Optical storage cooperative scheduling method, system and equipment for intelligent construction site equipment

Abstract

The invention provides a light storage cooperative scheduling method, system and equipment for intelligent construction site equipment, and belongs to the technical field of intelligent construction site energy management. The method comprises the following steps: acquiring multi-dimensional sensing data of each monitoring sub-region; and performing nonlinear correction on the photovoltaic output based on the illumination intensity, the environment temperature and the construction site shielding coefficient corresponding to the monitoring sub-region to obtain the corrected photovoltaic output. According to the historical load data, the real-time working condition data and a load prediction model, determining a load demand curve of each monitoring sub-region in a preset time period; and inputting the corrected photovoltaic output, the load demand curve and the multi-dimensional sensing data into the dynamic weight cooperation model, and determining an output weight triple. And when it is determined that the real-time fluctuation of the output weight triad is greater than a preset threshold value, determining a weight adjustment parameter and / or generating a cross-sub-region power supply relay path based on a preset abnormal level matrix, and storing corresponding scheduling data to a preset database to update the load prediction model and the weight adjustment parameter.

Description

Technical Field

[0001] The present application relates to the field of smart construction site energy management technology, and in particular to a method, system, and equipment for coordinated scheduling of photovoltaic and energy storage equipment for smart construction site equipment. Background Art

[0002] Smart construction site equipment (such as tower cranes and concrete mixing plants) generally have problems such as large fluctuations in power load, insufficient stability of traditional power grid power supply, and high carbon emissions.

[0003] Photovoltaic energy storage (PV-storage) systems, as a clean energy technology, can supplement construction site electricity through photovoltaic power generation. However, low PV-storage synergy efficiency and poor power supply reliability are prominent drawbacks, affected by factors such as sunlight intensity and fluctuations in equipment energy consumption. Furthermore, existing scheduling methods often rely on empirical rules or simple load forecasts, failing to achieve flexible, dynamic, and precise matching of PV output, energy storage status, and equipment energy consumption.

[0004] Based on this, there is an urgent need for a technical solution that can dynamically and accurately match photovoltaic output, energy storage status and equipment energy consumption. Summary of the Invention

[0005] The embodiments of the present application provide a photovoltaic-storage coordinated scheduling method, system, and device for smart construction site equipment, which are used to solve the technical problem of how to dynamically and accurately match photovoltaic output, energy storage status, and equipment energy consumption to provide stable power supply to smart construction site equipment.

[0006] In one aspect, an embodiment of the present application provides a method for coordinated scheduling of photovoltaic and storage equipment for smart construction site equipment, the method comprising: Divide monitoring sub-areas according to the functional zoning and geographic coordinates of the construction site, and obtain multi-dimensional sensing data of each monitoring sub-area through a pre-deployed sensor network at a corresponding preset monitoring frequency; wherein the multi-dimensional sensing data includes at least: photovoltaic output, energy storage SOC, equipment energy consumption, and grid stability coefficient; Based on the light intensity, ambient temperature and construction site shading coefficient corresponding to the monitoring sub-area, the photovoltaic output is nonlinearly corrected to obtain a corrected photovoltaic output; Determine the load demand curve of each monitoring sub-area within a preset time period based on corresponding historical load data, real-time operating condition data and a pre-trained load forecasting model; Inputting the corrected photovoltaic output, the load demand curve, and the multi-dimensional sensing data into a pre-built dynamic weight coordination model to determine an output weight triplet; the output weight triplet includes a photovoltaic output weight, an energy storage output weight, and a grid output weight; When it is determined that the real-time fluctuation of the output weight triplet is greater than a preset threshold, the corresponding weight adjustment parameters are determined and / or a cross-sub-area power supply relay path is generated based on a preset abnormality level matrix, and the corresponding scheduling data is stored in a preset database to update the load forecasting model and the weight adjustment parameters.

[0007] In one implementation of the present application, based on the light intensity, ambient temperature, and construction site shading coefficient corresponding to the monitoring sub-area, a nonlinear correction is performed on the photovoltaic output to obtain a corrected photovoltaic output, specifically including: Calculating a light intensity correction parameter according to the light intensity and the light nonlinear correction coefficient; Calculating an ambient temperature correction parameter according to the ambient temperature and a preset temperature correction coefficient; The corrected photovoltaic output is calculated according to the photovoltaic output, the light intensity correction parameter, the ambient temperature correction parameter and the construction site shading coefficient.

[0008] In one implementation of the present application, before calculating the corrected photovoltaic output based on the photovoltaic output, the light intensity correction parameter, the ambient temperature correction parameter, and the construction site shading coefficient, the method further includes: Obtain the real-time location coordinates of the smart construction site tower crane and the photovoltaic array coordinates of the smart construction site, and unify them into the global coordinate system of the construction site; Obtain real-time solar altitude and solar azimuth angles from user terminals and calculate the solar direction unit vector; Determine the tower body shadow area and boom shadow area corresponding to the smart construction site tower crane based on the real-time position coordinates, the sun direction unit vector and a preset shadow projection model; The shaded area corresponding to the photovoltaic array is determined according to the photovoltaic array coordinates, the tower shadow area and the boom shadow area, so as to determine the construction site shade coefficient corresponding to the corresponding photovoltaic array based on the shaded area and the total area of ​​the photovoltaic array.

[0009] In one implementation of the present application, before determining the load demand curve of each monitoring sub-area within a preset time period based on the corresponding historical load data, real-time operating condition data, and a pre-trained load forecasting model, the method further includes: Determine the area type of the monitoring sub-area corresponding to the load demand curve to be predicted; the area type includes at least a hoisting area, a mixing area, and an auxiliary area; the monitoring sub-area corresponding to the hoisting area includes at least the following smart construction site equipment: a tower crane and a construction elevator; the monitoring sub-area corresponding to the auxiliary area includes at least the following smart construction site equipment: lighting equipment and monitoring equipment; According to the said area type, determine the corresponding model channels, so as to call the first prediction sub-model in the load prediction model to conduct load prediction on the monitored sub-areas in the hoisting area, and call the second prediction sub-model in the load prediction model to conduct load prediction on the monitored sub-areas in the auxiliary area; the required computing power of the first prediction sub-model is at least greater than that of the second prediction sub-model.

[0010] In an implementation manner of the present application, input the corrected photovoltaic output power, the load demand curve, and the energy storage SOC into a pre-constructed dynamic weight collaboration model to determine an output power weight triple, which specifically includes: Taking meeting the load demand curve as the goal, through the dynamic weight collaboration model, according to the corrected photovoltaic output power, the maximum photovoltaic output power, the power grid stability coefficient, the loads of each device in the corresponding monitored sub-area, and the priority coefficient of each device, determine the corresponding photovoltaic output power weight; wherein, the priority coefficient of the device is positively correlated with the energy consumption of the device; Through the dynamic weight collaboration model, according to the energy storage SOC, the loads of each device, the priority coefficient of each device, the corrected photovoltaic output power, and the maximum energy storage output power, determine the corresponding energy storage output power weight; Through the dynamic weight collaboration model, according to the photovoltaic output power weight and the energy storage output power weight, determine the power grid output power weight, and add the photovoltaic output power weight, the energy storage output power weight, and the power grid output power weight to the output power weight triple.

[0011] In an implementation manner of the present application, based on a preset abnormal level matrix, determine the corresponding weight adjustment parameter and / or generate a cross-sub-area power supply relay path, which specifically includes: Preset a first preset fluctuation amplitude F1, a second preset fluctuation amplitude F2, and a third preset fluctuation amplitude F3, and F < F2 < F3, preset a first preset fluctuation duration t1 and a second preset fluctuation duration t2, t1 < t2, and construct the preset abnormal level matrix according to the abnormal level preset by the user and the combination of each preset fluctuation amplitude and each preset fluctuation duration; Determine the real-time fluctuation amplitude corresponding to the output power weight triple and the corresponding fluctuation duration; Compare the real-time fluctuation amplitude with each preset fluctuation amplitude in the preset abnormal level matrix, and compare the fluctuation duration with each preset fluctuation duration in each preset abnormal level matrix, so as to determine the abnormal level according to the comparison result; When the abnormal level is level one, according to a preset temporary wave suppression correction comparison table, determine the corresponding weight adjustment parameter to correct each output power weight in the output power weight triple; the correction includes positive correction and negative correction; When the abnormality level is level 2, the corresponding weight adjustment parameter is determined according to the preset temporary wave suppression correction comparison table, the local output weight in the output weight triplet is corrected, and the cross-sub-area power supply relay path is generated so that other monitoring sub-areas supply power to the corresponding monitoring sub-area; When the abnormality level is level three, the corresponding weight adjustment parameters are determined according to the preset temporary wave suppression correction comparison table, the global output weight in the output weight triplet is corrected, and the cross-sub-area power supply relay path is generated to enable other monitoring sub-areas to supply power to the corresponding monitoring sub-area.

[0012] In one implementation of the present application, generating a cross-sub-area power supply relay path specifically includes: Constructing a construction site topology map of the smart construction site, wherein the nodes of the construction site topology map are the monitoring sub-areas and the edges are the transmission lines; Determining the energy redundancy corresponding to each of the nodes based on the energy storage SOC and the maximum preset energy storage output of each of the nodes; Determining the transmission loss corresponding to each of the edges according to the resistivity corresponding to the transmission line, the distance between each of the edges, and the preset cross-sectional area of ​​the conductor; The abnormal monitoring sub-region in which the real-time fluctuation of the output weight triplet is greater than a preset threshold is selected as a target support region, and based on the load gap value corresponding to the target support region and the energy redundancy corresponding to each monitoring sub-region, one or more candidate support regions in each monitoring sub-region are screened, and if there is only one candidate support region, it is determined as the support region; When there are multiple candidate support areas, determining path weights based on the energy redundancy and corresponding transmission loss of each candidate support area, and selecting support areas based on the path weights; wherein the sum of the energy redundancy corresponding to the multiple support areas is at least greater than the load gap value of the target support area; The cross-sub-area power supply relay path is established according to the edge between the support area and the target support area in the construction site topology map.

[0013] In one implementation of the present application, screening and obtaining the support area according to the path weight specifically includes: When it is determined that there are multiple target support areas, the product value of the device priority coefficient corresponding to the target support area and the corresponding path weight is calculated, and the product value is used as the updated path weight to filter the support area according to the updated path weight.

[0014] In a second aspect, an embodiment of the present application further provides a solar-storage collaborative scheduling system for smart construction site equipment, the system comprising: A division acquisition module is used to divide the monitoring sub-areas according to the functional zoning and geographical coordinates of the construction site, and obtain multi-dimensional sensing data of each monitoring sub-area through a pre-deployed sensor network at a corresponding preset monitoring frequency; wherein the multi-dimensional sensing data includes at least: photovoltaic output, energy storage SOC, equipment energy consumption, and grid stability coefficient; a correction module, configured to perform nonlinear correction on the photovoltaic output based on the light intensity, ambient temperature, and construction site shading coefficient corresponding to the monitoring sub-area to obtain a corrected photovoltaic output; A first determination module is configured to determine a load demand curve for each of the monitoring sub-areas within a preset time period based on corresponding historical load data, real-time operating condition data, and a pre-trained load prediction model; An input determination module is configured to input the corrected photovoltaic output, the load demand curve, and the multi-dimensional sensing data into a pre-built dynamic weight coordination model to determine an output weight triplet; the output weight triplet includes a photovoltaic output weight, an energy storage output weight, and a grid output weight; The second determination module is used to determine the corresponding weight adjustment parameters and / or generate a cross-sub-area power supply relay path based on a preset abnormality level matrix when it is determined that the real-time fluctuation of the output weight triplet is greater than a preset threshold, and store the corresponding scheduling data in a preset database to update the load forecasting model and the weight adjustment parameters.

[0015] In a third aspect, an embodiment of the present application further provides a photovoltaic and storage collaborative scheduling device for smart construction site equipment, the device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a photovoltaic and storage collaborative scheduling method for smart construction site equipment as described above.

[0016] Compared with the prior art, this application has the following significant effects: Through the above technical solution, it is possible to perform photovoltaic and energy storage data perception monitoring on monitoring sub-areas with different functions and locations, adaptively correct photovoltaic output, accurately predict load demand, and then determine the dynamic weights of photovoltaic, energy storage and power grid, and realize accurate coordinated allocation of dynamic weights, so as to carry out more flexible photovoltaic and energy storage coordinated scheduling. At the same time, this application also sets up an abnormal response mechanism, which triggers weight adjustment parameters or cross-sub-area power supply relay paths based on the abnormal level matrix to avoid problems such as one-size-fits-all or lag in traditional abnormal responses. This will achieve dynamic and accurate matching of photovoltaic output, energy storage status and equipment energy consumption, and intelligently carry out photovoltaic and energy storage coordinated scheduling to provide stable power supply for smart construction site equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a flow chart of a method for coordinated scheduling of photovoltaic and storage equipment for smart construction site equipment according to an embodiment of the present application; Figure 2 This is a structural diagram of a photovoltaic and storage collaborative scheduling system for smart construction site equipment in an embodiment of the present application; Figure 3 This is a structural diagram of a photovoltaic and storage collaborative scheduling device for smart construction site equipment in an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] Smart construction site equipment (such as tower cranes and concrete mixing plants) commonly suffers from large power load fluctuations, insufficient traditional grid power supply stability, and high carbon emissions. Photovoltaic energy storage (PV-ESS) systems, as a clean energy technology, can supplement construction site electricity consumption through PV power generation. However, due to factors such as light intensity and equipment energy consumption fluctuations, low PV-ESS synergy efficiency and poor power supply reliability are prominent pain points. Furthermore, existing scheduling methods often rely on empirical rules or simple load forecasts, failing to achieve flexible, dynamic, and precise matching of PV output, energy storage status, and equipment energy consumption.

[0020] Based on this, the embodiments of the present application provide a photovoltaic-storage coordinated scheduling method, system and equipment for smart construction site equipment, which are used to solve the technical problem of how to dynamically and accurately match photovoltaic output, energy storage status and equipment energy consumption to provide stable power supply to smart construction site equipment.

[0021] The following describes in detail various embodiments of the present application with reference to the accompanying drawings.

[0022] The embodiment of the present application provides a method for coordinating solar and energy storage for smart construction site equipment. Figure 1 As shown, the method may include steps S101-S105: S101, the server divides the monitoring sub-areas according to the functional zoning and geographical location coordinates of the construction site, and obtains multi-dimensional perception data of each monitoring sub-area through a pre-deployed sensor network at a corresponding preset monitoring frequency.

[0023] Among them, the multi-dimensional sensing data includes at least: photovoltaic output, energy storage remaining capacity (State of Charge, SOC), equipment energy consumption, and grid stability coefficient.

[0024] The functional zoning of the construction site can be divided according to the type of area, such as the hoisting area, mixing area, auxiliary area, photovoltaic array area, living area, etc., which can be set by the user according to the actual smart construction site, or the functional zoning of the smart construction site can be divided according to the experience of experts. No specific restrictions are made here. At the same time, in order to avoid overlapping or omissions in the monitoring range, the spatial boundaries of each sub-area will be clarified through geographic location coordinates, thereby forming an electronic map of the distribution of monitoring sub-areas in the smart construction site. Subsequently, a sensor network pre-deployed in each monitoring sub-area will be used for data collection. The sensor network may include GPS positioning sensors, inclination sensors, temperature and humidity sensors, current sensors, irradiance sensors, power sensors, voltage sensors, harmonic analysis sensors, etc., which can be set by the user according to the specific needs of the smart construction site, and no specific restrictions are made here.

[0025] Among them, photovoltaic output can be collected through power sensors; energy storage SOC can be collected through voltage sensors and current sensors, and calculated by calling a preset SOC calculation tool, which is specifically set by the user according to the actual usage scenario; equipment energy consumption can be collected through current sensors and voltage sensors, and obtained by calling an energy consumption calculation tool; the grid stability coefficient reflects the stability of indicators such as grid voltage, frequency, and harmonics, and can be collected through voltage sensors, frequency sensors, harmonic analysis sensors, etc., and calculated by an expert system. The above-mentioned photovoltaic output, energy storage SOC, equipment energy consumption, and grid stability coefficient can all be obtained by inputting the collected data into the expert system, and this application does not make specific restrictions on this.

[0026] At the same time, this application will also pre-set monitoring frequencies for different monitoring sub-areas to flexibly collect multi-dimensional perception data for each monitoring sub-area. By pre-setting monitoring frequencies and acquiring data at differentiated collection time intervals, data redundancy or information lag can be avoided, effectively ensuring data timeliness.

[0027] It should be noted that the server, as the executor of the optical-storage collaborative scheduling method for smart construction site equipment, is only an example. The executor is not limited to the server, but can also be an edge computing terminal set up at the smart construction site. This application does not make specific restrictions on this.

[0028] S102: The server performs nonlinear correction on the photovoltaic output based on the light intensity, ambient temperature and construction site shading coefficient corresponding to the monitoring sub-area to obtain the corrected photovoltaic output.

[0029] In the embodiment of the present application, based on the light intensity, ambient temperature, and site shading coefficient corresponding to the monitoring sub-area, a nonlinear correction is performed on the photovoltaic output to obtain the corrected photovoltaic output, specifically including: Calculate the light intensity correction parameter based on the light intensity and the nonlinear light correction coefficient. Calculate the ambient temperature correction parameter based on the ambient temperature and the preset temperature correction coefficient. Calculate the corrected PV output based on the PV output, light intensity correction parameter, ambient temperature correction parameter, and the construction site shading coefficient.

[0030] The specific formula for nonlinear correction of photovoltaic output in this application is as follows: .

[0031] in, Indicates the The corrected PV output of each monitoring sub-area, Indicates the The original photovoltaic output of each monitoring sub-area, Indicates the preset temperature correction coefficient, Indicates the The ambient temperature of each monitoring sub-area, Indicates the preset illumination nonlinear correction coefficient, Indicates the light intensity, Indicates the The construction site occlusion coefficient corresponding to the moment. Indicates the light intensity correction parameter; Indicates the ambient temperature correction parameter.

[0032] Through the above formula, the photovoltaic output can be corrected by combining light intensity, temperature and site-specific shading factors to obtain accurate photovoltaic output.

[0033] Among them, since the tower crane position and the sun angle are constantly changing, the construction site shading coefficient is not an absolute value. Therefore, before calculating the corrected photovoltaic output based on the photovoltaic output, light intensity correction parameter, ambient temperature correction parameter and the construction site shading coefficient, this application can calculate the construction site shading coefficient, including: Obtain the real-time location coordinates of the smart construction site tower crane and the coordinates of the smart construction site's photovoltaic array, and unify them into the site's global coordinate system. Obtain real-time solar altitude and azimuth angles from the user terminal and calculate the solar direction unit vector. Determine the tower and boom shadow areas corresponding to the smart construction site tower crane based on the real-time location coordinates, solar direction unit vector, and a preset shadow projection model. Determine the shaded area corresponding to the photovoltaic array based on the PV array coordinates, tower shadow area, and boom shadow area. Determine the shaded area corresponding to the photovoltaic array, and then determine the construction site shade coefficient corresponding to the photovoltaic array based on the shaded area and the total area of ​​the photovoltaic array.

[0034] In other words, the server can obtain the real-time position coordinates of the tower crane and the photovoltaic array coordinates of the smart construction site through the sensors installed on the tower crane or the information from the user terminal. The real-time position coordinates at least include the tower crane coordinates and the boom end point coordinates. The boom end point coordinates : .

[0035] in, Indicates the crane coordinates corresponding to the boom, is the length of the boom, is the azimuth of the boom, which may be a rotation angle relative to a predetermined direction, such as true north; is the elevation angle of the crane arm, that is, the angle with the horizontal plane. The photovoltaic array coordinates are , The first Vertices, when the photovoltaic array is a rectangular array, the photovoltaic array coordinates need to include the coordinates of four vertices. This application unifies the above coordinates into the global coordinate system of the construction site to facilitate the calculation of shadow occlusion.

[0036] The solar altitude angle and solar azimuth angle can come from the user terminal, which can be a user's mobile phone, computer or other device. The user terminal can connect to other sensors or the Internet to obtain the solar altitude angle at the current time under the local latitude and longitude of the smart construction site. , azimuth . Then calculate the sun direction unit vector , where the solar altitude angle at the horizon is 0 degrees, and the value range is 0 to 90 degrees. The solar azimuth angle in the south direction is 0 degrees, and the west direction is positive. The solar azimuth angle ranges from -180° to 180°.

[0037] Subsequently, the server can call the preset shadow projection model to calculate the shadow area. Specifically, the preset shadow projection model regards the tower crane body as a cylinder with a radius of , the height is , its projection on the ground is abstracted as an ellipse, and its shadow center coordinates : , , long axis , short axis , and get the shadow area of ​​the tower body. For the crane arm, its shadow is a strip area formed along a shadow line segment on the ground. As the starting point, with the shadow coordinates For the end point, The calculation formula is as follows: .

[0038] The server is based on the preset length and width , get the width of the shadow on the ground The shadow area of ​​the boom is along the two sides of the shadow line segment. strip area.

[0039] Subsequently, the server will calculate whether the coordinates of the photovoltaic array are in the shadow area of ​​the tower body and the shadow area of ​​the crane arm. This can be determined by calculating the distance from the vertex of the photovoltaic array to the center point of the shadow area, or by using a pre-trained neural network model to determine whether the photovoltaic array area overlaps with the shadow area. This application does not make specific restrictions on this. When the photovoltaic array area and the shadow area overlap, the area of ​​the overlapping area will be calculated. The area of ​​the overlapping area is the blocked area. This application can output the blocked area through the above-mentioned neural network model, or can use computer vision algorithms to identify the shadow area and calculate the blocked area through image acquisition and other methods. The algorithm can be deployed in the image acquisition device, and this application does not make specific restrictions on this.

[0040] The server then calculates the The construction site shading coefficient is calculated, where: Indicates the preset attenuation coefficient, which is set by the user based on expert experience; It represents the overlapping area and value of the shadow areas of the photovoltaic array area, the tower body shadow area, and the boom shadow area. Represents the total area of ​​the photovoltaic array.

[0041] S103, the server determines the load demand curve of each monitoring sub-area within a preset time period based on the corresponding historical load data, real-time operating condition data and a pre-trained load forecasting model.

[0042] In an embodiment of the present application, before determining the load demand curve of each monitoring sub-area within a preset time period based on the corresponding historical load data, real-time operating condition data, and a pre-trained load forecasting model, the method further includes: Determine the area type of the monitoring sub-area corresponding to the load demand curve to be predicted. Area types include at least the hoisting area, mixing area, and auxiliary area. The monitoring sub-area corresponding to the hoisting area includes at least the following smart construction site equipment: tower cranes and construction elevators. The monitoring sub-area corresponding to the auxiliary area includes at least the following smart construction site equipment: lighting equipment and monitoring equipment. Based on the area type, determine the corresponding model channel to call the first prediction sub-model in the load prediction model to perform load prediction on the hoisting area monitoring sub-area, and call the second prediction sub-model in the load prediction model to perform load prediction on the auxiliary area monitoring sub-area. The required computing power of the first prediction sub-model is at least greater than that of the second prediction sub-model.

[0043] That is to say, for different regional types, different sub-models in the load forecasting model can be used for forecasting processing, where the first forecasting sub-model can be a pre-trained long short-term memory network (Long Short-Term Memory, LSTM) model, and the second forecasting sub-model can be a model with less computing power requirements, such as the ARIMA model, exponential smoothing, etc. The specific model algorithm used can also be adjusted in the actual usage scenario, and this application does not make specific restrictions on this. By selecting different models to process data of different regional types and perform load forecasting, accurate forecasts can be made for important areas, while load forecasts that maintain computing efficiency can be made for ordinary areas, avoiding waste of computing power and improving the data processing efficiency of photovoltaic and storage collaborative scheduling.

[0044] The above-mentioned historical load data may be pre-stored in a database connected to the server. When executing the load demand curve, the server can access the historical load data from this database. Real-time operating condition data, such as the temperature, humidity, light intensity, number of operating devices, and device power level of each monitoring sub-area, is set by the user based on the scenario and is not specifically limited here. By inputting historical load data and real-time operating condition data over a period of time into the load forecasting model, the load forecasting model can output the load demand curve for the preset period.

[0045] In step S104 , the server inputs the corrected photovoltaic output, load demand curve, and multi-dimensional sensing data into a pre-built dynamic weight coordination model to determine an output weight triplet.

[0046] Among them, the output weight triplet includes photovoltaic output weight, energy storage output weight and grid output weight.

[0047] In the embodiment of the present application, the corrected photovoltaic output, load demand curve, and energy storage SOC are input into a pre-built dynamic weight coordination model to determine the output weight triplet, specifically including: With the goal of meeting the load demand curve, the dynamic weight coordination model is used to determine the corresponding photovoltaic output weight based on the corrected photovoltaic output, maximum photovoltaic output, grid stability coefficient, load of each device in the corresponding monitoring sub-area, and priority coefficient of each device. Among them, the device priority coefficient is positively correlated with the energy consumption of the device. The dynamic weight coordination model is used to determine the corresponding energy storage output weight based on the energy storage SOC, the load of each device, the priority coefficient of each device, the corrected photovoltaic output, and the maximum energy storage output. The dynamic weight coordination model is used to determine the grid output weight based on the photovoltaic output weight and the energy storage output weight. The photovoltaic output weight, energy storage output weight, and grid output weight are added to the output weight triplet. Specifically, the dynamic weighted collaborative model can calculate the output weights for the three dimensions of photovoltaics, energy storage, and power grid, using the load demand value at each moment in the aforementioned load demand curve as a target. This involves multiplying the three adjusted output weights by the processing power of the three dimensions, and comparing the sum of the product values ​​of each dimension with the corresponding load demand value in the load demand curve. This correspondence refers to the time when the two comparison values ​​correspond. If at least the sum of the product values ​​of each dimension is greater than the load demand value in the load demand curve, the output weight is determined to meet the requirements. Otherwise, the output weights of the three dimensions will continue to be calculated and updated until the sum of the product values ​​of each dimension is greater than the load demand value in the load demand curve.

[0048] The calculation formula for photovoltaic output weight is as follows: .

[0049] in, Indicates the The photovoltaic output weight of each monitoring sub-area, For the The maximum photovoltaic output of each monitoring sub-area, represents the grid stability coefficient, Indicates the Device priority coefficient for class devices, Indicates the Equipment load of class equipment, Indicates the total number of devices of different categories. Categories can be classified according to the energy consumption of the devices.

[0050] The calculation formula for energy storage output weight is as follows: .

[0051] in, Indicates the The energy storage output weight of each monitoring sub-area, Indicates energy storage SOC, Indicates the The maximum energy storage output preset for each monitoring sub-area.

[0052] The grid output weight is: .

[0053] The above-mentioned photovoltaic output weight, energy storage output weight, and grid output weight are used to adjust the photovoltaic output, energy storage output, and grid output, respectively. The photovoltaic output, energy storage output, and grid output can be understood as power, with the unit being kilowatts (kW). After obtaining the output weight triplet containing the photovoltaic output weight, energy storage output weight, and grid output weight, the maximum output power of the photovoltaic system can be corrected, the discharge threshold and charge and discharge current in the energy storage system can be adjusted, and the maximum power of the grid can be adjusted. That is, the present application can specifically adjust the scheduling parameters in the current photovoltaic-storage coordinated scheduling strategy based on the output weight triplet. The specific adjustment method can be obtained through expert experience, and the present application does not specifically limit this.

[0054] S105. When the server determines that the real-time fluctuation of the output weight triplet is greater than a preset threshold, it determines the corresponding weight adjustment parameters and / or generates a cross-sub-area power supply relay path based on the preset abnormality level matrix, and stores the corresponding scheduling data in the preset database to update the load forecasting model and weight adjustment parameters.

[0055] Among them, the server can use a preset fluctuation detection time window to perform statistical analysis on the real-time fluctuation of the output weight triplet, and calculate the continuous change of the output weight within the fluctuation detection time window. The continuous change of the output weight can be obtained by deriving the weight change curve of each output weight within the fluctuation detection time window, and adding the absolute values ​​of the three derivative results. The continuous change of the output weight is used as the fluctuation value corresponding to the real-time fluctuation. Subsequently, the fluctuation value of the real-time fluctuation is compared with a preset threshold value. The preset threshold value can be set by the user or obtained based on expert experience. The preset threshold value can be regularly updated based on expert experience, and this application does not make specific restrictions on this.

[0056] In the embodiment of the present application, based on the preset abnormality level matrix, determining the corresponding weight adjustment parameters and / or generating the cross-sub-area power supply relay path specifically includes: Preset a first preset fluctuation amplitude F1, a second preset fluctuation amplitude F2, and a third preset fluctuation amplitude F3, and F1 < F2 < F3. Preset a first preset fluctuation duration t1 and a second preset fluctuation duration t2, where t1 < t2. Then, construct a preset abnormal level matrix according to the abnormal level preset by the user and the combinations of each preset fluctuation amplitude and each preset fluctuation duration. Determine the real-time fluctuation amplitude and the corresponding fluctuation duration corresponding to the output power weight triple. Compare the real-time fluctuation amplitude with each preset fluctuation amplitude in the preset abnormal level matrix, and compare the fluctuation duration with each preset fluctuation duration in each preset abnormal level matrix to determine the abnormal level according to the comparison results. When the abnormal level is level one, determine the corresponding weight adjustment parameter according to the preset temporary wave suppression correction comparison table to correct each output power weight in the output power weight triple. The correction includes positive correction and negative correction. When the abnormal level is level two, determine the corresponding weight adjustment parameter according to the preset temporary wave suppression correction comparison table, correct the local output power weight in the output power weight triple, and generate a cross-sub-region power supply relay path to enable other monitoring sub-regions to supply power to the corresponding monitoring sub-region. When the abnormal level is level three, determine the corresponding weight adjustment parameter according to the preset temporary wave suppression correction comparison table, correct the global output power weight in the output power weight triple, and generate a cross-sub-region power supply relay path to enable other monitoring sub-regions to supply power to the corresponding monitoring sub-region. The preset temporary wave suppression correction comparison table can be set by the user according to the actual usage scenario, and includes the corresponding relationship between different abnormal levels and the correction step size of the weight adjustment parameter. The corresponding correction step size can be obtained through the preset temporary wave suppression correction comparison table to perform correction processing on the weight adjustment parameter.

[0057] In other words, this application presets a preset abnormal level matrix including the corresponding relationship between the preset fluctuation amplitude, the preset fluctuation duration, and the abnormal level. The preset abnormal level matrix represents the first preset fluctuation amplitude F1, the second preset fluctuation amplitude F2, and the third preset fluctuation amplitude F3, as well as the first preset fluctuation duration t1 and the second preset fluctuation duration t2 through different coding values. The server will further determine the real-time fluctuation amplitude of the output power weight triple and the fluctuation duration. Subsequently, the abnormal level is determined by comparing the real-time fluctuation amplitude and the fluctuation duration with the preset abnormal level matrix. The comparison results are shown in Table 1 below.

[0058] Table 1 Comparison Result Table

[0059] For different abnormal levels, this application will adopt different abnormal handling methods to flexibly handle different abnormal fluctuations, so as to make the photovoltaic and energy storage collaborative scheduling more flexible.

[0060] It should be noted that the above-mentioned local output weight can be understood as part of the output weight in the output weight triplet, and the global output weight represents all the output weights in the output weight triplet.

[0061] Furthermore, for Level 2 and Level 3 anomaly conditions, the generation of cross-subregion power relay paths differs. For example, for Level 2 anomalies, paths are prioritized to those in other monitored subregions with high energy redundancy and close proximity. For Level 3 anomalies, paths are prioritized to those in other monitored subregions with high transmission capacity (e.g., conductor cross-sectional area greater than 50 square millimeters) and high reliability (e.g., historical transmission failure rate less than 1%). The specific settings can be determined by the user based on actual usage scenarios and are not specified here.

[0062] In one embodiment of the present application, generating the cross-sub-area power supply relay path specifically includes: A construction site topology map for the smart construction site is constructed, with nodes representing monitoring sub-areas and edges representing transmission lines. The energy redundancy corresponding to each node is determined based on the energy storage SOC and the maximum preset energy storage output of each node. The transmission loss corresponding to each edge is determined based on the corresponding resistivity of the transmission line, the distance between edges, and the preset cross-sectional area of ​​the conductor. Abnormal monitoring sub-areas with real-time fluctuations in output weight triples greater than a preset threshold are designated as target support areas. Based on the load gap value corresponding to the target support area and the energy redundancy corresponding to each monitoring sub-area, one or more candidate support areas are selected from each monitoring sub-area. If there is only one candidate support area, it is selected as the support area. If there are multiple candidate support areas, path weights are determined based on the energy redundancy and corresponding transmission loss of each candidate support area. Support areas are selected based on the path weights. The sum of the energy redundancy corresponding to the multiple support areas must be at least greater than the load gap value of the target support area. Cross-sub-area power supply relay paths are established based on the edges between the support areas and the target support area in the construction site topology map.

[0063] That is to say, the application constructs the site topology map in advance, then calculates the energy redundancy and transmission loss, and then calculates the path weight, thereby generating the shortest power supply path from the support area to the target support area that needs to be supported. Calculated, Indicates the The energy storage SOC of each monitoring sub-area, Indicates the The energy redundancy of each monitoring sub-area. The transmission loss of the edge is calculated by Calculated, Indicates the The monitoring sub-area and The transmission loss of the edges between the monitoring sub-areas, represents the resistivity, Indicates the The monitoring sub-area and The distance between the edges of the monitoring sub-regions, Indicates the The monitoring sub-area and The preset cross-sectional area of ​​the transmission line conductor between the monitoring sub-areas.

[0064] Subsequently, the server will further calculate the actual load demand at this time through the load forecasting model, and calculate the actual output power (output) and value of photovoltaic, energy storage, and power grid at this time respectively, and calculate the difference between the actual load demand and the actual output power and value to obtain the load gap value.

[0065] Before screening one or more candidate support areas in each monitoring sub-area based on the load gap value corresponding to the target support area and the energy redundancy corresponding to each monitoring sub-area, if the load gap value is negative, it indicates an energy supply surplus. At this time, it is modified from the target support area label to a candidate support area for screening candidate support areas; if the load gap value is positive, it indicates insufficient energy supply. At this time, it is used as a target support area.

[0066] The server then compares the load gap value with the energy redundancy of other monitored sub-regions and selects one or more candidate support regions whose combined energy redundancy is at least greater than the load gap value. If there is only one candidate support region, then the candidate region's own energy redundancy is greater than the load gap value.

[0067] If there are multiple candidate support areas, Calculate the path weight, Indicates the The monitoring sub-area and The supporting areas are selected based on the path weights of the monitoring sub-areas from largest to smallest, ensuring that the sum of the energy redundancy of the supporting areas is at least greater than the load gap value. Based on the selected supporting areas and the construction site topology, a cross-sub-area power supply relay path is established. This generates a path map that includes the power supply from the other monitoring sub-areas selected as supporting areas to the target supporting area.

[0068] In addition, since a smart construction site may have multiple locations under construction at the same time, there may be multiple target support areas. Therefore, this application selects support areas based on path weights, specifically including: When it is determined that there are multiple target support areas, the product value of the device priority coefficient corresponding to the target support area and the corresponding path weight is calculated, and the product value is used as the updated path weight to filter the support area according to the updated path weight.

[0069] That is to say, if there are multiple target support areas in this application, a preemption conflict may occur when screening the support areas. At this time, this application adds a device priority coefficient, calculates the product value of the path weight and the device priority coefficient, and further adds priority factors related to the target support area to the support area, thereby giving priority to supporting and replenishing power to the target support area with a larger updated path weight to meet the load gap of the target support area.

[0070] In addition, the present application also stores the scheduling data in a preset database electrically connected to the server, thereby serving as historical data for retraining the load forecasting model and for reference by experts or users to update weight adjustment parameters.

[0071] Through the above technical solution, this application can perform photovoltaic and energy storage data perception monitoring on monitoring sub-areas with different functions and locations, and make adaptive corrections to photovoltaic output, accurately predict load demand, and then determine the dynamic weights of photovoltaic, energy storage and power grid, to achieve accurate coordinated allocation of dynamic weights, so as to carry out photovoltaic and energy storage coordinated scheduling more flexibly. At the same time, this application also sets up an abnormal response mechanism, which triggers weight adjustment parameters or cross-sub-area power supply relay paths based on the abnormal level matrix, avoiding problems such as one-size-fits-all or lag in traditional abnormal responses. This will achieve dynamic and accurate matching of photovoltaic output, energy storage status and equipment energy consumption, and intelligently carry out photovoltaic and energy storage coordinated scheduling to provide stable power supply for smart construction site equipment.

[0072] Through the full-process design of "precise perception-intelligent correction-dynamic allocation-abnormal coordination-closed-loop optimization", this application significantly improves the accuracy, stability and economy of the coordinated scheduling of photovoltaic and storage systems in smart construction sites, providing reliable technical support for the efficient use of energy in complex construction site scenarios.

[0073] Figure 2 A schematic diagram of the structure of a photovoltaic storage collaborative scheduling system for smart construction site equipment provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the photovoltaic and storage coordinated scheduling system 200 for smart construction site equipment includes: The division acquisition module 201 is used to divide the monitoring sub-areas into functional zones and geographic coordinates of the construction site. It then uses a pre-deployed sensor network at a preset monitoring frequency to acquire multidimensional sensing data from each monitoring sub-area. This multidimensional sensing data includes at least photovoltaic output, energy storage SOC, equipment energy consumption, and the grid stability coefficient. The correction module 202 is used to perform nonlinear correction on the photovoltaic output based on the corresponding light intensity, ambient temperature, and construction site shading coefficient of the monitoring sub-area to obtain the corrected photovoltaic output. The first determination module 203 is used to determine the load demand curve for each monitoring sub-area within a preset time period based on corresponding historical load data, real-time operating condition data, and a pre-trained load forecasting model. The input determination module 204 is used to input the corrected photovoltaic output, load demand curve, and multidimensional sensing data into a pre-built dynamic weight coordination model to determine the output weight triplet. The output weight triplet includes the photovoltaic output weight, the energy storage output weight, and the grid output weight. The second determination module 205 is used to determine the corresponding weight adjustment parameters and / or generate a cross-sub-area power supply relay path based on a preset abnormality level matrix when it is determined that the real-time fluctuation of the output weight triplet is greater than a preset threshold, and store the corresponding scheduling data in a preset database to update the load forecasting model and weight adjustment parameters.

[0074] Figure 3 A schematic diagram of the structure of a photovoltaic storage collaborative scheduling device for smart construction site equipment provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the equipment includes: At least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Monitoring sub-areas are divided according to the construction site's functional zoning and geographic coordinates. Multidimensional sensing data from each monitoring sub-area is acquired through a pre-deployed sensor network at a preset monitoring frequency. This multi-dimensional sensing data includes at least photovoltaic output, energy storage SOC, equipment energy consumption, and the grid stability coefficient. Based on the corresponding light intensity, ambient temperature, and site shading coefficient for the monitoring sub-area, a nonlinear correction is performed on the photovoltaic output to obtain the corrected photovoltaic output. The load demand curve for each monitoring sub-area within a preset time period is determined based on historical load data, real-time operating condition data, and a pre-trained load forecasting model. The corrected photovoltaic output, load demand curve, and multi-dimensional sensing data are input into a pre-built dynamic weight coordination model to determine an output weight triplet. The output weight triplet includes the photovoltaic output weight, energy storage output weight, and grid output weight. When the real-time fluctuation of the output weight triplet is determined to exceed a preset threshold, the system determines corresponding weight adjustment parameters and / or generates a cross-sub-area power relay path based on a preset anomaly level matrix. The corresponding scheduling data is stored in a pre-set database to update the load forecasting model and weight adjustment parameters.

[0075] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system and device embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the partial description of the method embodiments.

[0076] The systems, devices, and methods provided in the embodiments of the present application correspond one-to-one. Therefore, the systems and devices also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and devices will not be repeated here.

[0077] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0078] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for coordinated scheduling of photovoltaic and storage equipment for smart construction sites, characterized in that: The method comprises: Divide monitoring sub-areas according to the functional zoning and geographic coordinates of the construction site, and obtain multi-dimensional sensing data of each monitoring sub-area through a pre-deployed sensor network at a corresponding preset monitoring frequency; wherein the multi-dimensional sensing data includes at least: photovoltaic output, energy storage SOC, equipment energy consumption, and grid stability coefficient; Based on the light intensity, ambient temperature and construction site shading coefficient corresponding to the monitoring sub-area, the photovoltaic output is nonlinearly corrected to obtain a corrected photovoltaic output; Determine the load demand curve of each monitoring sub-area within a preset time period based on corresponding historical load data, real-time operating condition data and a pre-trained load forecasting model; Inputting the corrected photovoltaic output, the load demand curve, and the multi-dimensional sensing data into a pre-built dynamic weight coordination model to determine an output weight triplet; the output weight triplet includes a photovoltaic output weight, an energy storage output weight, and a grid output weight; When it is determined that the real-time fluctuation of the output weight triplet is greater than a preset threshold, the corresponding weight adjustment parameters are determined and / or a cross-sub-area power supply relay path is generated based on a preset abnormality level matrix, and the corresponding scheduling data is stored in a preset database to update the load forecasting model and the weight adjustment parameters.

2. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction sites according to claim 1, characterized in that: Based on the light intensity, ambient temperature and construction site shading coefficient corresponding to the monitoring sub-area, the photovoltaic output is nonlinearly corrected to obtain the corrected photovoltaic output, specifically including: Calculating a light intensity correction parameter according to the light intensity and the light nonlinear correction coefficient; Calculating an ambient temperature correction parameter according to the ambient temperature and a preset temperature correction coefficient; The corrected photovoltaic output is calculated according to the photovoltaic output, the light intensity correction parameter, the ambient temperature correction parameter and the construction site shading coefficient.

3. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction site equipment according to claim 2, characterized in that: Before calculating the corrected photovoltaic output based on the photovoltaic output, the light intensity correction parameter, the ambient temperature correction parameter, and the construction site shielding coefficient, the method further includes: Obtain the real-time location coordinates of the smart construction site tower crane and the photovoltaic array coordinates of the smart construction site, and unify them into the global coordinate system of the construction site; Obtain real-time solar altitude and solar azimuth angles from user terminals and calculate the solar direction unit vector; Determine the tower body shadow area and boom shadow area corresponding to the smart construction site tower crane based on the real-time position coordinates, the sun direction unit vector and a preset shadow projection model; The shaded area corresponding to the photovoltaic array is determined according to the photovoltaic array coordinates, the tower shadow area and the boom shadow area, so as to determine the construction site shade coefficient corresponding to the corresponding photovoltaic array based on the shaded area and the total area of ​​the photovoltaic array.

4. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction sites according to claim 1, characterized in that: Before determining the load demand curve of each monitoring sub-area within a preset time period based on the corresponding historical load data, real-time operating condition data, and a pre-trained load forecasting model, the method further includes: Determine the regional type of the monitored sub-region corresponding to the load demand curve to be predicted; the regional type at least includes a hoisting area, a mixing area, and an auxiliary area; the monitored sub-region corresponding to the hoisting area at least includes the following intelligent construction site equipment: tower crane, construction elevator; the monitored sub-region corresponding to the auxiliary area at least includes the following intelligent construction site equipment: lighting equipment, monitoring equipment; According to the regional type, determine the corresponding model channels to call the first prediction sub-model in the load prediction model to perform load prediction on the monitored sub-region of the hoisting area, and call the second prediction sub-model in the load prediction model to perform load prediction on the monitored sub-region of the auxiliary area; the required computing power of the first prediction sub-model is at least greater than that of the second prediction sub-model.

5. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction site equipment according to claim 1, characterized in that: Input the corrected photovoltaic output, the load demand curve, and the energy storage SOC into a pre-constructed dynamic weight collaboration model to determine the output weight triple, specifically including: With the goal of meeting the load demand curve, through the dynamic weight collaboration model, according to the corrected photovoltaic output, the maximum photovoltaic output, the grid stability coefficient, the loads of each device in the corresponding monitored sub-region, and the priority coefficient of each device, determine the corresponding photovoltaic output weight; wherein, the device priority coefficient is positively correlated with the device energy consumption; Through the dynamic weight collaboration model, according to the energy storage SOC, the loads of each device, the priority coefficient of each device, the corrected photovoltaic output, and the maximum energy storage output, determine the corresponding energy storage output weight; Through the dynamic weight collaboration model, according to the photovoltaic output weight and the energy storage output weight, determine the grid output weight, and add the photovoltaic output weight, the energy storage output weight, and the grid output weight to the output weight triple.

6. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction sites according to claim 1, characterized in that: Based on a preset abnormal level matrix, determine the corresponding weight adjustment parameter and / or generate a cross-sub-region power supply relay path, specifically including: Preset a first preset fluctuation amplitude F1, a second preset fluctuation amplitude F2, and a third preset fluctuation amplitude F3, and F1 < F2 < F3, preset a first preset fluctuation duration t1 and a second preset fluctuation duration t2, t1 < t2, and construct the preset abnormal level matrix according to the abnormal level preset by the user and the combination of each preset fluctuation amplitude and each preset fluctuation duration; Determine the real-time fluctuation amplitude corresponding to the output weight triple and the corresponding fluctuation duration; Compare the real-time fluctuation amplitude with each preset fluctuation amplitude in the preset abnormal level matrix, and compare the fluctuation duration with each preset fluctuation duration in each preset abnormal level matrix to determine the abnormal level according to the comparison results; When the abnormal level is level one, determine the corresponding weight adjustment parameter according to a preset temporary wave suppression correction comparison table to correct each output weight in the output weight triple; the correction includes positive correction and negative correction; When the abnormality level is level 2, the corresponding weight adjustment parameter is determined according to the preset temporary wave suppression correction comparison table, the local output weight in the output weight triplet is corrected, and the cross-sub-area power supply relay path is generated so that other monitoring sub-areas supply power to the corresponding monitoring sub-area; When the abnormality level is level three, the corresponding weight adjustment parameters are determined according to the preset temporary wave suppression correction comparison table, the global output weight in the output weight triplet is corrected, and the cross-sub-area power supply relay path is generated to enable other monitoring sub-areas to supply power to the corresponding monitoring sub-area.

7. A method for coordinated scheduling of photovoltaic and storage equipment for smart construction site equipment according to any one of claims 1 to 6, characterized in that: Generate cross-sub-region power supply relay paths, including: Constructing a construction site topology map of the smart construction site, wherein the nodes of the construction site topology map are the monitoring sub-areas and the edges are the transmission lines; Determining the energy redundancy corresponding to each of the nodes based on the energy storage SOC and the maximum preset energy storage output of each of the nodes; Determining the transmission loss corresponding to each of the edges according to the resistivity corresponding to the transmission line, the distance between each of the edges, and the preset cross-sectional area of ​​the conductor; The abnormal monitoring sub-region in which the real-time fluctuation of the output weight triplet is greater than a preset threshold is selected as a target support region, and based on the load gap value corresponding to the target support region and the energy redundancy corresponding to each monitoring sub-region, one or more candidate support regions in each monitoring sub-region are screened, and if there is only one candidate support region, it is determined as the support region; When there are multiple candidate support areas, determining path weights based on the energy redundancy and corresponding transmission loss of each candidate support area, and selecting support areas based on the path weights; wherein the sum of the energy redundancy corresponding to the multiple support areas is at least greater than the load gap value of the target support area; The cross-sub-area power supply relay path is established according to the edge between the support area and the target support area in the construction site topology map.

8. The method for coordinated scheduling of photovoltaic and storage equipment for smart construction sites according to claim 7, characterized in that: Based on the path weights, support areas are screened, including: When it is determined that there are multiple target support areas, the product value of the device priority coefficient corresponding to the target support area and the corresponding path weight is calculated, and the product value is used as the updated path weight to filter the support area according to the updated path weight.

9. A solar-storage collaborative scheduling system for smart construction site equipment, characterized in that: The system comprises: A division acquisition module is used to divide the monitoring sub-areas according to the functional zoning and geographical coordinates of the construction site, and obtain multi-dimensional sensing data of each monitoring sub-area through a pre-deployed sensor network at a corresponding preset monitoring frequency; wherein the multi-dimensional sensing data includes at least: photovoltaic output, energy storage SOC, equipment energy consumption, and grid stability coefficient; a correction module, configured to perform nonlinear correction on the photovoltaic output based on the light intensity, ambient temperature, and construction site shading coefficient corresponding to the monitoring sub-area to obtain a corrected photovoltaic output; A first determination module is configured to determine a load demand curve for each of the monitoring sub-areas within a preset time period based on corresponding historical load data, real-time operating condition data, and a pre-trained load prediction model; An input determination module is configured to input the corrected photovoltaic output, the load demand curve, and the multi-dimensional sensing data into a pre-built dynamic weight coordination model to determine an output weight triplet; the output weight triplet includes a photovoltaic output weight, an energy storage output weight, and a grid output weight; The second determination module is used to determine the corresponding weight adjustment parameters and / or generate a cross-sub-area power supply relay path based on a preset abnormality level matrix when it is determined that the real-time fluctuation of the output weight triplet is greater than a preset threshold, and store the corresponding scheduling data in a preset database to update the load forecasting model and the weight adjustment parameters.

10. A solar-storage collaborative scheduling device for smart construction site equipment, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a photovoltaic storage coordinated scheduling method for smart construction site equipment as described in any one of claims 1-8 above.

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