree and scope of adjusting power distribution to adapt to different power demands.

[0136] Fuzzy logic technology, a mathematical method for handling uncertainty and fuzzy information, is used to assess user comfort and ensure that power dispatch not only meets functional requirements but also takes into account user experience.

[0137] In an embodiment of the present application, based on the priority list of key equipment, the priority of each power-consuming unit in power dispatch is confirmed; the network flow optimization algorithm of graph theory is used to analyze the topological structure of the power network inside the square cabin hospital, and an optimized power network path plan is generated; combined with the priority list of key equipment, the power dispatch intensity is adjusted to ensure sufficient power supply for key equipment, and a preliminary power dispatch plan is generated; fuzzy logic technology is used to evaluate user comfort in combination with environmental parameters and user feedback, and a final optimized power dispatch plan is generated.

[0138] Optionally, in step 103, based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the topological structure of the power network inside the shelter hospital, adjust the power dispatching intensity, and use fuzzy logic technology to evaluate user comfort to generate an optimized power dispatching plan, including: based on the priority list of key equipment, confirming the priority of each power-consuming unit in power dispatching to obtain power dispatching priority guidance; based on the power dispatching priority guidance, using a network flow optimization algorithm based on graph theory to analyze the topological structure of the power network inside the shelter hospital, and generating a power network optimization path plan; based on the power network optimization path plan, combined with the priority list of key equipment, adjusting the power dispatching intensity to ensure sufficient power supply for key equipment and generating a preliminary power dispatching plan; based on the preliminary power dispatching plan, using fuzzy logic technology, combined with environmental parameters and user feedback, to evaluate user comfort and generate an optimized power dispatching plan.

[0139] In an embodiment of the present application, the priority order of each power-consuming unit in power dispatch is determined based on the priority list of key equipment, and a power dispatch priority guide is generated; based on the power dispatch priority guide, the network flow optimization algorithm of graph theory is used to analyze the topological structure of the power network, and an optimized path plan for the power network is generated; in combination with the optimized path plan for the power network and the priority list of key equipment, the power dispatch intensity is adjusted to ensure sufficient power supply for key equipment, and a preliminary power dispatch plan is generated; based on the preliminary power dispatch plan, fuzzy logic technology is used, combined with environmental parameters and user feedback, to evaluate user comfort and generate an optimized power dispatch plan.

[0140] Assume that there is a mobile medical facility, which contains multiple critical power consumption areas. These areas require the power system to operate efficiently and ensure that key medical equipment has power supply priority. Based on the priority list of key equipment, confirm the priority of each power unit in power dispatch, for example, the operating room and ICU have the highest priority, and the logistics support area has the lowest priority. Generate power dispatch priority guidelines to ensure that the power supply of key equipment is guaranteed first. Based on the power dispatch priority guidelines, use the network flow optimization algorithm based on graph theory to analyze the topological structure of the power network inside the mobile medical facility. Generate a power network optimization path plan to ensure the efficiency and reliability of the power transmission path. The optimization path plan may include adjusting the connection mode of power lines to reduce power transmission losses; based on the power network optimization path plan, combined with the priority list of key equipment, adjust the power dispatching efforts to ensure sufficient power supply for key equipment; generate a preliminary power dispatching plan to ensure that the power supply of key equipment such as operating rooms and ICUs is prioritized, while taking into account the power demand of other power-consuming units; based on the preliminary power dispatching plan, use fuzzy logic technology, combined with environmental parameters (such as temperature, humidity, light intensity) and user feedback to evaluate user comfort; generate an optimized power dispatching plan to ensure that while meeting power demand, the user experience and comfort are improved. For example, by adjusting the power supply of air conditioning and lighting systems, the temperature and light in the ward can be ensured to be appropriate.

[0141] Through the above steps, mobile medical facilities can comprehensively evaluate the importance of each key power consumption area, generate an optimized power dispatch plan, ensure that the power supply of key equipment is prioritized, and improve the efficient operation of the power system and user satisfaction.

[0142] Optionally, based on the power dispatch priority guidelines, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the square cabin hospital and generate a power network optimization path plan, including: based on the power dispatch priority guidelines, evaluating the relative importance of each power consuming unit in the power dispatch, and generating an importance assessment result; based on the importance assessment result, using a network flow optimization algorithm based on graph theory to model the connection relationship of the power network nodes inside the square cabin hospital, and generate a power network topology model; based on the power network topology model, deeply analyze the power network, identify key nodes and paths, evaluate transmission efficiency and reliability, and generate a power network assessment report; based on the power network assessment report and in combination with the importance assessment results, formulate the optimal power transmission path, ensure the efficiency of the power supply path for key equipment, and generate a power network optimization path plan.

[0143] In an embodiment of the present application, according to the power dispatch priority guidance, the relative importance of each power consuming unit in the power dispatch is evaluated; the importance assessment result is generated to ensure that the power supply of key equipment is guaranteed in priority; based on the importance assessment result, the network flow optimization algorithm of graph theory is used to establish a node connection relationship model of the power network; a power network topology model is generated to ensure that the model can accurately reflect the structure and connection relationship of the power network; based on the power network topology model, the power network is deeply analyzed to identify key nodes and paths; the transmission efficiency and reliability are evaluated, and a power network evaluation report is generated to ensure the comprehensiveness and accuracy of the evaluation results; based on the power network evaluation report and the importance assessment results, the optimal path for power transmission is formulated; the power supply path of key equipment is ensured to be efficient, and an optimized path plan for the power network is generated.

[0144] Assume that there is an emergency medical facility with multiple core power demand areas, all of which need to maintain the efficient operation of the power system and ensure that key medical equipment has primary power supply guarantee; deploy smart meters in each power consumption core area of ​​the emergency medical facility to capture power consumption data in real time; install environmental sensing devices on the periphery of the facility to monitor and record external environmental parameters such as temperature, humidity, and light level; transmit all collected information to the central data processing center for deep cleaning, verification and comprehensive processing to ensure the accuracy and completeness of the data; evaluate the relative importance of each power unit in power dispatch based on the power dispatch priority guidance; generate importance assessment results to ensure the priority of key equipment Power supply is guaranteed as a priority; based on the importance assessment results, the node connection relationship of the internal power network of the cabin hospital is modeled using a network flow optimization algorithm based on graph theory; a power network topology model is generated to ensure that the model can accurately reflect the structure and connection relationship of the power network; based on the power network topology model, the power network is deeply analyzed, key nodes and paths are identified, and transmission efficiency and reliability are evaluated; a power network evaluation report is generated to record in detail the transmission efficiency and reliability of each node and path to ensure the comprehensiveness and accuracy of the evaluation results; based on the power network evaluation report and importance assessment results, the optimal path for power transmission is formulated; the power supply path of key equipment is ensured to be efficient, and an optimized path plan for the power network is generated.

[0145] Through the above steps, emergency medical sites can comprehensively evaluate the importance of each power-consuming unit, generate an optimized path plan for the power network, ensure that the power supply of key equipment is prioritized, and improve the efficient operation and reliability of the power system.

[0146] Optionally, based on the importance assessment result, a network flow optimization algorithm based on graph theory is used to model the connection relationship of the power network nodes inside the shelter hospital to generate a power network topology model, including:

[0147] Based on the importance assessment results, using geographic information system tools to obtain precise location information of each node;

[0148] Calculate the physical distance between nodes to construct a distance matrix, and perform weighted processing to generate an initial connection evaluation value;

[0149] The initial connection evaluation value is calculated using the following formula:

[0150]

[0151] Among them, A ij is the initial connection evaluation value between node i and node j; ω is the distance weight coefficient; D ij is the physical distance between node i and node j; λ is the distance nonlinear adjustment parameter; φ is the connection evaluation base value; v is the distance fluctuation adjustment coefficient; D max is the maximum physical distance; τ is the distance logarithm adjustment coefficient; ξ is the distance logarithm adjustment parameter;

[0152] Based on the initial connection evaluation value, combined with the importance evaluation result, additional fluctuation and adjustment coefficients are introduced, nonlinear effects of distances between nodes are considered, and multiple factors are comprehensively considered to generate a final connection cost;

[0153] The final connection cost is calculated using the following formula:

[0154]

[0155] Among them, C ij is the final connection cost between node i and node j; A ij is the initial connection evaluation value between node i and node j; μ is the importance adjustment coefficient; I i is the importance evaluation result of the i-th power consumption unit; ψ is the additional connection cost coefficient; κ is the additional fluctuation adjustment coefficient; D avg is the average physical distance; ρ is the quadratic adjustment parameter of importance; ζ is the cubic adjustment coefficient of distance; σ is the logarithmic adjustment coefficient of importance; η is the square adjustment coefficient of distance; γ is the square root adjustment parameter of importance;

[0156] Based on the final connection cost, the nodes and edges of the power network topology model are defined, the final connection cost is used as the edge weight, the shortest path method is used to optimize the topological structure of the power network, the optimal connection path between the nodes is determined, and the power network topology model is generated.

[0157] This method aims to ensure the efficiency and reliability of the power network through multi-dimensional evaluation and adjustment, taking into account the physical distance between nodes, node importance evaluation results and various nonlinear influencing factors. By introducing the initial connection evaluation value of multiple factors and combining the final connection cost calculation of the node importance evaluation results, the optimal power network topology is generated, the optimal connection path between nodes is clarified, and the operation efficiency and stability of the power network are improved.

[0158] In the initial connection evaluation value, the distance weight term Reflects the impact of the physical distance between nodes on the connection evaluation, and controls the nonlinear change of the distance impact through the nonlinear adjustment parameter λ; the connection evaluation base value Provides a basic connection evaluation value, taking into account the periodic fluctuation of distance through the fluctuation adjustment coefficient v; the distance logarithm term τln(1+ξD ij ): The logarithmic adjustment coefficient ξ reflects the logarithmic effect of distance on connection evaluation, ensuring that the evaluation value of long-distance connections is not too high;

[0159] Among them, ω is the distance weight coefficient, which is obtained by fitting historical data; λ is the distance nonlinear adjustment parameter, which is obtained by fitting historical data; φ is the connection evaluation base value, which is set according to the actual situation; ν is the distance fluctuation adjustment coefficient, which is obtained by fitting historical data; D max is the maximum physical distance, obtained through geographic information system tools; τ is the distance logarithm adjustment coefficient, obtained through historical data fitting; ξ is the distance logarithm adjustment parameter, obtained through historical data fitting; D ij is the physical distance between node i and node j, obtained through geographic information system tools;

[0160] In the final connection cost, the initial connection evaluation value item : Combine the initial connection evaluation value and the node importance evaluation result, and adjust the connection cost by the importance adjustment coefficient μ; the additional connection cost item The additional connection cost coefficient ψ and fluctuation adjustment coefficient κ are introduced to consider the periodic fluctuation of distance and the quadratic effect of node importance; the distance cubic term The increase in the cost of long-distance connections is reflected by the distance cubic adjustment coefficient ζ and the importance logarithmic adjustment coefficient σ; the distance square term The distance square adjustment coefficient η and the importance square adjustment parameter γ are used to reflect the increased cost of medium-distance connections;

[0161] Among them, μ is the importance adjustment coefficient, which is obtained by fitting historical data; I iis the importance assessment result of the i-th power consumption unit, calculated by the importance assessment model; ψ is the additional connection cost coefficient, set according to the actual situation; κ is the additional fluctuation adjustment coefficient, obtained by fitting historical data; D avg is the average physical distance, obtained through the geographic information system tool; ρ is the importance quadratic adjustment parameter, obtained through historical data fitting; ζ is the distance cubic adjustment coefficient, obtained through historical data fitting; σ is the importance logarithmic adjustment coefficient, obtained through historical data fitting; η is the distance square adjustment coefficient, obtained through historical data fitting; γ is the importance root sign adjustment parameter, obtained through historical data fitting; D ij is the physical distance between node i and node j, obtained through geographic information system tools; D max is the maximum physical distance, obtained through geographic information system tools;

[0162] Assume that there is a comprehensive medical shelter with multiple important power supply areas, which requires the power distribution system to operate efficiently to ensure that the core medical equipment has the primary power supply;

[0163] Assume that the parameters are ω=0.1;λ=0.01;φ=0.05;v=0.005;D max =100; τ = 0.02; ξ = 0.01; D ij =[10,20,30,…,90];

[0164] Initial connection evaluation value

[0165] Assume that the parameters are μ = 0.01; I i =[0.8,0.9,1.0,…,0.8]; ψ=0.05; κ=0.005; D avg =50; ρ=0.01; ζ=0.02; σ=0.01; η=0.03; γ=0.01; D ij =[10,20,30,…,90]; D max =100;

[0166] Final connection cost 3;

[0168] Assuming that the threshold is set to 1.01, since the final connection cost result of 1.003 is less than the set threshold, it shows that the topological structure of the power network is successfully optimized based on the consideration of the physical distance between nodes and the importance evaluation results, ensuring the optimal connection path between nodes and improving the efficiency and reliability of the power network. Through the above steps, the node connection relationship of the power network inside the comprehensive medical shelter is effectively optimized, an efficient topological structure is generated, and the stability and economy of the power system are ensured.

[0169] 104. Based on the optimized power dispatch plan, regularly review the power distribution effect, collect user feedback and system operation data, optimize the power distribution model, and generate an efficient and energy-saving power distribution system.

[0170] In this step, regular review refers to regularly checking and evaluating the effectiveness of power distribution to ensure that the system is always in optimal condition.

[0171] User feedback refers to the evaluation of the power supply and services of the shelter hospitals by patients and medical staff. Such feedback is very important for improving the power distribution plan.

[0172] System operation data includes various operating indicators of the power system, such as power consumption, equipment operating status, etc. These data are used to evaluate the performance of the system.

[0173] Optimizing the power distribution model means continuously adjusting and optimizing the power distribution model based on review results and user feedback to improve the efficiency and energy-saving effects of the system.

[0174] An efficient and energy-saving power distribution system refers to a power distribution plan formed after multiple optimizations, aiming to achieve the goal of efficient and energy-saving.

[0175] In the embodiment of the present application, based on the optimized power dispatch plan, the effect of power distribution is reviewed regularly to ensure the effectiveness and reliability of the power distribution plan; user feedback and system operation data are collected, including the operating status, power consumption, energy consumption, etc. of the power system, to evaluate the effect of power distribution; based on the collected data, the power distribution model is optimized, the algorithm parameters are adjusted, and the efficiency and energy saving of power distribution are improved; an efficient power distribution system is generated to ensure long-term efficient operation and continuous improvement of power distribution.

[0176] Optionally, the power dispatch plan based on the optimization in step 104 periodically reviews the power distribution effect, collects user feedback and system operation data, optimizes the power distribution model, and generates an efficient and energy-saving power distribution system, including: based on the optimized power dispatch plan, periodically reviews the power distribution effect, evaluates the power distribution and energy-saving efficiency, and generates a power distribution effect evaluation report; based on the power distribution effect evaluation report, collects user power supply satisfaction feedback information, combines system operation data, and generates a feedback and operation data set; based on the feedback and operation data set, analyzes the performance of the existing power distribution model, identifies model improvement points, proposes targeted improvement measures, and generates a power distribution model optimization plan; based on the power distribution model optimization plan, adjusts the power distribution strategy to ensure efficient energy utilization and generates a efficient and energy-saving power distribution system.

[0177] In an embodiment of the present application, the execution effect of the optimized power dispatch plan is checked regularly to evaluate the efficiency and energy-saving effect of power distribution; a power distribution effect evaluation report is generated to record in detail the power distribution situation and energy-saving effect of each power consumption unit; based on the power distribution effect evaluation report, user satisfaction feedback information on power supply is collected, such as obtaining feedback through questionnaires or user interviews; combined with the system's operating data, including the operating status, power consumption, energy consumption, etc. of the power system, a feedback and operation data set is generated; based on the feedback and operation data set, the performance of the existing power distribution model is analyzed to identify the improvement points of the model; specific improvement measures are proposed, such as adjusting the power distribution ratio, optimizing the power transmission path, etc., to generate a power distribution model optimization plan; based on the power distribution model optimization plan, the power distribution strategy is adjusted to ensure the efficiency of energy utilization; an efficient and energy-saving power distribution system is generated to ensure the efficiency and energy saving of power distribution and improve user satisfaction.

[0178] Assume that there is a comprehensive medical center with multiple areas with special needs for electricity. It is necessary to ensure the efficient operation of the power system in these areas and the priority of power supply for key medical equipment; regularly review the execution effect of the optimized power dispatch plan, and evaluate the power distribution and energy-saving efficiency; generate a power distribution effect evaluation report, and record in detail the power distribution situation and energy-saving effect of each power-consuming unit; based on the power distribution effect evaluation report, collect user satisfaction feedback on power supply, such as obtaining feedback through questionnaires or user interviews; combine system operation data, including the operating status, power consumption, and energy consumption of the power system, to generate feedback and operation data sets; based on the feedback and operation data sets, analyze the performance of the existing power distribution model and identify model improvement points; propose targeted improvement measures, such as adjusting the power distribution ratio, optimizing the power transmission path, etc., to generate a power distribution model optimization plan; based on the power distribution model optimization plan, adjust the power distribution strategy to ensure efficient energy utilization; generate an efficient and energy-saving power distribution system to ensure the efficiency and energy saving of power distribution and improve user satisfaction.

[0179] Through the above steps, the comprehensive medical center can regularly review the power distribution effect, collect user feedback and system operation data, optimize the power distribution model, generate an efficient and energy-saving power distribution system, and ensure the efficiency and energy saving of power distribution.

[0180] Figure 2 The present application provides a schematic diagram of a high-efficiency and energy-saving power distribution system for a square cabin hospital, such as Figure 2 As shown, the device comprises:

[0181] The collection module 21 is used to collect the real-time power demand of each power unit in the square cabin hospital by using the sensor network, and generate a comprehensive energy demand report in combination with external meteorological conditions;

[0182] An adjustment module 22 is used to automatically adjust the power distribution ratio based on the comprehensive energy demand report, using a mixed integer linear programming algorithm, combining actual power load and renewable energy output, and using a hierarchical analysis method to evaluate the importance of each power unit and generate a priority list of key equipment;

[0183] An analysis module 23 is used to analyze the topology of the power network inside the square cabin hospital based on the priority list of key equipment, use a network flow optimization algorithm based on graph theory, adjust the power dispatching strength, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan;

[0184] The review module 24 is used to regularly review the power distribution effect based on the optimized power dispatch plan, collect user feedback and system operation data, optimize the power distribution model, and generate an efficient and energy-saving power distribution system.

[0185] Figure 2 The highly efficient and energy-saving power distribution system for square cabin hospitals can be implemented Figure 1 The implementation principle and technical effects of the high-efficiency and energy-saving power distribution method for square cabin hospitals described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the high-efficiency and energy-saving power distribution system for square cabin hospitals in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0186] In one possible design, Figure 2 An energy-efficient power distribution system for square cabin hospitals in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0187] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0188] The processing component 32 is used to: use the sensor network to collect the real-time power demand of each power unit in the square cabin hospital, and generate a comprehensive energy demand report in combination with external meteorological conditions; based on the comprehensive energy demand report, use a mixed integer linear programming algorithm, combined with actual power load and renewable energy output, automatically adjust the power distribution ratio, use hierarchical analysis method to evaluate the importance of each power unit, and generate a priority list of key equipment; based on the priority list of key equipment, use a network flow optimization algorithm based on graph theory to analyze the internal power network topology of the square cabin hospital, adjust the power dispatching intensity, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan; based on the optimized power dispatching plan, regularly review the power distribution effect, collect user feedback and system operation data, optimize the power distribution model, and generate an efficient and energy-saving power distribution system.

[0189] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0190] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0191] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0192] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0193] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0194] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0195] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a highly efficient and energy-saving power distribution method for a square cabin hospital.

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0197] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0198] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A highly efficient and energy-saving power distribution method for square cabin hospitals, characterized in that: include: The sensor network is used to collect the real-time power demand of each power-consuming unit in the Fangcang Hospital, and combined with external meteorological conditions, a comprehensive energy demand report is generated; Based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio by combining the actual power load and renewable energy output, and a hierarchical analysis method is used to evaluate the importance of each power unit to generate a priority list of key equipment; Based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the topology of the power network inside the Fangcang Cabin Hospital, adjust the power dispatching strength, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan; Based on the optimized power dispatch plan, the power distribution effect is regularly reviewed, user feedback and system operation data are collected, the power distribution model is optimized, and an efficient and energy-saving power distribution system is generated.

2. The method according to claim 1, characterized in that Based on the comprehensive energy demand report, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio in combination with the actual power load and renewable energy output, and a hierarchical analysis method is used to evaluate the importance of each power unit to generate a priority list of key equipment, including: Based on the comprehensive energy demand report, combined with actual electricity load and renewable energy output, the power distribution ratio is optimized and an optimized power distribution plan is generated; Based on the optimized power distribution scheme, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power consumption unit to generate an optimal power distribution strategy; Based on the optimal power distribution strategy, a hierarchical analysis method is used to pre-set an evaluation index system to evaluate the importance of each power consumption unit and generate a quantitative importance score; Based on the quantitative importance scores, a priority list of key equipment is generated by sorting in descending order of scores.

3. The method according to claim 2, characterized in that Based on the optimized power distribution scheme, a mixed integer linear programming algorithm is used to automatically adjust the power distribution ratio of each power consumption unit to generate an optimal power distribution strategy, including: Based on the optimized power distribution scheme, configuring input parameters of a mixed integer linear programming algorithm to generate an input data set; Based on the input data set, a mixed integer linear programming algorithm is used to set an objective function, control the total energy cost, maximize the utilization rate of renewable energy, and generate an electricity distribution optimization model; Based on the power distribution optimization model, the optimal solution is automatically found through a solution algorithm to generate a power distribution optimization result; Based on the power distribution optimization results, the specific power distribution ratio of each power consumption unit is analyzed and adjusted to ensure the feasibility and practicality of the distribution plan and generate the optimal power distribution strategy.

4. The method according to claim 3, characterized in that: Based on the input data set, a mixed integer linear programming algorithm is used to set the objective function, control the total energy cost, maximize the utilization of renewable energy, and generate an electricity distribution optimization model, including: Based on the input data set, verification and integration are performed via a central data processing system; Deeply mine the key statistical features of the input data set and perform multi-dimensional time series analysis to generate the total energy cost; The total energy cost is calculated using the following formula: Among them, C total is the total energy cost; α is the power purchase cost coefficient of the power grid; P grid，t is the amount of electricity purchased from the power grid at time t; β is the cost coefficient of renewable energy generation; P renewable，t is the power generation of renewable energy at time t; γ is the nonlinear adjustment coefficient of the cost of renewable energy power generation as the power generation increases; % is the charging and discharging cost coefficient of the energy storage system; P storage，t is the charge and discharge capacity of the energy storage system at time t; η is the load cost coefficient; P load，t is the total load at time t; t is the index of the time period, from 1 to T; T is the total number of time periods; Based on the total energy cost, a multi-dimensional system stability analysis is performed, and a nonlinear transformation is performed by introducing multiple reward and penalty mechanisms to generate an objective function; The objective function is calculated using the following formula: Among them, F is the objective function; C total is the total energy cost; ω is the renewable energy utilization rate reward coefficient; θ is the renewable energy utilization rate attenuation coefficient, which is used to simulate the phenomenon of diminishing marginal benefits as the renewable energy utilization rate increases; μ is the exponential attenuation coefficient of renewable energy utilization, which further simulates the diminishing marginal benefits under high utilization rate; φ is the load deviation penalty coefficient; P load，i is the load of the ith power unit; P renewable，i is the renewable energy power allocated to the i-th power unit; λ is the load deviation adjustment coefficient, which is used to balance the impact of load deviation; v is the load fluctuation adjustment coefficient, which is used to consider the impact of load fluctuation on penalty; P max is the maximum load; t is the index of the time period, from 1 to T; T is the total number of time periods; i is the index of the power unit, from 1 to N; N is the total number of power units; Based on the objective function, relevant constraints are integrated as input configurations, and the optimal solution is output through the branch and bound method to ensure that the utilization rate of renewable energy is maximized while controlling the total energy cost, and generate a power distribution optimization model.

5. The method according to claim 2, characterized in that: Based on the optimal power distribution strategy, the hierarchical analysis method is used to pre-set an evaluation index system to evaluate the importance of each power consumption unit and generate a quantitative importance score, including: Based on the optimal power distribution strategy, analyze the power distribution of each power consumption unit in different time periods to generate power demand characteristic data; Based on the power demand characteristic data, a multi-dimensional evaluation index system is pre-set to ensure that the evaluation standards are comprehensive and scientific and to generate an evaluation index system framework; Based on the evaluation index system framework, the hierarchical analysis method is used to construct a judgment matrix for pairwise comparison analysis, evaluate the relative importance of each index, and generate relative weight coefficients; Based on the relative weight coefficient, the overall importance of each power consumption unit is quantified, and the specific performance of different evaluation indicators of each power consumption unit is comprehensively evaluated to generate a quantitative importance score.

6. The method according to claim 1, characterized in that Based on the priority list of key equipment, a network flow optimization algorithm based on graph theory is used to analyze the topology of the power network inside the Fangcang Cabin Hospital, adjust the power dispatching strength, and use fuzzy logic technology to evaluate user comfort to generate an optimized power dispatching plan, including: Based on the priority list of key equipment, the priority of each power-consuming unit in power dispatch is confirmed to obtain a power dispatch priority guide; Based on the power dispatch priority guidelines, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the Fangcang Hospital and generate a power network optimization path plan; Based on the power network optimization path plan and the priority list of key equipment, the power dispatching strength is adjusted to ensure sufficient power supply for key equipment and generate a preliminary power dispatching plan; Based on the preliminary power dispatch plan, fuzzy logic technology is used to combine environmental parameters and user feedback to evaluate user comfort and generate an optimized power dispatch plan.

7. The method according to claim 6, characterized in that Based on the power dispatch priority guidance, a network flow optimization algorithm based on graph theory is used to analyze the internal power network topology of the square cabin hospital and generate a power network optimization path plan, including: Based on the power dispatch priority guide, evaluate the relative importance of each power consumption unit in power dispatch and generate an importance evaluation result; Based on the importance assessment results, a network flow optimization algorithm based on graph theory is used to model the connection relationship of the power network nodes inside the cabin hospital to generate a power network topology model; Based on the power network topology model, deeply analyze the power network, identify key nodes and paths, evaluate transmission efficiency and reliability, and generate a power network evaluation report; Based on the power network assessment report and combined with the importance assessment results, an optimal power transmission path is formulated to ensure efficient power supply paths for key equipment and generate a power network optimization path plan.

8. The method according to claim 7, characterized in that Based on the importance assessment results, the network flow optimization algorithm based on graph theory is used to model the connection relationship of the power network nodes inside the cabin hospital, and generate a power network topology model, including: Based on the importance assessment results, using geographic information system tools to obtain precise location information of each node; Calculate the physical distance between nodes to construct a distance matrix, and perform weighted processing to generate an initial connection evaluation value; The initial connection evaluation value is calculated using the following formula: Among them, A ij is the initial connection evaluation value between node i and node j; ω is the distance weight coefficient; D ij is the physical distance between node i and node j; λ is the distance nonlinear adjustment parameter; φ is the connection evaluation base value; v is the distance fluctuation adjustment coefficient; D max is the maximum physical distance; τ is the distance logarithm adjustment coefficient; ξ is the distance logarithm adjustment parameter; Based on the initial connection evaluation value, combined with the importance evaluation result, additional fluctuation and adjustment coefficients are introduced, nonlinear effects of distances between nodes are considered, and multiple factors are comprehensively considered to generate a final connection cost; The final connection cost is calculated using the following formula: Among them, C ij is the final connection cost between node i and node j; A ij is the initial connection evaluation value between node i and node j; μ is the importance adjustment coefficient; I i is the importance evaluation result of the i-th power consumption unit; ψ is the additional connection cost coefficient; κ is the additional fluctuation adjustment coefficient; D avg is the average physical distance; ρ is the quadratic adjustment parameter of importance; ζ is the cubic adjustment coefficient of distance; σ is the logarithmic adjustment coefficient of importance; ηη is the square adjustment coefficient of distance; γ is the square root adjustment parameter of importance; D max is the maximum physical distance between all node pairs; Based on the final connection cost, the nodes and edges of the power network topology model are defined, the final connection cost is used as the edge weight, the shortest path method is used to optimize the topological structure of the power network, the optimal connection path between the nodes is determined, and the power network topology model is generated.

9. The method according to claim 1, characterized in that: The optimized power dispatch plan is based on regular review of power distribution effects, collection of user feedback and system operation data, optimization of power distribution models, and generation of an efficient and energy-saving power distribution system, including: Based on the optimized power dispatch plan, regularly review the power distribution effect, evaluate the power distribution and energy-saving efficiency, and generate a power distribution effect evaluation report; Based on the power distribution effect evaluation report, collect user power supply satisfaction feedback information, combine it with system operation data, and generate feedback and operation data sets; Based on the feedback and operation data set, analyze the performance of the existing power distribution model, identify model improvement points, propose targeted improvement measures, and generate a power distribution model optimization plan; Based on the power distribution model optimization scheme, the power distribution strategy is adjusted to ensure efficient energy utilization and generate an efficient and energy-saving power distribution system.

10. An efficient and energy-saving power distribution system for square cabin hospitals, characterized in that: include: The collection module is used to collect the real-time power demand of each power-consuming unit in the square cabin hospital by using the sensor network, and generate a comprehensive energy demand report based on external meteorological conditions; An adjustment module is used to automatically adjust the power distribution ratio based on the comprehensive energy demand report, using a mixed integer linear programming algorithm, combining actual power load and renewable energy output, and using a hierarchical analysis method to evaluate the importance of each power unit and generate a priority list of key equipment; An analysis module is used to analyze the topology of the power network inside the Fangcang Cabin Hospital based on the priority list of key equipment, use a network flow optimization algorithm based on graph theory, adjust the power dispatching strength, use fuzzy logic technology to evaluate user comfort, and generate an optimized power dispatching plan; The review module is used to regularly review the power distribution effect based on the optimized power dispatch plan, collect user feedback and system operation data, optimize the power distribution model, and generate an efficient and energy-saving power distribution system.

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