Hospital energy scheduling method and system based on smart energy management platform

Abstract

The application relates to the technical field of energy management, and discloses a hospital energy scheduling method and system based on a smart energy management platform. The method comprises the following steps: collecting the position and power consumption data of the lighting area of an operating room in real time through a deployed sensor network, dividing high and low energy consumption areas by using K-means clustering, calculating the area connection strength, screening a preferential transmission link, and then generating an initial shortest path by using a Dijkstra algorithm; when the line load exceeds an adaptive threshold, immediately rearranging the node priority, optimizing the transmission path, accurately allocating energy according to the demand ratio, and simultaneously correcting the lighting node layout in a reverse direction according to the measured efficiency deviation; further calculating a backup path and its load distribution based on the updated data, fusing the initial and backup paths to obtain a comprehensive load diagram, locking the final proportion scheduling through a terminal key node. The method can solve the problem of energy scheduling imbalance in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and in particular to a hospital energy scheduling method and system based on a smart energy management platform. BACKGROUND

[0002] At present, operating room lighting is classified as the highest grade load in the hospital, and must be continuously powered with "zero flash and zero fluctuation". After the popularization of LED shadowless lamps, intelligent dimming controllers and intraoperative image navigation equipment, the single-room peak power grows rapidly, and the operation scheduling is random, and the load can jump by more than 30% in minutes, requiring the scheduling system to have a second-level response and centimeter-level topology analysis capability.

[0003] In one prior art, the "offline topology calibration + timing polling" method is used to manage the operating room energy: first, a static energy consumption baseline is formed by manually entering the building CAD diagram, and then the total power of each distribution box is summarized in 15 min cycles, and the electrical energy is pre-allocated to the lighting circuit according to a fixed proportion; when new operating lamps or shadowless lamp positions are adjusted, the topology needs to be manually corrected and the strategy needs to be reissued. In addition, the system relies on historical average to set the line current threshold, and does not integrate real-time position information and instantaneous energy consumption characteristics, and cannot dynamically refresh the optimal transmission path according to impedance changes.

[0004] Because the static baseline cannot reflect the minute-level load mutation, the offline topology update cycle is long, and there is a lack of position-function coupling modeling for different lighting levels, resulting in that high-energy-consuming nodes cannot be preferentially powered during peak periods, and low-energy-consuming nodes are in an over-allocated state for a long time; at the same time, fixed proportion allocation ignores the dynamic impedance of the line, which is easy to form additional voltage drop and transient overload at branch nodes, resulting in unbalanced energy scheduling and increasing the risk of key lamp flash. Therefore, there is an unbalanced energy scheduling problem in the prior art due to offline topology and static allocation strategy. SUMMARY

[0005] The present application provides a hospital energy scheduling method and system based on a smart energy management platform to solve the problem of unbalanced energy scheduling in the prior art.

[0006] In a first aspect, to solve the above technical problems, the present application provides a hospital energy scheduling method based on a smart energy management platform, comprising:

[0007] Obtaining real-time data of the lighting area of the hospital operating room, classifying and processing the real-time data, dividing high-energy-consuming lighting areas and low-energy-consuming lighting areas, analyzing the dynamic relationship between the high-energy-consuming lighting areas and the low-energy-consuming lighting areas, calculating the area connection strength, and obtaining the area connection strength data set, wherein the real-time data includes: real-time position information, energy consumption data and an operating room layout map containing lighting device distribution points;

[0008] According to the inter-regional connection strength dataset, a priority transmission region is screened, and in combination with the real-time data, an initial shortest path of energy transmission to the high-energy-consumption lighting region is calculated, and an initial path load distribution is calculated, to obtain an initial path load distribution map;

[0009] If there is a line load exceeding a preset line load threshold in the initial path load distribution map, priority adjustment is performed on the path nodes, to obtain an optimized line transmission path;

[0010] According to the optimized line transmission path, an energy distribution ratio is calculated, according to the energy distribution ratio, an initial energy utilization efficiency is calculated, and the operating room layout map is corrected, to obtain an updated operating room layout map;

[0011] According to the updated operating room layout map, a backup path and a backup path load distribution are calculated, to obtain a backup path load distribution map;

[0012] According to the backup path load distribution map, a backup energy distribution ratio is calculated, according to the backup energy distribution ratio, a backup energy utilization efficiency is calculated, a deviation between the backup energy utilization efficiency and the initial energy utilization efficiency is calculated, and according to the deviation calculation result, the initial path load distribution map and the backup path load distribution map are fused, to obtain a comprehensive load distribution map;

[0013] According to the comprehensive load distribution map, a final energy utilization efficiency of the operating room lighting region is calculated.

[0014] Preferably, real-time data of a hospital operating room lighting region is obtained, the real-time data is classified and processed, a high-energy-consumption lighting region and a low-energy-consumption lighting region are divided, a dynamic relationship between the high-energy-consumption lighting region and the low-energy-consumption lighting region is analyzed, a regional connection strength is calculated, and a regional connection strength dataset is obtained, including:

[0015] Real-time data of a hospital operating room lighting region is obtained, the real-time data including: real-time position information, energy consumption data, and an operating room layout map containing lighting device distribution points;

[0016] According to the real-time position information and the energy consumption data of the hospital operating room lighting region, a K-means clustering algorithm is used for classification processing, to obtain a classification result of the high-energy-consumption lighting region and the low-energy-consumption lighting region;

[0017] According to the real-time position information and the energy consumption data of the hospital operating room lighting region, energy demand value calculation of each region is performed, to obtain an energy demand peak time period dataset;

[0018] According to the classification results of the high-energy-consumption lighting area and the low-energy-consumption lighting area, inter-regional dynamic analysis is performed to obtain a dynamic relationship between the high-energy-consumption lighting area and the low-energy-consumption lighting area.

[0019] According to the dynamic relationship between the high-energy-consumption lighting area and the low-energy-consumption lighting area and the operating room layout map, regional connection strength calculation is performed to obtain a regional connection strength data set.

[0020] Preferably, the priority transmission region is screened according to the regional connection strength data set, the real-time data is combined, the initial shortest path of energy transmission to the high-energy-consumption lighting area is calculated, and the initial path load distribution is calculated to obtain an initial path load distribution map, including:

[0021] The regions in the regional connection strength data set that have a connection strength higher than a preset strength threshold are screened and marked as priority transmission regions to obtain a priority transmission link set;

[0022] According to the energy demand peak time period data set, Dijkstra algorithm is used to calculate the shortest transmission path from the energy source to the high-energy-consumption lighting area to obtain a path node sequence.

[0023] According to the path node sequence and the energy consumption data, fusion operation is performed to obtain an initial path load distribution map.

[0024] Preferably, according to the optimized line transmission path, the energy distribution ratio is calculated, the initial energy utilization efficiency is calculated according to the energy distribution ratio, and the operating room layout map is corrected to obtain an updated operating room layout map, including:

[0025] According to the optimized line transmission path, the energy distribution ratio of each node is calculated to obtain an energy distribution ratio table.

[0026] According to the optimized line transmission path, lighting device distribution data is extracted to obtain lighting node distribution data.

[0027] According to the energy distribution ratio table, scheduling operation is performed to calculate the initial energy utilization efficiency of the hospital operating room lighting area.

[0028] If the initial energy utilization efficiency is lower than a preset correction threshold, efficiency deviation calculation is performed to obtain an initial efficiency deviation.

[0029] According to the initial efficiency deviation, the lighting node distribution data is corrected by reverse correction operation to obtain an updated operating room layout map.

[0030] Preferably, the calculating the backup path and the backup path load distribution according to the updated operating room layout map comprises:

[0031] If the connection strength of any priority link in the updated operating room layout map is lower than the reset threshold, link relabeling is performed and information investigation is re-performed to obtain real-time position information and energy consumption data of the updated hospital operating room lighting area;

[0032] According to the real-time position information and energy consumption data of the updated hospital operating room lighting area, peak time period calculation is re-performed to obtain an updated energy demand peak time period data set;

[0033] According to the updated energy demand peak time period data set, dynamic path generation and load distribution calculation are performed to obtain a backup path load distribution map.

[0034] Preferably, the calculating the backup path and the backup path load distribution according to the updated operating room layout map comprises:

[0035] If there is a load lower than the preset overload threshold in the backup path load distribution map, node priority rearrangement is performed to obtain an optimization sequence;

[0036] According to the optimization sequence, energy redistribution is performed to obtain the backup energy distribution ratio;

[0037] According to the backup energy distribution ratio, scheduling operation is performed to obtain the backup energy utilization efficiency of the hospital operating room lighting area;

[0038] According to the initial energy utilization efficiency and the backup energy utilization efficiency, comparison operation is performed to obtain a backup efficiency deviation;

[0039] According to the backup efficiency deviation, the initial path load distribution map and the backup path load distribution map are fused to obtain a comprehensive load distribution map.

[0040] Preferably, the final energy utilization efficiency of the operating room lighting area is calculated according to the comprehensive load distribution map through terminal optimization operation, comprising:

[0041] If the comprehensive load of a node in the comprehensive load distribution map exceeds the preset integration threshold, primary node and backup node priority adjustment operation is performed to obtain a final transmission path sequence;

[0042] According to the final transmission path sequence, a final key node locking is performed to obtain a final energy distribution ratio;

[0043] According to the final energy distribution ratio, a scheduling operation is performed to obtain a final energy utilization efficiency of the operating room lighting area.

[0044] In a second aspect, the present application provides a hospital energy scheduling system based on a smart energy management platform, comprising:

[0045] A data preprocessing module is configured to acquire real-time data of an operating room lighting area of a hospital, perform classification processing on the real-time data, divide high-energy-consumption lighting areas and low-energy-consumption lighting areas, analyze a dynamic relationship between the high-energy-consumption lighting areas and the low-energy-consumption lighting areas, calculate an area connection strength, and obtain an area connection strength data set, wherein the real-time data comprises real-time position information, energy consumption data, and an operating room layout map containing lighting device distribution points.

[0046] An initial load map generation module is configured to filter priority transmission areas according to the area connection strength data set, combine the real-time data, calculate an initial shortest path of energy transmission to the high-energy-consumption lighting areas and an initial path load distribution, and obtain an initial path load distribution map.

[0047] A route optimization module is configured to, when there is a line load exceeding a preset line load threshold in the initial path load distribution map, adjust the priority of path nodes to obtain an optimized line transmission path.

[0048] A layout updating module is configured to calculate an energy distribution ratio according to the optimized line transmission path, calculate an initial energy utilization efficiency according to the energy distribution ratio, and correct the operating room layout map to obtain an updated operating room layout map.

[0049] A backup path generation module is configured to calculate a backup path and a backup path load distribution according to the updated operating room layout map, and obtain a backup path load distribution map.

[0050] A comprehensive load calculation module is configured to calculate a backup energy distribution ratio according to the backup path load distribution map, calculate a backup energy utilization efficiency according to the backup energy distribution ratio, calculate a deviation between the backup energy utilization efficiency and the initial energy utilization efficiency, and fuse the initial path load distribution map and the backup path load distribution map according to the deviation calculation result to obtain a comprehensive load distribution map.

[0051] A final output module is configured to perform a final optimization operation according to the comprehensive load distribution map to calculate a final energy utilization efficiency of the operating room lighting area.

[0052] In a third aspect, the present application also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the hospital energy scheduling method based on the smart energy management platform according to any one of the above description when executing the computer program.

[0053] In a fourth aspect, the present application also provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium performs the hospital energy scheduling method based on the smart energy management platform according to any one of the above description when the computer program runs.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] (1) The present application obtains the "real-time position-power consumption" raw data of the operating room lighting area in real time through the sensor network, and performs K-means clustering and noise removal to obtain the high / low energy consumption lighting area classification result; all subsequent path, load and distribution calculations are based on the cleaned accurate data set, avoiding offline CAD input errors and manual baseline drift, ensuring the integrity, consistency and accuracy of the energy scheduling decision.

[0056] (2) The present application calculates the area connection strength based on the accurate classification result, dynamically selects the priority transmission link, and then uses the Dijkstra algorithm to generate the shortest path; compared with the fixed cycle polling scheme, the system can identify the minute-level load jump in seconds, significantly shorten the peak current response time, and reduce the transient pressure drop and flash-off risk of the operating shadow lamp.

[0057] (3) When the path load exceeds the adaptive threshold, the present application automatically rearranges the node priority and re-distributes the flow, tilting the energy to high energy consumption nodes and allowing flow to low energy consumption nodes; this strategy can alleviate the imbalance phenomenon of power supply shortage in high demand areas and long-term overflow in low demand areas caused by traditional "average pre-distribution", reduce the overall line loss, and effectively reduce the number of circuit breaker trips in the cabinet.

[0058] (4) The present application corrects the lighting device distribution in reverse according to the measured efficiency deviation, dynamically migrates the nodes that waste energy to the power supply point, and synchronously updates the operating room layout map; this closed-loop correction reduces manual recalibration, shortens the layout change period from several weeks to minutes, and ensures that the system still adapts immediately after adding new operating lamps or moving shadow lamps.

[0059] (5)The application generates a comprehensive load distribution diagram and a final energy allocation ratio through initial-backup dual-path parallel calculation, efficiency deviation fusion and final key node locking. The strategy has the characteristics of scalability, reusability and dynamic adjustment, so that the hospital can maintain stable continuous power supply when facing random surgery scheduling and single-room power jump of more than 30%, and comprehensively improve the stability of the intelligent energy management platform. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a hospital energy scheduling method flow diagram based on an intelligent energy management platform provided by the first embodiment of the application;

[0061] Figure 2 is a hospital energy scheduling system structure diagram based on an intelligent energy management platform provided by the second embodiment of the application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0063] With reference to Figure 1 , the first embodiment of the application provides a hospital energy scheduling method based on an intelligent energy management platform, including the following steps:

[0064] S11, obtaining real-time data of a hospital operating room lighting area, classifying and processing the real-time data, dividing high-energy-consumption lighting areas and low-energy-consumption lighting areas, analyzing the dynamic relationship between the high-energy-consumption lighting areas and the low-energy-consumption lighting areas, calculating the area connection strength, and obtaining the area connection strength data set, wherein the real-time data includes real-time position information, energy consumption data and an operating room layout diagram containing lighting device distribution points;

[0065] S12, filtering a priority transmission area according to the area connection strength data set, combining the real-time data, calculating an initial shortest path of energy transmission to the high-energy-consumption lighting area and calculating an initial path load distribution, and obtaining an initial path load distribution diagram;

[0066] S13, if the line load in the initial path load distribution diagram exceeds a preset line load threshold, adjusting the priority of the path node to obtain an optimized line transmission path;

[0067] S14, according to the optimized line transmission path, calculate the energy distribution ratio, according to the energy distribution ratio, calculate the initial energy utilization efficiency and correct the operating room layout to obtain an updated operating room layout;

[0068] S15, according to the updated operating room layout, calculate the backup path and backup path load distribution to obtain a backup path load distribution diagram;

[0069] S16, according to the backup path load distribution diagram, calculate the backup energy distribution ratio, according to the backup energy distribution ratio, calculate the backup energy utilization efficiency, calculate the deviation of the backup energy utilization efficiency and the initial energy utilization efficiency, according to the deviation calculation result, fuse the initial path load distribution diagram and the backup path load distribution diagram to obtain a comprehensive load distribution diagram;

[0070] S17, according to the comprehensive load distribution diagram, perform final optimization operation to calculate the final energy utilization efficiency of the operating room lighting area.

[0071] In step S11, real-time data of a hospital operating room lighting area is obtained, the real-time data is classified and processed, high energy consumption lighting areas and low energy consumption lighting areas are divided, the dynamic relationship between the high energy consumption lighting areas and the low energy consumption lighting areas is analyzed, the area connection strength is calculated, and an area connection strength data set is obtained, wherein the real-time data includes: real-time position information, energy consumption data and an operating room layout including lighting device distribution points, including:

[0072] Obtain real-time data of a hospital operating room lighting area, the real-time data including: real-time position information, energy consumption data and an operating room layout including lighting device distribution points;

[0073] According to the real-time position information and the energy consumption data of the hospital operating room lighting area, a K-means clustering algorithm is used for classification processing to obtain classification results of high energy consumption lighting areas and low energy consumption lighting areas;

[0074] According to the real-time position information and the energy consumption data of the hospital operating room lighting area, the energy demand value of each area is calculated to obtain an energy demand peak time period data set;

[0075] According to the classification results of the high energy consumption lighting areas and the low energy consumption lighting areas, inter-regional dynamic analysis is performed to obtain the dynamic relationship between the high energy consumption lighting areas and the low energy consumption lighting areas;

[0076] According to the dynamic relationship between the high energy consumption lighting areas and the low energy consumption lighting areas and the operating room layout, the area connection strength is calculated to obtain an inter-regional connection strength data set.

[0077] In an implementation, the acquisition of real-time data of the hospital operating room lighting area can be to deploy an ultra-wideband positioning base station at the four corners of the operating room ceiling, embed a power metering chip at the driving power supply of each LED shadowless lamp to calculate the power consumption of a single LED shadowless lamp, and then calculate the regional lighting power consumption after aggregation. The signal is aggregated to the nurse station edge gateway through the same PoE network cable. The gateway packages once per second to form a JSON record of "timestamp-lamp ID-XY coordinate-instantaneous power consumption", for example: 2025-09-11T14:23:45, Lamp03, (3.2 meters, 4.8 meters), 2.7 kilowatts. The acquisition of the operating room layout map containing the distribution points of lighting devices can be that the system acquires the operating room completion CAD file from the hospital infrastructure department, extracts the reference layers of walls, air supply ceiling, bridge layout, etc. as the initial layout base map; through the on-site UWB surveying and mapping label, the coordinates of three points of the center of the ceiling, the T-shaped mouth of the bridge, and the reference point of the wall are measured, and the CAD theoretical coordinates are translated and rotated to align, completing the scale correction; then the XY coordinates of each LED shadowless lamp reported in real time are superimposed on the corrected base map to form an operating room layout map containing the distribution points of lighting devices, and the version number is automatically incremented with the change of the lamp position.

[0078] The K-means clustering algorithm is used for classification processing. The whole process is to randomly select two records as initial cluster centers, calculate the Euclidean distance between the remaining records and the cluster centers, and assign the lighting devices to the cluster with closer distance. Then the average of the XY coordinates and power consumption in the cluster is taken as the new cluster center, and the iteration is performed until the cluster center no longer moves. Finally, the cluster with high average power consumption is the high-energy consumption lighting area. For example, after five iterations, the average power consumption of cluster A is 3.1 kilowatts, and the average power consumption of cluster B is 0.9 kilowatts. Therefore, cluster A is defined as the high-energy consumption lighting area, and cluster B is defined as the low-energy consumption lighting area.

[0079] Among them, the calculation of the Euclidean distance of the remaining records and the cluster center can be to take the position and power consumption in the lamp record as a multi-dimensional point, the position takes the X coordinate and Y coordinate, the power consumption takes the instantaneous kilowatt value, and the three form a three-dimensional point. The cluster center also has the same three-dimensional value. After summing up the square of the corresponding dimension difference value, a "distance value" of a lamp record to a cluster center is obtained. The smaller the value is, the closer the characteristics are. During dimension processing, the X coordinate and Y coordinate are in meters, and the power consumption is in kilowatts. Due to different numerical ranges, the system first divides the coordinates by the maximum length of the operating room, and divides the power consumption by the historical maximum power consumption, so that the three types of numerical values fall between zero and one, thereby avoiding the power consumption value being large and hiding the coordinate difference. The selection rule is to calculate the distance of the same lamp to two cluster centers respectively, and then directly compare the size of the two distance values. The lamp is classified into the cluster with smaller distance value. If the two distance values are completely equal, the cluster traversed first is defaulted to be classified into; for example, the three-dimensional value of the current cluster center 1 is 0.3, 0.4, and 0.35, the three-dimensional value of the cluster center 2 is 0.7, 0.8, and 0.05, and the lamp record to be classified is 0.2, 0.5, and 0.30. The distance to cluster center 1 is 0.15, and the distance to cluster center 2 is 0.63. Since 0.15 is smaller, the lamp is classified into the cluster where the cluster center 1 is located, and the Euclidean distance operation of the record and the cluster center is completed.

[0080] The energy demand of each area is calculated. The specific method is to divide a day into one-minute granularity, sum up the power consumption of the same lamp ID in each minute to obtain the area power consumption, and then divide the area power consumption by the area size to obtain the energy demand value. Then, a 30-minute window is slid to find the highest continuous period of demand value, for example, the demand value in the 07:45-08:15 window reaches 0.85 kilowatts per square meter, which is higher than the rest of the period. Therefore, the window is recorded in the energy demand peak time period data set.

[0081] The inter-regional dynamic analysis first calculates the total power consumption of the high-energy-consumption lighting area and the low-energy-consumption lighting area by minutes to obtain two "power consumption-time" curves, denoted as PH and PL; then the difference sequence ΔD = PH-PL is obtained at the same time; if the slope of PH is obviously steep and the slope of PL is close to zero, the demand of the high-energy-consumption lighting area rises faster; if the slope of PL gradually increases and the slope of PH tends to be slow, the low-energy-consumption lighting area starts to rise; after comparing the slopes, the ΔD change section is associated with the "surgery start" broadcast timestamp pushed by the hospital information system, and if the time difference between the ΔD expansion starting point and the broadcast is less than 2 minutes and the PL slope does not increase synchronously, it indicates that the high-energy-consumption lighting area responds sensitively to the surgery start signal, and the low-energy-consumption lighting area lags behind, reflecting the dynamic relationship between the regions. For example, at 08:00, the HIS broadcasts that OR03 surgery starts, and from 08:00 to 08:10, ΔD rises from 1.8 kW to 2.4 kW, and at the same time, the slope of PH is 0.08 kW per minute, and the slope of PL is 0.02 kW per minute. The time difference between the broadcast and the ΔD expansion is 55 seconds. The system generates a dynamic relationship that "the high-energy-consumption lighting area responds sensitively to the surgery start signal, and the low-energy-consumption lighting area lags behind" based on this, and stores it in the dataset for subsequent path optimization.

[0082] The inter-regional connection strength calculation is completed in five steps. First, the lamp positions are extracted from the CAD lighting device distribution points. Then, the circuit topology graph is generated along the actual bridge direction. Next, real-time electrical parameters are collected. Then, the unit impedance power flow flux is calculated. Finally, the strength value is written into the dataset to ensure that the connection strength reflects the real cable direction and electrical load. Topology extraction takes the power distribution cabinet outlet end as the starting point, and the actual cable direction from the bridge, wire slot, branch pipeline to the end lamp is used as the basis. The system converts each bridge into a "node-edge" graph on the CAD layout graph, with edge weight being the product of cable impedance and length, forming a circuit topology graph to ensure that the current path is consistent with the actual laying. Electrical parameter collection is achieved by installing micro current-voltage sensors at the T-type port of each bridge and the end lamp loop, which real-time returns the line segment impedance, current-carrying capacity and node voltage drop. The gateway updates every minute to obtain a three-dimensional electrical parameter set of "edge impedance-edge current-node voltage drop" as the basis for strength calculation. The inter-regional connection strength calculation can be based on the circuit topology graph to calculate the actual cable length between the cluster center of the high-energy-consumption area and the cluster center of the low-energy-consumption area. Then, the average power consumption difference in the dynamic relationship is used as the energy gradient. The connection strength is obtained by dividing the energy gradient by the actual cable length. For example, the actual cable length between the cluster center of the high-energy-consumption area and the cluster center of the low-energy-consumption area is 6 meters, and the average power consumption difference is 2.4 kW. The connection strength is recorded as "0.4 kW / m". The higher the value, the more need to prioritize the laying of power supply lines. Finally, the strength values of all regions are written into the inter-regional connection strength dataset for subsequent shortest path and backup path calculation.

[0083] In step S12, the priority transmission area is screened according to the inter-area connection strength data set, the initial shortest path of energy transmission to the high-energy consumption lighting area is calculated in combination with the real-time data, and the initial path load distribution is calculated to obtain an initial path load distribution diagram, including:

[0084] The area in the inter-area connection strength data set whose connection strength is higher than the preset strength threshold value is screened and marked as a priority transmission area to obtain a priority transmission link set;

[0085] According to the energy demand peak time period data set, the Dijkstra algorithm is used to calculate the shortest transmission path from the energy source to the high-energy consumption lighting area to obtain a path node sequence;

[0086] According to the path node sequence and the energy consumption data, a fusion operation is performed to obtain an initial path load distribution diagram.

[0087] In an implementation manner, the connection strength data set can be arranged in descending order of strength value, and the top 30% records are taken as candidates. The node pair containing the surgical shadowless lamp is forced to be reserved, and the node pairs with strength values greater than the strength threshold value of 0.4 kilowatts per meter are reserved. The reserved node pairs are the priority transmission areas, for example, the connection strength of “surgical area center-surgical area edge” is 0.55 kilowatts per meter, which is higher than the threshold value, and is marked as a red priority line and added to the priority transmission link set. The connection strength of “corridor-dirty interval” is 0.3 kilowatts per meter, which is discarded.

[0088] It should be noted that the strength threshold value is obtained by self-statistics of the past 30 days of historical data. The system pulls all connection strength values into a descending list at dawn every day, and takes the 85th percentile value as the strength threshold value. Therefore, 0.4 kilowatts per meter is the 85th percentile value in the past month, and the front 30% proportion is used in the existing operation and maintenance specification of the hospital that “no more than three key areas”. The surgical shadowless lamp node is forced to be reserved according to the hospital electrical safety specification, so the threshold value does not need to be manually input, and is automatically refreshed every day. For example, if the addition of a high-power surgery yesterday caused the 85th percentile value to rise to 0.42, the screening threshold value will automatically change to 0.42.

[0089] The shortest transmission path can be calculated, taking the power distribution room as the starting point, each lighting partition as the node, and the line length as the edge weight. The distance from the starting point is initialized to 0, and the distances of the remaining nodes are infinite. From the starting point, adjacent nodes are added to the list to be investigated. The new distance is accumulated every time a new node is reached. If the new distance is less than the current record, the distance is updated and the predecessor node is recorded. The expansion is repeated until the high-energy-consuming lighting area node is removed. The predecessor is backtracked to obtain the path node sequence such as "power distribution room-bridge T3-surgery center". For example, the power distribution room node is denoted as A, the bridge T3 is denoted as B, the surgery center is denoted as C, and the corridor node D is not involved. The length of the AB line segment is 18 meters, the length of the BC segment is 5 meters, and the length of the AC segment is directly 30 meters but there is a bend in the middle, which is actually equivalent to 25 meters. The distance from A is initialized to 0, and the distances of the remaining nodes are infinite. From A, the distances of adjacent nodes B and C are 18 and 25, respectively. The minimum distance of 18 is selected to B. B further explores C to obtain 18 plus 5, which is equal to 23, which is less than 25. Therefore, the distance of C is updated to 23 and the predecessor is recorded as B. At this time, C is the end point of the high-energy-consuming lighting area. The path node sequence obtained by backtracking the predecessor in reverse is A-B-C, i.e., "power distribution room-bridge T3-surgery center". The total length of the line is 23 meters, which is selected as the shortest transmission path.

[0090] The initial path load distribution diagram can be obtained by performing a fusion operation on the path node sequence and the energy consumption data. The power consumption value of each node in the sequence at the peak period is superimposed on the node. Adjacent nodes are connected by line segments. The color of the line segment is displayed from cold to warm according to the total power consumption from high to low. The line width is displayed from thin to thick according to the current estimated value. The initial path load distribution diagram with color bands and line widths is generated on the CAD base map. The operation and maintenance personnel can directly see the highest load segment and prioritize reinforcement. For example, the system first captures the data of the peak period 08:00-08:30. The power consumption of node A is 0 kW, the power consumption of node B is 1 kW, and the power consumption of node C is 3 kW. The power consumption value is superimposed on the node. The line is connected using the CAD base map. The total power consumption of the A-B segment is 1 kW, which is mapped to light yellow with a line width of 1 mm. The total power consumption of the B-C segment is 4 kW, which is mapped to orange red with a line width of 3 mm. The current estimated value of 8 A is also marked on the line segment. The initial path load distribution diagram is generated. After the operation and maintenance personnel open the diagram, they immediately see that the orange red B-C segment is the thickest, confirming that this segment has the highest load. Therefore, they arrange to add a parallel cable to this segment during the lunch break to complete the prioritized reinforcement.

[0091] It is worth noting that the current estimation is completed once in the gateway. The total power consumption of the adjacent two nodes is taken as the active power of the segment. The current is calculated by dividing the active power by 0.95 and then by 220 volts according to the default power factor of 0.95 in the operating room and the phase voltage. The estimated current is rounded to an integer ampere. It is directly written into the line segment attribute. For example, the active power of the B-C segment is 4 kW, which is approximately 19 A after conversion. The system displays 19 A and uses it for line width and color mapping. The operation and maintenance personnel judge the load level and arrange reinforcement accordingly.

[0092] In step S13, if the line load in the initial path load distribution exceeds a preset line load threshold, the priority of the path node is adjusted to obtain an optimized line transmission path.

[0093] In an implementation manner, the line load threshold is adaptively generated by the system at 23:00 of the previous night, the maximum current of the same period in the past 7 days is taken as the daily limit, a sliding window is used for comparison, the line segment current is collected every 10 seconds, and the adjustment is triggered only when the threshold is exceeded for three times in succession to avoid instantaneous glitches; the priority sorting rule is that the node with the greater remaining capacity, the fewer historical faults and the shorter distance to the terminal point is given priority, the system normalizes the three indexes and adds them to obtain a node priority score, and the node with a higher score is arranged into the path first; the path regeneration method is that the nodes at both ends of the overloaded line segment are locked, the temporary weight of the line segment is doubled, and Dijkstra is called again to force the subsequent flow to bypass the bottleneck to generate an optimized line transmission path; for example, at 08:15, the system finds that the real-time current of the "bridge T3-surgery center" segment reaches 12 A for three times in succession, which exceeds the daily threshold of 10 A, and triggers the adjustment, the node B has a remaining capacity of 30 A, no historical fault and the shortest distance to the terminal point C, and has the highest priority score, the node D has a small capacity and a long distance, and has a low priority score, the system doubles the weight of the B-C segment and recalculates, the new path is changed to "power distribution room-bridge T2-standby port-surgery center", the path avoids the original B-C bottleneck, and the maximum current is reduced to 8 A, the optimization is completed, and an optimized line transmission path is obtained.

[0094] It is worth noting that when the system detects multiple line segments concurrently overloaded, a "stepwise weight doubling + batch recalculating" strategy can be used for centralized processing: first, queue the overloaded segments in descending order of the exceeding amplitude in the sliding window, the weight of the segment with the largest amplitude is multiplied by 2, the weight of the segment with the second largest amplitude is multiplied by 1.5, and the weight of the remaining segments is multiplied by 1.2 to form a batch processing sequence; then, Dijkstra is called round by round according to the principle of "main first and then secondary", and only the segment weight in the list of the current round is adjusted in each round, if the recalculation still causes individual segments to exceed the limit, the weight of the segment is continued to be multiplied by 1.5 and the pre-installed standby bridge is simultaneously enabled, and the recalculation is performed again, and the iteration is performed at most three times to press the current of the whole network to below the daily threshold of 10 A; if the phenomenon of the new segment bypassed being overloaded occurs, the system is rolled back to the last feasible topology, and the two segments with the highest concentrated weights are simultaneously reduced by 50% and recalculated in parallel to balance and disperse the flow, ensure that the main path has no bottleneck and only one standby bridge is enabled, and finally the optimized line transmission path is obtained, the current of the optimized line transmission path is all qualified, the version number is incremented and returned to the large screen, and the processing of the multiple line segments concurrently overloaded is completed.

[0095] It is worth mentioning that the normalization operation is completed once at 23:00 every night: the system first scans all network nodes to find the maximum and minimum values of the three original values of "remaining capacity, historical failure times, and distance to the end point"; then the normalization processing is performed on any three data, wherein the remaining capacity is obtained by using the difference between the current value and the minimum value and the difference between the maximum value and the minimum value, the historical failure times and the distance to the end point are obtained by using the difference between the maximum value and the current value and the difference between the maximum value and the minimum value, the three data are mapped to the dimensionless interval of 0-1, and then the three normalized data are added according to the weight of 5:3:2 (the weight setting is based on the path optimization goal setting, and the optimization goals are in turn: large remaining capacity, low historical failure times, and short distance to the end point), forming a node priority score between 0 and 1, and the higher the score, the higher the priority to be arranged into the path.

[0096] In step S14, according to the optimized line transmission path, the energy distribution ratio is calculated, the initial energy utilization efficiency is calculated according to the energy distribution ratio, and the operating room layout map is corrected to obtain an updated operating room layout map, including:

[0097] According to the optimized line transmission path, the energy distribution ratio of each node is calculated to obtain an energy distribution ratio table;

[0098] According to the optimized line transmission path, the lighting device distribution data is extracted to obtain lighting node distribution data;

[0099] According to the energy distribution ratio table, the scheduling operation is performed to calculate the initial energy utilization efficiency of the hospital operating room lighting area;

[0100] If the initial energy utilization efficiency is lower than the preset correction threshold, the efficiency deviation is calculated to obtain an initial efficiency deviation;

[0101] According to the initial efficiency deviation, the lighting node distribution data is corrected by a reverse correction operation to obtain an updated operating room layout map.

[0102] In an implementation, the energy distribution ratio of each node is calculated. The ratio of the optimized line transmission path of each node in the peak period to the total demand of the path is directly taken as the energy distribution ratio. The greater the demand, the more electricity is obtained, and the no flash is ensured. Then, normalization operation is performed. The normalization method is automatically completed by the system. The total value is obtained by accumulating the peak power consumption of all nodes in the path. Then, the power consumption of each node is divided by the total value and the integer percentage is retained. When the sum is less than or exceeds 100%, the difference is sequentially supplemented to the node with the smallest percentage by 0.01 steps until the sum is 100%, ensuring that the output of the power distribution cabinet can be borne by the whole circuit breaker. After generation, it is written into the energy distribution ratio table. The table form is four columns: node number, node position, peak power consumption kilowatt, and energy distribution ratio %. It is directly read by the scheduling module. For example, the optimized path is "power distribution room-bridge T2-backup port-surgery center", corresponding to nodes A, E, F, C, the peak power consumption is 0 kilowatt, 1 kilowatt, 0.5 kilowatt, and 3 kilowatts, respectively, the total demand is 4.5 kilowatts, and the system normalization obtains A accounting for 0%, E accounting for 22%, F accounting for 11%, and C accounting for 67%. The power distribution room outputs the initial allocated energy according to the proportion during scheduling, and the energy distribution ratio calculation is completed.

[0103] The extraction range of the lighting device distribution data extraction is limited to the nodes and their directly adjacent lighting loops passed by the optimized line transmission path, ensuring that only the lamps that may be affected by the new path are grabbed, avoiding data redundancy on the whole floor. The extraction content includes six types of information: node number, node coordinates, lamp model, lamp quantity, peak power consumption, and loop number. The peak power consumption is obtained from the real-time statistics in the previous step, and the loop number is used to correspond to the power distribution cabinet branch, facilitating subsequent scheduling. The data format is CSV, the first row is the column field name, and each subsequent row corresponds to a lamp. The file is named "lighting node distribution data + timestamp" and uploaded to the edge gateway for calling by the correction module. For example, the optimized path is "power distribution room-bridge T2-backup port-surgery center". The system traverses the path and finds that there are three LED shadowless lamps hanging under the bridge T2 node, with coordinates (5, 3), (5, 5), and (7, 3), model LED500, and peak power consumption 0.9 kilowatt each. The loop number is L2. There are two ordinary panel lamps hanging under the backup port node, with coordinates (9, 4) and (9, 6), model PL60, and peak power consumption 0.2 kilowatt each. The loop number is L3. There are four LED shadowless lamps hanging under the surgery center node, with coordinates (12, 4), (12, 6), (14, 4), and (14, 6), model LED500, and peak power consumption 0.9 kilowatt each. The loop number is L4. Finally, the lighting node distribution data file is generated, with a total of 9 records, and the lighting device distribution data extraction is completed.

[0104] The scheduling operation is undertaken by the edge gateway, which converts the energy distribution proportion table into Modbus current limiting instructions and transmits them to the intelligent circuit breakers of the corresponding loops one by one through the RS485 bus. The circuit breakers are closed according to the percentage within 10 seconds, completing the no-flashover switching. After field execution, the gateway continues to collect the actual power of each loop every 5 seconds for 5 minutes, eliminates the first 1-minute transient state, takes the average power consumption of the last 4 minutes as the actual consumption, and reads the voltage, current and power factor to ensure data stability. The initial energy utilization efficiency is defined as the "effective light flux corresponding to the equivalent power consumption" divided by the initial allocated energy consumption. The system first converts the measured light flux into equivalent power consumption according to the light efficiency of the lamp at the factory, and then compares it with the theoretical power consumption allocated in proportion to obtain the percentage efficiency. For example, the proportion table gives L2 loop 22%, L3 loop 12%, and L4 loop 66%, with a total allocated capacity of 4.5 kW. The theoretical power consumption is 0.99 kW, 0.54 kW and 2.97 kW, respectively. The actual L2 is 0.95 kW, the actual L3 is 0.52 kW, and the actual L4 is 2.90 kW. The gateway converts the effective equivalent power consumption to 4.37 kW according to the light efficiency, and the theoretical allocated power consumption is 4.5 kW. The initial energy utilization efficiency is calculated to be 97%, which is higher than the hospital correction threshold of 89%, completing the scheduling operation and efficiency calculation.

[0105] It is worth noting that the measured light flux is provided by the built-in light-sensitive feedback loop in the lamp: each LED shadowless lamp drive board integrates a linear light-sensitive sensor, which samples the chip junction temperature and output light power in real time, converts it into the light flux lm on the surface of the lamp, and returns it to the gateway through the same PoE channel with the power consumption data. The conversion rule uses the factory-calibrated "light efficiency-power" curve. The gateway divides the measured light flux by the rated light efficiency of the lamp to obtain the "equivalent light power consumption", and then adds up the values of all lamps in the same loop to form the effective equivalent power consumption at the loop level, which is used for efficiency calculation. For example, the L4 loop returns a total light flux of 11800 lm under a 2.97 kW allocated power consumption, with a rated light efficiency of 4000 lm / kW. The equivalent light power consumption is 2.95 kW, which is 99% of the theoretical 2.97 kW. The system uses this to participate in the loop-level efficiency statistics to complete the initial energy utilization efficiency calculation.

[0106] The correction threshold is set by the hospital mechanical and electrical department, and the default is 95% of the average utilization efficiency of the same type of operation in the past 30 days. Yesterday, the system calculated the average efficiency of 94%, so today's threshold is set to 89%. If the real-time efficiency is lower than this value, it will trigger the deviation calculation; The initial efficiency deviation is defined as the difference between the correction threshold and the real-time initial energy utilization efficiency, expressed in percentage. The larger the difference, the more serious the waste, which needs to be corrected in reverse subsequently; The initial efficiency deviation calculation process is that the gateway compares with the threshold immediately after the efficiency is obtained in the last step. If it is lower than the threshold, the deviation value is automatically obtained by subtraction, and it is written into the log at the same time. The yellow prompt light of the nurse station is on, reminding the operation and maintenance personnel that the layout correction will be carried out; For example, after scheduling, the system obtains the initial energy utilization efficiency of 85%, which is lower than the threshold of 89% on that day, triggering the deviation calculation. The gateway performs 89% minus 85% to obtain the initial efficiency deviation of 4%. The log records "08:25 initial efficiency deviation 4%, to be corrected", and the yellow light of the nurse station is always on, prompting the on-duty engineer to prepare to adjust the lighting node distribution, and the efficiency deviation calculation is completed.

[0107] It is worth noting that the correction strategy is "logically moved" at the topology level rather than physically shifted. The system proportionally allocates the initial efficiency deviation to each node, and a deviation of 4% represents a 4% waste of over-allocated power. By "virtually approaching", the line loss is reduced, that is, in the topology diagram, the node with high waste is moved one grid (1 meter step) towards the power supply point, and a pre-laid cable is enabled in the standby bridge or parallel line slot to achieve cable length reduction. The physical location of the lamp remains unchanged to avoid affecting the operating room purification and illumination specifications; the node movement rule is "who wastes, who approaches", the gateway first finds the nodes with actual power consumption greater than allocated power, sorts them by difference, and the one with the largest difference is moved 1 grid along the shortest cable in the topology diagram. Recalculate the line loss and equivalent power consumption until the recovery amount reaches the deviation value and stop; the operating room layout update process is automatically completed by the gateway, only the topology node coordinates and standby bridge enable flag are changed, and the "updated operating room layout diagram" logical version is generated, the version number is incremented by 1, and the nurse station large screen is returned; immediately after updating, the circuit topology rationality check is performed: the gateway sends low-voltage detection pulses along the new link to verify whether the starting point to each node remains electrically connected, without open circuit or loop current. If the check fails, roll back to the previous version and alarm; after the check is passed, the large screen prompts "topology has been checked, no open circuit or loop current", and the next path calculation can be performed; for example, the initial efficiency deviation of 4% triggers correction, the system finds that the L4 loop (14, 6) lamp measures 3.1 kW, which is higher than the allocated 2.9 kW, and the difference is the largest. Therefore, in the topology diagram, the node is moved 1 meter along the power supply direction to (13, 6), and the standby bridge T1-T3 segment breakers are automatically closed to shorten the power supply cable by 1 meter, reducing line loss by about 0.18 kW, and recovering 4.1%. Subsequently, the gateway sends a 5-volt direct current pulse to confirm that the distribution room→bridge T1→fresh air shaft→surgery center are all connected and there is no loop current, the check is passed, the topology CAD version number is upgraded from V1.3 to V1.4, and the large screen prompts "correction completed, line loss recovery 4.1%, topology check OK". The on-duty engineer confirms, the updated operating room layout diagram takes effect, and the reverse correction operation is completed.

[0108] In step S15, according to the updated operating room layout diagram, the standby path and standby path load distribution are calculated to obtain a standby path load distribution diagram, including:

[0109] If the connection strength of any priority link in the updated operating room layout diagram is lower than the reset threshold, the link is re-labeled and the information investigation is re-performed to obtain updated real-time location information and energy consumption data of the hospital operating room lighting area;

[0110] According to the updated real-time location information and energy consumption data of the hospital operating room lighting area, the peak time period calculation is re-performed to obtain an updated energy demand peak time period data set;

[0111] According to the updated energy demand peak time period dataset, dynamic path generation and load distribution calculation are performed to obtain a backup path load distribution map.

[0112] In an implementation, the reset threshold is obtained by the system at dawn every day according to a 10% reduction of the average value of the connection strength of the same period in the past 7 days, for discovering potential power weakening caused by layout changes, and the value is 0.49 kilowatts per meter for today. The priority link is triggered to be readjusted once it is lower than the value; the comparison mode adopts a polling mode, the gateway scans the real-time connection strength of all links in the set of priority transmission links every 30 seconds, and if the strength of any pair of nodes is lower than 0.49 kilowatts per meter, the system immediately marks the link as “weak”, and the nurse station flashes yellow to remind; the re-marking rule is “weak link is downgraded, and backup link is upgraded”, the color of the downgraded link is changed from red to orange, the priority is reduced by 1 level, and the occupied backup cable resource is released for subsequent backup path use; the information investigation process is as follows: after being triggered, the gateway first suspends the power supply of the link for 5 seconds, starts a rapid scan, and commands the sensors along the line to upload the latest position coordinates and power consumption within 10 seconds to form updated real-time position information and energy consumption data of the hospital operating room lighting area, and the version number is automatically increased by 1; for example, in the updated operating room layout map V1.4, the real-time strength of the “bridge T2-backup port” link is reduced to 0.45 kilowatts per meter, which is lower than the reset threshold 0.49, the system immediately re-marks the link as orange and downgrades it, and then suspends the power supply for 10 seconds, the sensor returns the new data: the bridge T2 coordinates (9, 4) power consumption 1 kilowatt, the backup port coordinates (10, 6) power consumption 0.5 kilowatt, the gateway packages and generates the updated data set, and completes the re-information investigation to provide the latest input for subsequent backup path calculation.

[0113] It should be noted that the calculation granularity of the peak time period calculation is kept at 1 minute. After the latest position and power consumption data are accessed, the system first eliminates the 5-second abnormal value caused by the research pause, then accumulates the same loop power consumption by minute, and obtains the updated minute-level demand sequence. The sliding window is 30 minutes, and the step is 5 minutes. The system calculates the average demand in each window in turn, and compares it with the dynamic threshold generated at dawn on the same day. The threshold is set by increasing the average demand of the same period of the previous 7 days by 10%, and today is 0.9 kW / m2. If it is higher than this value, it is recorded as a candidate peak window. The threshold refresh rule is "one day more". If a new maximum value appears on the same day, the system stores the value in the history database and uses it for the next day's threshold calculation, ensuring that the peak value definition automatically drifts with the operation volume. For example, after the updated data set is uploaded, the system starts the sliding window from 08:00 and finds that the average demand of the 08:15-08:45 window is 0.95 kW / m2, which is higher than the threshold of 0.9 and is confirmed as a new peak period. The original 08:00-08:30 window has a demand of 0.85 after layout correction, which no longer meets the conditions, so the updated energy demand peak time period data set is recorded as 08:15-08:45, providing the latest time target for subsequent standby path generation.

[0114] The trigger condition for dynamic path generation and load distribution calculation is that once the updated energy demand peak time period data set takes effect, the system automatically starts standby path calculation to prevent the original path from overloading again in the new section. The path generation strategy adopts the "double-channel bypass" principle, taking the power distribution room as the starting point and the high-energy-consuming lighting area as the ending point, and forcibly avoiding the orange-level links that have been marked as "weak". Blue idle bridges and standby cable trenches are preferentially selected, and the edge weight is still the line length, ensuring the shortest and safest path. The load superposition rule writes the measured power consumption of each node in the updated peak period next to the corresponding node, the line segment color gradually changes from cold to warm according to the sum of the node power consumption, and the line width changes from thin to thick according to the current estimation value, forming a visual load distribution. For example, the updated peak period is 08:15-08:45, the system bypasses the orange "bridge T2-standby port" segment, and the new selected path is "power distribution room-bridge T1-new air shaft-surgery center". The power consumption of nodes A, E, G, and C is 0 kW, 0.8 kW, 0.4 kW, and 3 kW, respectively. The A-E segment and the E-G segment are 0.8 kW and 1.2 kW, respectively, which are mapped to light yellow and light orange, and the line width is 1 mm and 2 mm, respectively. The G-C segment and the G-C segment are 3.4 kW, which are mapped to orange-red and the line width is 4 mm. The standby path load distribution diagram is generated, and the operation and maintenance personnel can directly see that the G-C segment is the thickest. If its color continues to deepen, it will trigger a second reinforcement, and the dynamic path generation and load distribution calculation are completed.

[0115] In step S16, according to the backup path load distribution map, a backup energy distribution ratio is calculated, according to the backup energy distribution ratio, a backup energy utilization efficiency is calculated, a deviation of the backup energy utilization efficiency from the initial energy utilization efficiency is calculated, according to the deviation calculation result, the initial path load distribution map and the backup path load distribution map are fused to obtain a comprehensive load distribution map, including:

[0116] If there is a load lower than a preset overload threshold in the backup path load distribution map, a node priority rearrangement operation is performed to obtain an optimized sequence;

[0117] According to the optimized sequence, energy redistribution is performed to obtain the backup energy distribution ratio;

[0118] According to the backup energy distribution ratio, a scheduling operation is performed to obtain a backup energy utilization efficiency of the hospital operating room lighting area;

[0119] According to the initial energy utilization efficiency and the backup energy utilization efficiency, a comparison operation is performed to obtain a backup efficiency deviation;

[0120] According to the backup efficiency deviation, the initial path load distribution map and the backup path load distribution map fusion operation is performed to obtain a comprehensive load distribution map.

[0121] It should be noted that the overload threshold is generated adaptively the night before, taking 80% of the maximum current of the same class in the past 7 days as the daily limit, today is 10A, the comparison method adopts step-by-step scanning, the gateway reads the current of each segment of the standby path every 10 seconds, and it is determined that "the load is lower than the overload threshold" only when it is continuously lower than 10A for 3 times to prevent false triggering due to instantaneous fluctuations; The rearrangement rule is that the three indexes of "remaining capacity, historical failure times, and distance to the end point" are normalized and added, the normalization processing is to find the maximum value and the minimum value of the three original values of "remaining capacity, historical failure times, and distance to the end point" respectively; Then normalize any three data, among which the remaining capacity is obtained by using the difference between the current value and the minimum value and the difference between the maximum value and the minimum value, and the historical failure times and the distance to the end point are obtained by using the difference between the maximum value and the current value and the difference between the maximum value and the minimum value, and the three data are mapped to the 0-1 interval dimensionless, then the three normalized data are added according to the weight of 5:3:2 (the weight setting is based on the path optimization target setting, and the optimization targets are in turn: large remaining capacity, low historical failure times, and short distance to the end point), and the weighted addition is the node priority score, and the higher the score, the higher the priority; For example, the standby path "power distribution room-bridge T1-new air shaft-surgery center" current is measured as 8A for 3 times, which is lower than the threshold, the system immediately calculates the priority score: the normalized remaining capacity of bridge T1 is 0.80, the failure is 0, and the distance is 0.90, the weighted value is 0.80*5+0*3+0.90*2=5.80; The capacity of new air shaft is 0.60, the failure is 0, and the distance is 0.60, the value is 3.00; The capacity of surgery center is 0.40, the failure is 0, and the distance is 0, the value is 2.00, and after comparison, the optimization sequence "bridge T1-new air shaft-surgery center" is formed in descending order of score, and the node priority rearrangement operation is completed.

[0122] The distribution benchmark of energy redistribution is to optimize the measured power consumption demand of each node in the sequence during the update peak period 08:15-08:45, the higher the demand, the higher the priority, to prevent the surgical shadowless lamp from flickering due to insufficient power; the proportional calculation first accumulates the benchmark value of the total demand of the sequence, then divides each node demand by the benchmark value and keeps the integer percentage, if the total is not 100%, automatically supply the node with the smallest proportion by 0.01 steps until the distribution is complete, ensuring that the entire output of the power distribution cabinet is just utilized; the normalization correction is automatically completed by the gateway, generating a three-table form: node number, peak demand kilowatt, backup energy distribution proportion%, named "backup energy distribution proportion table" and latched, to prevent manual errors; for example, the optimization sequence is bridge T1 node E demand 0.8 kilowatt, fresh air shaft node G demand 0.4 kilowatt, surgical area center node C demand 3 kilowatt, total demand 4.2 kilowatt, system initial calculation E accounts for 19%, G accounts for 9%, C accounts for 71%, the total is 99%, supplement 0.01 to the smallest node G, the final backup energy distribution proportion table records: E-19%, G-10%, C-71%, when scheduling, the power distribution room outputs backup distribution energy according to this proportion, completing energy redistribution. Among them, the normalization correction is automatically executed by the gateway every day at 06:00: first, accumulate the peak demand of each node in the optimization sequence to get the total benchmark value, then divide each node demand by the total benchmark value to get the original proportion, the original proportion is rounded to an integer percentage after keeping three decimal places; if the sum of the integer percentages is less than 100%, the system supplies 1% to the node with the smallest proportion by 0.01 steps, until the total is exactly 100%, if there is a tie for the smallest, supply according to the node ID alphabetical order, to ensure that the output capacity of the power distribution cabinet is complete and not excessively occupied; the correction result is written into "backup energy distribution proportion table" and immediately latched, the version number is incremented, manual modification is not allowed, and the normalization correction is completed.

[0123] The scheduling operation is undertaken by the edge gateway, which converts the standby energy distribution ratio table into Modbus current limiting instructions, and sequentially issues them to the corresponding loop intelligent circuit breakers through the RS485 bus. The circuit breakers complete proportional closing within 5 seconds to realize flashless switching. After the field power is limited, the gateway continues to collect the actual power of each loop every 5 seconds for 5 minutes, eliminates the transient data in the first minute, and takes the average value of the last 4 minutes as the actual consumption to ensure stable and reliable data. The standby energy utilization efficiency is defined as the ratio of "actual effective light flux corresponding energy consumption" to "standby distribution energy consumption". For example, the standby ratio table gives the bridge T1 loop 19%, the fresh air well loop 10%, and the surgical area center loop 71%, with a total distribution capacity of 4.2 kW. The theoretical distribution values are 0.8 kW, 0.42 kW, and 2.98 kW, respectively. After the scheduling is executed, the actual measured values are 0.78 kW for the bridge T1, 0.41 kW for the fresh air well, and 2.95 kW for the surgical area center, all close to the theoretical values. The system converts the effective light flux to get the actual utilization of 4.14 kW, and the standby distribution of 4.2 kW. The calculated standby energy utilization efficiency is 98%, higher than the hospital's set target of 95%, completing the scheduling operation and efficiency calculation.

[0124] It should be noted that the purpose of the comparison operation is to quantify the energy saving or waste effect brought by the standby path, providing a quantitative basis for subsequent fusion. If the standby efficiency is higher than the initial efficiency, the standby path is preferentially enabled, otherwise the original path is maintained and the operation and maintenance are prompted; the difference algorithm is automatically completed by the gateway, which directly subtracts the initial energy utilization efficiency from the standby energy utilization efficiency, with the result rounded to one decimal place. A positive number indicates that the standby path is more optimal, while a negative number indicates that the initial path is more optimal. The deviation is recorded in percentage form, and the larger the absolute value of the difference, the greater the potential for path optimization. The system simultaneously writes the deviation value into the log and lights the corresponding indicator light: positive for green light and negative for yellow light, prompting the on-duty personnel of the path selection tendency. The output format is two lines of records: the first line is the initial energy utilization efficiency, and the second line is the standby energy utilization efficiency and the deviation. The file is named "standby efficiency deviation + timestamp" for the fusion module to call. For example, the initial energy utilization efficiency is 97%, the standby energy utilization efficiency is 98%, and the gateway comparison gives 98% minus 97% equal to positive 1%. The system records "standby efficiency deviation: 98%, 97%, +1%" with the green light always on, indicating that the subsequent fusion should increase the weight of the standby path, and completes the standby efficiency deviation acquisition.

[0125] The weight of the fusion operation is based on the standby efficiency deviation, the initial state is set to 100%, the standby path weight is increased by 10 percentage points when the positive deviation, the initial path is decreased by 10 percentage points, and the reverse operation is performed when the negative deviation, to ensure that the better path occupies a thicker and warmer visual effect in the comprehensive map; the color line width recalculation is automatically completed by the gateway, first summing the load of each segment according to the new weight, and then mapping to a unified color band: cold color represents low load, warm color represents high load, line width is divided into three levels according to the current estimated value, 1mm light load, 3mm medium load, 5mm heavy load, forming a load level that can be identified at a glance; the drawing output retains the original CAD base map, but the "comprehensive load" word is superimposed in the center of the line segment, the file name is automatically suffixed with "comprehensive load distribution map", the version number is incremented, and the nurse station large screen is returned synchronously for operation and maintenance personnel to make decisions; for example, the standby efficiency deviation is positive 1%, the system adjusts the initial path weight from 100% to 90%, and the standby path weight from 0% to 110%, the initial load of the "power distribution room-bridge T3-surgery center" segment is reduced from 4kW to 3.6kW, and the standby load of the "power distribution room-bridge T1-ventilation shaft-surgery center" segment is increased from 3.4kW to 3.7kW, the highest segment of the combined load is 3.7kW after merging, which is mapped to orange-red color and line width of 5mm, and the remaining segments decrease in turn, finally generating a comprehensive load distribution map, prompting the operation and maintenance personnel to preferentially reinforce the orange-red segment, completing the fusion operation.

[0126] In step S17, according to the comprehensive load distribution map, a final optimization operation is performed to calculate the final energy utilization efficiency of the operating room lighting area, including:

[0127] If the comprehensive load of a node in the comprehensive load distribution map exceeds the preset integration threshold, a primary node and standby node priority adjustment operation is performed to obtain a final transmission path sequence;

[0128] According to the final transmission path sequence, a final key node locking is performed to obtain a final energy distribution ratio;

[0129] According to the final energy distribution ratio, a scheduling operation is performed to obtain the final energy utilization efficiency of the operating room lighting area.

[0130] In an implementation, the integration threshold is derived by the system at dawn every day according to the maximum value of the comprehensive load of the past 7 days, which is reduced by 15% to capture the section about to overload, and the value of 3.3 kW calculated today triggers the final adjustment if the comprehensive load of any node exceeds this value; the comparison method uses real-time polling, and the gateway scans the comprehensive load distribution diagram of each node every 10 seconds, and if the node load exceeds 3.3 kW, it is confirmed to be overloaded; the priority adjustment rule is to add the scores of the three items of "remaining capacity, less historical failures, and close to the end point", and the master node is sorted together with the standby node, and the one with a high score is promoted to the main path, and the one with a low score is reduced to standby, to ensure that the final path is the most reliable; the path regeneration strategy is to first lock the nodes at both ends of the overloaded node, temporarily double the weight of the master node, and recalculate Dijkstra to force the traffic to bypass the bottleneck and generate the final transmission path sequence. Once the sequence is locked, it is written into the final configuration file, and the version number is incremented by 1; for example, the "new well-surgery center" node in the comprehensive load distribution diagram is measured at 35,000 kW for 3 consecutive times, which exceeds the threshold of 33,000 kW, and the system immediately starts the priority adjustment: the master node C has a remaining capacity of 120,000 kW, no failure, and a distance of 0 meters from the end point, with the highest score to keep the main position; the standby node G has a capacity of 150,000 kW, no failure, and a distance of 5 meters, with the second highest score to promote to the main path; the original master node E has a capacity of 100,000 kW and a distance of 10 meters, with the lowest score to be reduced to standby, and the final transmission path sequence is updated to "power distribution room-bridge T1-new well-surgery center", avoiding the original E segment bottleneck, and completing the priority adjustment operation of the master node and the standby node.

[0131] The lock rule is based on the measured power consumption of each node in the final transmission path sequence during the update peak period 08:15-08:45. The top 3 nodes with the highest power consumption are marked as critical nodes, and the system prioritizes their power supply. The remaining nodes are proportionally reduced to ensure that the surgical spotlight does not flicker off. The proportion reference is the total critical value obtained by adding the power consumption of the critical nodes. Each critical node's power consumption is divided by the total critical value and rounded to two decimal places. If the total is not 100%, the node with the smallest proportion is automatically supplemented by 0.01 steps until the total is reached, ensuring that the power distribution cabinet output is just utilized. The normalization correction is automatically completed by the gateway, generating a three-column table: node number, node location, and final energy distribution percentage. The file is named "Final Energy Distribution Percentage Table" and is locked to prevent manual errors. The version number is synchronized with the path sequence. For example, the final transmission path sequence is "power distribution room-bridge T1-new air shaft-surgical area center". The measured power consumption of nodes A, E, G, and C is 0 kW, 0.9 kW, 0.5 kW, and 3.1 kW, respectively. The system locks E, G, and C as critical nodes, with a total critical value of 3.5 kW. The initial calculation shows that E accounts for 26%, G accounts for 14%, and C accounts for 60%. The total is 100%, and no difference needs to be supplemented. The final energy distribution percentage table records: A-0%, E-26%, G-14%, and C-60%. During scheduling, the power distribution room outputs the final distribution energy according to this proportion, completing the final critical node locking and energy distribution percentage calculation.

[0132] The dispatch operation instruction is issued by the edge gateway, which converts the final energy distribution percentage table into Modbus current limiting instructions and sends them to the corresponding loop intelligent circuit breakers through the RS485 bus. The circuit breakers complete the proportional closing within 5 seconds, realizing flash-off switching and ensuring the continuation of the operation. After current limiting on site, the gateway continues to collect the actual power of each loop every 5 seconds for 5 minutes, excluding the first minute of transient data and taking the average of the last 4 minutes as the actual consumption to ensure stable and reliable data. The current, voltage, and power factor are also recorded. The final energy utilization efficiency is defined as the ratio of "actual effective luminous flux corresponding energy consumption" to "final distribution energy", serving as the final optimization result. For example, the final energy distribution percentage table shows that the bridge T1 loop accounts for 26%, the new air shaft loop accounts for 14%, and the surgical area center loop accounts for 60%, with a total distribution capacity of 3.5 kW. The theoretical distribution values are 0.91 kW, 0.49 kW, and 2.1 kW, respectively. After dispatch execution, the actual measurements are 0.88 kW for bridge T1, 0.48 kW for the new air shaft, and 2.07 kW for the surgical area center, all close to the theoretical values. After converting the effective luminous flux, the system obtains an actual utilization of 3.43 kW and a final distribution of 3.5 kW, resulting in a final energy utilization efficiency of 98% for the operating room lighting area, which is higher than the hospital's set target of 95%. The green light is always on, completing the final optimization operation.

[0133] In summary, the application discloses a hospital energy scheduling method based on a smart energy management platform, real-time position and power consumption data of a surgery room lighting area are collected through deployment of a sensor network, high and low energy consumption areas are divided by using K-means clustering, area connection strength is calculated, a priority transmission link is screened, and an initial shortest path is generated by using a Dijkstra algorithm; when line load exceeds an adaptive threshold, node priorities are immediately rearranged, transmission paths are optimized, energy is accurately distributed according to demand proportion, and lighting node layout is corrected in reverse according to a measured efficiency deviation; further, based on updated data, a standby path and standby path load distribution are calculated, a comprehensive load diagram is obtained by fusing the initial and standby paths, and final proportion scheduling is locked through a terminal key node, thereby solving the problem of energy scheduling imbalance in the prior art.

[0134] With reference to Figure 2 The second embodiment of the application provides a hospital energy scheduling device based on a smart energy management platform, comprising:

[0135] A data preprocessing module is configured to acquire real-time data of a hospital surgery room lighting area, perform classification processing on the real-time data, divide high energy consumption lighting areas and low energy consumption lighting areas, analyze dynamic relationships between the high energy consumption lighting areas and the low energy consumption lighting areas, calculate area connection strength, and obtain an area interconnection strength data set, wherein the real-time data includes real-time position information, energy consumption data, and a surgery room layout map including lighting device distribution points.

[0136] An initial load diagram generation module is configured to screen priority transmission areas according to the area interconnection strength data set, combine the real-time data, calculate an initial shortest path of energy transmission to the high energy consumption lighting areas and calculate an initial path load distribution, and obtain an initial path load distribution diagram.

[0137] A route optimization module is configured to, when line load in the initial path load distribution diagram exceeds a preset line load threshold, adjust priorities of path nodes to obtain an optimized line transmission path.

[0138] A layout update module is configured to calculate an energy distribution proportion according to the optimized line transmission path, calculate an initial energy utilization efficiency according to the energy distribution proportion, and correct the surgery room layout map to obtain an updated surgery room layout map.

[0139] A standby path generation module is configured to calculate a standby path and a standby path load distribution according to the updated surgery room layout map, and obtain a standby path load distribution diagram.

[0140] The comprehensive load calculation module calculates a backup energy distribution ratio according to the backup path load distribution diagram, calculates a backup energy utilization efficiency according to the backup energy distribution ratio, calculates a deviation of the backup energy utilization efficiency from the initial energy utilization efficiency, and fuses the initial path load distribution diagram and the backup path load distribution diagram according to the deviation calculation result to obtain a comprehensive load distribution diagram.

[0141] The final output module performs final optimization operation according to the comprehensive load distribution diagram to calculate a final energy utilization efficiency of the operating room lighting area.

[0142] It should be noted that the hospital energy dispatching device based on the smart energy management platform provided in the embodiments of the present application is used to execute all process steps of the hospital energy dispatching method based on the smart energy management platform in the above embodiments, and the working principles and beneficial effects of the two are one-to-one correspondence, thus not being repeated.

[0143] The embodiments of the present application also provide an electronic device. The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, for example, a hospital energy dispatching program based on a smart energy management platform. The processor implements the steps in each of the hospital energy dispatching method embodiments based on the smart energy management platform when executing the computer program, for example Figure 1 The step S11 shown. Alternatively, the processor implements the functions of each module in each of the above device embodiments when executing the computer program, for example, the data acquisition module.

[0144] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0145] The electronic device can be a desktop computer, a notebook, a palm computer, and a smart tablet, etc. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0146] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, and is a control center of the electronic device, which connects various parts of the electronic device through various interfaces and lines.

[0147] The memory can be used to store the computer programs or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created based on use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash storage device, or other volatile solid-state storage device.

[0148] The modules integrated in the electronic device can be stored in a computer readable storage medium if they are implemented in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0149] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0150] The above-described specific embodiments further illustrate the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the protection scope of the present application. In particular, any modifications, equivalent replacements, improvements, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A hospital energy scheduling method based on a smart energy management platform, characterized in that, The method comprises the following steps: obtaining real-time data of a hospital operating room lighting area, classifying the real-time data, dividing high-energy-consumption lighting areas and low-energy-consumption lighting areas, analyzing the dynamic relationship between the high-energy-consumption lighting areas and the low-energy-consumption lighting areas, calculating the area connection strength, and obtaining the inter-area connection strength data set, wherein the real-time data comprises real-time position information, energy consumption data, and an operating room layout map containing lighting device distribution points; screening a priority transmission area according to the inter-area connection strength data set, combining the real-time data, calculating an initial shortest path of energy transmission to the high-energy-consumption lighting areas and calculating an initial path load distribution to obtain an initial path load distribution map; if there is a line load exceeding a preset line load threshold in the initial path load distribution map, adjusting the priority of the path nodes to obtain an optimized line transmission path; calculating an energy distribution ratio according to the optimized line transmission path, calculating an initial energy utilization efficiency according to the energy distribution ratio, and correcting the operating room layout map to obtain an updated operating room layout map; calculating a backup path and a backup path load distribution according to the updated operating room layout map to obtain a backup path load distribution map; calculating a backup energy distribution ratio according to the backup path load distribution map, calculating a backup energy utilization efficiency according to the backup energy distribution ratio, calculating the deviation of the backup energy utilization efficiency from the initial energy utilization efficiency, and fusing the initial path load distribution map and the backup path load distribution map according to the deviation calculation result to obtain a comprehensive load distribution map; performing final optimization operation according to the comprehensive load distribution map to calculate the final energy utilization efficiency of the operating room lighting area. The calculation of the area connection strength comprises: based on a circuit topology map, calculating the actual cable length between the cluster center of the high-energy-consumption lighting area and the cluster center of the low-energy-consumption lighting area, and taking the average power consumption difference in the dynamic relationship as the energy gradient, and dividing the energy gradient by the actual cable length to obtain the connection strength. 2.The hospital energy scheduling method based on the smart energy management platform of claim 1, wherein, The method of obtaining real-time data of a hospital operating room lighting area, classifying the real-time data, dividing high-energy-consumption lighting areas and low-energy-consumption lighting areas, analyzing the dynamic relationship between the high-energy-consumption lighting areas and the low-energy-consumption lighting areas, and calculating the area connection strength to obtain the inter-area connection strength data set comprises: obtaining real-time data of a hospital operating room lighting area, wherein the real-time data comprises real-time position information, energy consumption data, and an operating room layout map containing lighting device distribution points; classifying the real-time data according to the real-time position information and the energy consumption data of the hospital operating room lighting area by using a K-means clustering algorithm to obtain the classification results of the high-energy-consumption lighting areas and the low-energy-consumption lighting areas; calculating the energy demand values of the areas according to the real-time position information and the energy consumption data of the hospital operating room lighting area to obtain an energy demand peak time period data set; performing inter-area dynamic analysis according to the classification results of the high-energy-consumption lighting areas and the low-energy-consumption lighting areas to obtain the dynamic relationship between the high-energy-consumption lighting areas and the low-energy-consumption lighting areas. According to the dynamic relationship between the high-energy-consumption lighting area and the low-energy-consumption lighting area and the operating room layout, a regional connection strength calculation is performed to obtain a regional connection strength data set. 3.The hospital energy scheduling method based on the smart energy management platform of claim 2, wherein, According to the regional connection strength data set, a priority transmission region is screened out, and the initial shortest path of energy transmission to the high-energy-consumption lighting area is calculated based on the real-time data, and an initial path load distribution diagram is obtained, including: The regions in the regional connection strength data set whose connection strength is higher than a preset strength threshold are screened out and marked as priority transmission regions to obtain a priority transmission link set; According to the energy demand peak time period data set, a shortest transmission path from the energy source to the high-energy-consumption lighting area is calculated using the Dijkstra algorithm to obtain a path node sequence; According to the path node sequence and the energy consumption data, a fusion operation is performed to obtain an initial path load distribution diagram. 4.The hospital energy scheduling method based on the smart energy management platform of claim 1, wherein, According to the optimized line transmission path, an energy distribution ratio is calculated, and based on the energy distribution ratio, an initial energy utilization efficiency is calculated and the operating room layout is corrected to obtain an updated operating room layout, including: According to the optimized line transmission path, an energy distribution ratio of each node is calculated to obtain an energy distribution ratio table; According to the optimized line transmission path, lighting device distribution data is extracted to obtain lighting node distribution data; According to the energy distribution ratio table, a scheduling operation is performed to calculate the initial energy utilization efficiency of the hospital operating room lighting area; If the initial energy utilization efficiency is lower than a preset correction threshold, an efficiency deviation is calculated to obtain an initial efficiency deviation; According to the initial efficiency deviation, the lighting node distribution data is corrected through a reverse correction operation to obtain an updated operating room layout. 5.The hospital energy scheduling method based on the smart energy management platform of claim 1, wherein, According to the updated operating room layout, a backup path and a backup path load distribution are calculated to obtain a backup path load distribution diagram, including: If the connection strength of any priority link in the updated operating room layout is lower than a preset threshold, the link is re-labeled and real-time location information and energy consumption data of the hospital operating room lighting area are obtained through information investigation; According to the updated real-time location information and energy consumption data of the hospital operating room lighting area, a peak time period calculation is performed again to obtain an updated energy demand peak time period data set; According to the updated energy demand peak time period data set, a dynamic path generation and load distribution calculation are performed to obtain a backup path load distribution diagram. 6.The hospital energy scheduling method based on the smart energy management platform of claim 4, wherein, According to the backup path load distribution diagram, a backup energy distribution ratio is calculated, and based on the backup energy distribution ratio, a backup energy utilization efficiency is calculated, and the deviation between the backup energy utilization efficiency and the initial energy utilization efficiency is calculated. According to the deviation calculation result, the initial path load distribution diagram and the backup path load distribution diagram are fused to obtain a comprehensive load distribution diagram, including: If there is a load lower than a preset overload threshold in the backup path load distribution map, a node priority rearrangement operation is performed to obtain an optimized sequence; According to the optimized sequence, an energy redistribution is performed to obtain a backup energy distribution ratio; According to the backup energy distribution ratio, a scheduling operation is performed to obtain a backup energy utilization efficiency of a hospital operating room lighting area; According to the initial energy utilization efficiency and the backup energy utilization efficiency, a comparison operation is performed to obtain a backup efficiency deviation; According to the backup efficiency deviation, a fusion operation of the initial path load distribution map and the backup path load distribution map is performed to obtain a comprehensive load distribution map. 7.The hospital energy scheduling method based on the smart energy management platform of claim 1, wherein, The final energy utilization efficiency of the operating room lighting area is calculated according to the comprehensive load distribution map through a final optimization operation, including: If the comprehensive load of a node in the comprehensive load distribution map exceeds a preset integration threshold, a primary node and backup node priority adjustment operation is performed to obtain a final transmission path sequence; According to the final transmission path sequence, a final key node locking is performed to obtain a final energy distribution ratio; According to the final energy distribution ratio, a scheduling operation is performed to obtain the final energy utilization efficiency of the operating room lighting area.

8. A hospital energy dispatching system based on a smart energy management platform, characterized in that, A hospital energy scheduling method based on a smart energy management platform is implemented, including: A data preprocessing module is configured to obtain real-time data of a hospital operating room lighting area, classify the real-time data, divide high-energy-consuming lighting areas and low-energy-consuming lighting areas, analyze the dynamic relationship between the high-energy-consuming lighting areas and the low-energy-consuming lighting areas, calculate the inter-regional connection strength, and obtain an inter-regional connection strength dataset, wherein the real-time data includes real-time location information, energy consumption data, and an operating room layout map containing lighting device distribution points; An initial load map generation module is configured to filter priority transmission areas according to the inter-regional connection strength dataset, combine the real-time data, calculate an initial shortest path for energy transmission to the high-energy-consuming lighting areas, and calculate an initial path load distribution to obtain an initial path load distribution map; A route optimization module is configured to adjust the priority of path nodes when there is a line load exceeding a preset line load threshold in the initial path load distribution map to obtain an optimized line transmission path; A layout update module is configured to calculate an energy distribution ratio according to the optimized line transmission path, calculate an initial energy utilization efficiency according to the energy distribution ratio, and correct the operating room layout map to obtain an updated operating room layout map; A backup path generation module is configured to calculate a backup path and a backup path load distribution according to the updated operating room layout map to obtain a backup path load distribution map. The integrated load calculation module calculates a backup energy distribution ratio according to the backup path load distribution map, calculates a backup energy utilization efficiency according to the backup energy distribution ratio, calculates a deviation of the backup energy utilization efficiency from the initial energy utilization efficiency, and fuses the initial path load distribution map and the backup path load distribution map according to the deviation calculation result to obtain an integrated load distribution map. The final output module performs final optimization operation according to the integrated load distribution map to calculate a final energy utilization efficiency of the operating room lighting area.

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