Photovoltaic panel movement control optimization method and photovoltaic power generation container

Abstract

The invention provides a photovoltaic panel movement control optimization method and a photovoltaic power generation container, and belongs to the technical field of photovoltaic panels, and the method comprises the steps: obtaining the preliminary required electric quantity according to a user activity scene, capturing the future weather information of the user activity scene, constructing a weather matrix, analyzing the weather matrix, and obtaining the optimal electric quantity of the user activity scene; determining a plurality of variables influencing illumination and the influence weight of each variable; analyzing the required electric quantity according to the plurality of variables and the influence weight in combination with the electric quantity conversion efficiency of the single model of the photovoltaic panel, and obtaining the initial number of the required photovoltaic panels; according to an equivalent modeling mode and a minimum cluster number principle, constructing a photovoltaic mobile cluster based on a single block model; each photovoltaic panel in each photovoltaic mobile cluster is controlled to move to an optimal illumination receiving position to construct a power vector, and all the power vectors are analyzed to optimize the corresponding photovoltaic mobile cluster, so that the application efficiency of the photovoltaic panels is improved under the condition that the power demand is met as far as possible.

Description

Technical Field

[0001] The invention relates to a photovoltaic panel movement control optimization method and a photovoltaic power generation container, belonging to the technical field of photovoltaic panels. Background Art

[0002] Photovoltaic panel components are a type of power generation device that generates direct current when exposed to sunlight. They are composed of thin solid photovoltaic cells made almost entirely of semiconductor materials (such as silicon). Photovoltaic panels can be used in a variety of different places to provide people with a comfortable travel environment and reduce dependence on and consumption of traditional energy.

[0003] As photovoltaic panels are increasingly needed to be used in flexible and changeable scenarios such as the difficulty and high cost of electricity in daily life, photovoltaic panels cannot be set up in advance, or there are photovoltaic panels in related scenarios but the photovoltaic panels themselves have low power and cannot meet current electricity demand. All of these unreasonable situations lead to low efficiency of photovoltaic panels in these application scenarios.

[0004] Therefore, the present invention proposes a photovoltaic panel movement control optimization method and a photovoltaic power generation container. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a photovoltaic panel mobile control optimization method and a photovoltaic power generation container, which are used to set the number of photovoltaic panels by analyzing activity scenes and weather information, and further optimize the relevant settings of the photovoltaic panels by constructing a photovoltaic mobile cluster and a power vector, so as to improve the application efficiency of the photovoltaic panels while meeting the electricity demand as much as possible.

[0006] The present invention provides a photovoltaic panel movement control optimization method, comprising: Step 1: Obtain the preliminary power requirement according to the user activity scene, and at the same time, capture the future weather information of the user activity scene to build a weather matrix, and analyze the weather matrix to determine several variables affecting the lighting and the influence weight of each variable; Step 2: Analyze the required power according to the variables and the influence weights and in combination with the power conversion efficiency of the single-block model of the photovoltaic panel to obtain the preliminary number of required photovoltaic panels; Step 3: According to the equivalent modeling method and the principle of minimum number of clusters, a photovoltaic mobile cluster based on a single block model is constructed; Step 4: Control each photovoltaic panel in each photovoltaic mobile cluster to move to the position that best receives light to construct a power vector, and analyze all power vectors to optimize the corresponding photovoltaic mobile cluster.

[0007] Furthermore, the optimization is to configure new photovoltaic panels for the corresponding photovoltaic mobile cluster and / or replace the original photovoltaic panels.

[0008] Furthermore, the initial required power is obtained according to the user activity scenario, including: Analyze the user activity scenario to obtain a number of scenario targets and a category of electrical equipment based on each scenario target; Based on the category of electrical equipment and the number of equipment, estimated usage time, and rated power of each type of electrical equipment, a first power consumption corresponding to the scenario target is obtained; According to the type and rated power of the electrical equipment, the estimated power loss is obtained by matching from the type-rated-loss database; Based on all first power consumption and estimated power loss, the preliminary power requirement is obtained.

[0009] Furthermore, the weather matrix is ​​analyzed to determine several variables affecting the lighting and the influence weight of each variable, including: Obtaining a predefined indicator set of the future weather information; Normalizing the weather matrix to obtain a normalized matrix; Determine that each predefined indicator in the predefined indicator set draws an analysis curve based on the normalized matrix; Determine the fluctuation coefficient of the analysis curve, and at the same time, determine the first difference values ​​at adjacent moments in sequence starting from the second moment point until the Nth moment point; Determine a compensation coefficient based on the first difference value of each adjacent moment and the unit change amount at the corresponding adjacent moment, and assign it to the left time point immediately adjacent to the adjacent moment; According to the fluctuation coefficient, the normalized value of the original curve at each left time point and the assigned compensation coefficient, the future state of the corresponding predefined indicator is obtained; Matching the future state with a state impact database corresponding to a predefined indicator to obtain an impact coefficient; The predefined indicators with an impact coefficient greater than the preset coefficient are retained and regarded as the first indicators; Based on the influence coefficients of all the first indicators, the influence weight of each first indicator is calculated, wherein the indicator variable of each first indicator is regarded as a variable that affects the illumination.

[0010] Furthermore, the preliminary number of photovoltaic panels required is obtained, including: Obtain several photovoltaic panel decisions based on each weather condition from a historical database, and establish a power supply relationship between each photovoltaic panel decision and the weather condition; Trace the overlap of decisions based on different photovoltaic panels under each weather condition, and at the same time, trace the overlap of all electricity supply relationships under each weather condition; Determine the adjustment probability of the photovoltaic panel under each weather condition according to the decision overlap and the relationship overlap; Unlock and obtain the future weather type from the future weather information, and match the adjusted probabilities under all weather conditions to obtain the type probability of each future weather type; Select the photovoltaic panel adjustment strategies with the maximum probability and the second maximum probability from the type probabilities, and regard them as the first strategy and the second strategy respectively; The first strategy is analyzed to obtain a first increase amount of the photovoltaic panel to be adjusted and a first initial amount that is originally set; At the same time, the second strategy is analyzed to obtain the adjusted second increase amount of the photovoltaic panel and the second initial amount originally set; Based on the first increase amount, the first initial amount, the second increase amount, and the second initial amount, a recommended power conversion efficiency of a single photovoltaic panel is obtained; Based on each variable and the influence weight of each variable, determine the efficiency loss factor for the single block model; Adjusting the power conversion efficiency based on all efficiency loss factors to obtain a final conversion efficiency; Based on the final conversion efficiency, the theoretical light energy received by the single-block model under the future weather information, and combined with the required power, the preliminary number of required photovoltaic panels is obtained.

[0011] Furthermore, a photovoltaic mobile cluster based on a single block model is constructed, including: Decomposing the preliminary number according to the minimum cluster number principle to obtain a number of decomposed groups; The disassembled group is equivalently constructed into a single-block model according to the equivalent modeling method to obtain a photovoltaic mobile cluster.

[0012] Furthermore, all power vectors are analyzed to optimize the corresponding photovoltaic mobile cluster, including: When the photovoltaic panel moves to the best position to receive light, the power generated by each photovoltaic panel per unit time is actually captured, and a power vector based on the corresponding photovoltaic mobile cluster is constructed; Analyze each power vector to obtain the initial energy supply of the corresponding photovoltaic mobile cluster, and at the same time, lock the first number of low-power photovoltaic panels in the corresponding power vector, and combine the second number of all photovoltaic panels in the corresponding photovoltaic mobile cluster to obtain a quantity ratio; According to the quantity ratio and the initial function status, the current photovoltaic state is set for the corresponding photovoltaic mobile cluster, wherein the current photovoltaic state includes: a qualified state and an unqualified state; Determine the sufficient power of the photovoltaic mobile cluster in a qualified state, and at the same time, determine the insufficient power of the photovoltaic mobile cluster in an unqualified state; If the sufficient power is greater than or equal to the insufficient power, the current photovoltaic mobile cluster remains unchanged; If the sufficient power is less than the insufficient power, then all low-power photovoltaic panels are sorted by size, and photovoltaic panels that meet the replacement conditions are selected for replacement.

[0013] The present invention provides a photovoltaic power generation container, which is used to execute any of the photovoltaic panel movement control optimization methods.

[0014] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of any one of the photovoltaic panel movement control optimization methods.

[0015] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the photovoltaic panel movement control optimization methods.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The number of photovoltaic panels is set by analyzing activity scenarios and weather information, and the relevant settings of the photovoltaic panels are further optimized by building photovoltaic mobile clusters and power vectors, thereby improving the application efficiency of photovoltaic panels while meeting electricity demand as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a photovoltaic panel movement control optimization method of the present invention; Figure 2 This is a structural diagram of the photovoltaic power generation container of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of this application.

[0019] It should be noted that when an element is referred to as being "fixed on" or "set on" another component, it can be directly on the other component or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to the other component or indirectly connected to the other component.

[0020] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "multiple" and "several" mean two or more, unless otherwise clearly and specifically defined.

[0022] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0023] Embodiment 1: The present invention provides a photovoltaic panel movement control optimization method, such as Figure 1 As shown, including: Step 1: Obtain the preliminary power requirement according to the user activity scene, and at the same time, capture the future weather information of the user activity scene to build a weather matrix, and analyze the weather matrix to determine several variables affecting the lighting and the influence weight of each variable; Step 2: Analyze the required power according to the variables and the influence weights and in combination with the power conversion efficiency of the single-block model of the photovoltaic panel to obtain the preliminary number of required photovoltaic panels; Step 3: According to the equivalent modeling method and the principle of minimum number of clusters, a photovoltaic mobile cluster based on a single block model is constructed; Step 4: Control each photovoltaic panel in each photovoltaic mobile cluster to move to the position that best receives light to construct a power vector, and analyze all power vectors to optimize the corresponding photovoltaic mobile cluster.

[0024] Furthermore, the optimization is to configure new photovoltaic panels for the corresponding photovoltaic mobile cluster and / or replace the original photovoltaic panels.

[0025] In this embodiment, the user activity scenario refers to a scenario in which electricity is needed and photovoltaic panels need to be deployed.

[0026] In this embodiment, the initial required power refers to the estimated power that can be used in this scenario.

[0027] In this embodiment, future weather information is directly acquired based on the weather forecast, and the next few days refer to the activity time of the user in the scene.

[0028] In this embodiment, the weather matrix contains weather information for several days, with several rows for each day, and the parameters in each row vector include: ambient temperature, humidity, illuminance, visibility, perceived temperature, pollution concentration, wind direction and other related values.

[0029] In this embodiment, the variables affecting the illumination are associated with the parameters in each row vector.

[0030] In this embodiment, the sum of the impact weights of all variables is 1.

[0031] In this embodiment, the purpose of the analysis is to determine how many photovoltaic panels need to be deployed based on the required amount of electricity, so that there will be enough electricity to supply later.

[0032] In this embodiment, the position for receiving the best light refers to the position where the sunlight shines as directly as possible on the photovoltaic panel.

[0033] In this embodiment, the power vector refers to the effectively converted output power after the photovoltaic panel actually receives the light.

[0034] In this embodiment, the power vector={the output power of each photovoltaic panel in the corresponding photovoltaic mobile cluster}.

[0035] The beneficial effects of the above technical solution are: setting the number of photovoltaic panels by analyzing activity scenes and weather information, and further optimizing the relevant settings of photovoltaic panels by building photovoltaic mobile clusters and power vectors, thereby improving the application efficiency of photovoltaic panels while meeting electricity demand as much as possible.

[0036] Embodiment 2: Based on Example 1, obtaining the initial power required according to the user activity scenario includes: Analyze the user activity scenario to obtain a number of scenario targets and a category of electrical equipment based on each scenario target; Based on the category of electrical equipment and the number of equipment, estimated usage time, and rated power of each type of electrical equipment, a first power consumption corresponding to the scenario target is obtained; According to the type and rated power of the electrical equipment, the estimated power loss is obtained by matching from the type-rated-loss database; Based on all first power consumption and estimated power loss, the preliminary power requirement is obtained.

[0037] In this embodiment, the user activity scene analysis is to decompose the scene into several points. For example, scene 1 can be decomposed into: sub-scene 01, sub-scene 02, and sub-scene 03. Specifically, for example, the user activity scene is a scene in which the crew is shooting outdoors where electricity is required, and in this scene, power needs to be provided to the shooting equipment, power needs to be provided to the actors' mobile phones, and lighting needs to be provided for the shooting site, etc. At this time, the decomposed sub-scene 01 corresponds to the shooting equipment, sub-scene 02 corresponds to the mobile phone device, and sub-scene 03 corresponds to the lighting equipment. It should be noted that each sub-scene is regarded as a scene target, and all equipment that requires electricity under the sub-scene can be understood as the category of power equipment.

[0038] In this embodiment, the estimated usage time is pre-set, such as an estimated usage of 10 hours, and the rated power is set before each device leaves the factory.

[0039] In this embodiment, the first power consumption=the sum of the rated power per unit time of each type of electrical equipment in the electrical equipment category×the estimated usage time×the number of electrical equipment of the corresponding type.

[0040] In this embodiment, the type-rated-loss database includes the types, rated powers, and estimated power losses of different electrical equipment, and the database is pre-planned, mainly to facilitate subsequent direct matching.

[0041] In this embodiment, the initial required power consumption=the sum of all first power consumptions+the corresponding estimated power loss.

[0042] The beneficial effect of the above technical solution is: by analyzing the scenarios, the scope of electrical equipment under different scenario targets is obtained, and then combined with the first power consumption and the estimated power loss, the preliminary required power is effectively obtained, providing a basis for the subsequent determination of the preliminary quantity.

[0043] Embodiment 3: Based on Example 1, the weather matrix is ​​analyzed to determine several variables affecting the sunlight and the influence weight of each variable, including: Obtaining a predefined indicator set of the future weather information; Normalizing the weather matrix to obtain a normalized matrix; Determine that each predefined indicator in the predefined indicator set draws an analysis curve based on the normalized matrix; Determine the fluctuation coefficient of the analysis curve, and at the same time, determine the first difference values ​​at adjacent moments in sequence starting from the second moment point until the Nth moment point; Determine a compensation coefficient based on the first difference value of each adjacent moment and the unit change amount at the corresponding adjacent moment, and assign it to the left time point immediately adjacent to the adjacent moment; According to the fluctuation coefficient, the normalized value of the original curve at each left time point and the assigned compensation coefficient, the future state of the corresponding predefined indicator is obtained; Matching the future state with a state impact database corresponding to a predefined indicator to obtain an impact coefficient; The predefined indicators with an impact coefficient greater than the preset coefficient are retained and regarded as the first indicators; Based on the influence coefficients of all the first indicators, the influence weight of each first indicator is calculated, wherein the indicator variable of each first indicator is regarded as a variable that affects the illumination.

[0044] In this embodiment, the predefined indicator set is related to ambient temperature, humidity, illuminance, visibility, perceived temperature, pollution concentration, wind direction, etc. There is a measurement value under each indicator, which facilitates the construction of a weather matrix.

[0045] In this embodiment, the purpose of the normalization process is to normalize the value of each element in the matrix to between 0 and 1 to facilitate subsequent calculations.

[0046] In this embodiment, each column in the weather matrix represents the measured value of a predefined indicator under different future weather conditions, and thus an analysis curve can be drawn, wherein the horizontal axis of the curve is time, and the vertical axis is the measured value under the corresponding predefined indicator.

[0047] In this embodiment, the fluctuation coefficient = ,in, Represents the normalized value at the i1th time point in the analysis curve; Indicates all the analysis curves The average value of , Respectively represent all the The maximum and minimum values ​​in .

[0048] In this embodiment, the first difference is, for example, a difference between normalized values ​​at the second time point and the third time point.

[0049] In this embodiment, the unit change is pre-set corresponding to the predefined indicator, and is obtained based on the indicator-change comparison table, and the table includes different predefined indicators and unit changes based on the indicators, so the compensation coefficient = first difference / unit change. Based on the third time point, the left time point is the second time point.

[0050] In this embodiment, the future variation coefficient of each left time point is determined according to the fluctuation coefficient and the original normalized value of each left time point and the assigned compensation coefficient; Future coefficient of change = in, It is the normalized value of the original curve at the left time point; is a symbolic function, when Greater than or equal to hour, The value is 1, otherwise, The value is -1.

[0051] The average value and variance of the future coefficient of variation at each left time point are calculated to obtain the corresponding first value and second value; The future state that matches the future coefficient of change is obtained from the two-value-state comparison table, that is, the possible situation of the predefined indicator in the future weather. For example, for the temperature indicator, the temperature may increase, decrease, or remain stable in the future, and the comparison table is pre-planned and includes different combinations of first values ​​and second values ​​and the future states that match them.

[0052] In this embodiment, the state impact database contains the impact coefficients of predefined indicators in different future states, which can be directly matched, and the value range of the impact coefficient is 0 to 1.

[0053] In this embodiment, the value of the preset coefficient is 0.5.

[0054] In this embodiment, the influence weight=the influence coefficient of the corresponding first indicator / the sum of the influence coefficients of all first indicators.

[0055] The beneficial effect of the above technical solution is: by obtaining weather-related indicators and normalizing the matrix, it is convenient to determine the fluctuation coefficient of the analysis curve under different indicators, and combined with the compensation coefficient obtained by the first difference, it is convenient to effectively determine the future state of different indicators and then match it with the database to obtain the influence coefficient, which provides a basis for indicator screening and weight setting, and facilitates the subsequent reasonable setting of photovoltaic panels.

[0056] Embodiment 4: Based on Example 1, the preliminary number of photovoltaic panels required is obtained, including: Obtain several photovoltaic panel decisions based on each weather condition from a historical database, and establish a power supply relationship between each photovoltaic panel decision and the weather condition; Trace the overlap of decisions based on different photovoltaic panels under each weather condition, and at the same time, trace the overlap of all electricity supply relationships under each weather condition; Determine the adjustment probability of the photovoltaic panel under each weather condition according to the decision overlap and the relationship overlap; Unlock and obtain the future weather type from the future weather information, and match the adjusted probabilities under all weather conditions to obtain the type probability of each future weather type; Select the photovoltaic panel adjustment strategies with the maximum probability and the second maximum probability from the type probabilities, and regard them as the first strategy and the second strategy respectively; The first strategy is analyzed to obtain a first increase amount of the photovoltaic panel to be adjusted and a first initial amount that is originally set; At the same time, the second strategy is analyzed to obtain the adjusted second increase amount of the photovoltaic panel and the second initial amount originally set; Based on the first increase amount, the first initial amount, the second increase amount, and the second initial amount, a recommended power conversion efficiency of a single photovoltaic panel is obtained; Based on each variable and the influence weight of each variable, determine the efficiency loss factor for the single block model; Adjusting the power conversion efficiency based on all efficiency loss factors to obtain a final conversion efficiency; Based on the final conversion efficiency, the theoretical light energy received by the single-block model under the future weather information, and combined with the required power, the preliminary number of required photovoltaic panels is obtained.

[0057] In this embodiment, the historical database contains several decisions under different weather conditions. For example, there are 3 decisions under weather condition 1, and the power supply results of each decision under the corresponding weather conditions are obtained in turn. For example, the power supply relationship between decision a1 and weather condition 1 is that n0 more photovoltaic panels are needed to provide more than d0 electricity, etc.

[0058] In this embodiment, decision overlap refers to the overlapping ideas of decision a1, decision a2, decision a2, decision a3, decision a1, decision a3 under the weather conditions are regarded as decision overlap.

[0059] In this embodiment, the electricity supply relationship means, for example, adding n0 more photovoltaic panels provides more than d0 electricity, adding n1 more photovoltaic panels provides more than d1 electricity, and the overlapping relationship means: adding min(n0,n1) more photovoltaic panels provides more than min(d0,d1) electricity.

[0060] In this embodiment, the adjustment probability=the proportion of information with overlapping decisions×the proportion of information with overlapping relationships, square root of the two.

[0061] In this embodiment, the future weather types include, for example, heavy rain type, heavy snow type, and the like.

[0062] In this embodiment, the adjustment probability is obtained based on each weather condition, so after determining the future weather type, the relevant probability can be directly matched, and the matched relevant probability is the type probability.

[0063] In this embodiment, because the strategy includes setting information for the increase of photovoltaic panels and the number of photovoltaic panels that originally existed before the setting, it will directly go to the first increase amount, the second increase amount, the first initial amount and the second initial amount.

[0064] In this embodiment, the power conversion efficiency = ((total converted power of all photovoltaic panels after the first increase - total converted power of all photovoltaic panels before the first increase) / first increase) / total absorbed light energy of a random photovoltaic panel under the first increase.

[0065] In this embodiment, the efficiency loss factor=the influence weight of the corresponding variable×the theoretical influence coefficient of the corresponding variable on electric energy.

[0066] In this embodiment, the final conversion efficiency = (1-the sum of all efficiency loss factors) × electric energy conversion efficiency.

[0067] In this embodiment, the preliminary quantity=ceiling(required power / (theoretical light energy received quantity×final conversion efficiency)), wherein ceiling is a round-up symbol.

[0068] The beneficial effects of the above technical solution are: by obtaining photovoltaic panel decisions from the historical database to determine the adjustment probability from the two aspects of decision overlap and relationship overlap, to provide a judgment basis for the reliable strategy adopted for subsequent photovoltaic panel adjustments, and by analyzing the two strategies to determine the power conversion efficiency, and combining the efficiency loss factor, effectively calculate the preliminary quantity, providing a basis for the addition of photovoltaic panels.

[0069] Embodiment 5: Based on Example 1, a photovoltaic mobile cluster based on a single block model is constructed, including: Decomposing the preliminary number according to the minimum cluster number principle to obtain a number of decomposed groups; The disassembled group is equivalently constructed into a single-block model according to the equivalent modeling method to obtain a photovoltaic mobile cluster.

[0070] In this embodiment, the minimum centralized quantity principle is pre-set, for example, 10 blocks. At this time, the initial quantity / 10 can be rounded up, and the result obtained is the number of disassembly groups.

[0071] In this embodiment, equivalent construction is to combine the single block model and the number of models together, and the result obtained is the photovoltaic mobile cluster.

[0072] The beneficial effect of the above technical solution is: disassembly and equivalent construction according to the principle of minimum number of groups can facilitate the acquisition of photovoltaic mobile clusters, providing a basis for the subsequent arrangement of several photovoltaic power generation containers.

[0073] Embodiment 6: Based on Example 1, all power vectors are analyzed to optimize the corresponding photovoltaic mobile cluster, including: When the photovoltaic panel moves to the best position to receive light, the power generated by each photovoltaic panel per unit time is actually captured, and a power vector based on the corresponding photovoltaic mobile cluster is constructed; Analyze each power vector to obtain the initial energy supply of the corresponding photovoltaic mobile cluster, and at the same time, lock the first number of low-power photovoltaic panels in the corresponding power vector, and combine the second number of all photovoltaic panels in the corresponding photovoltaic mobile cluster to obtain a quantity ratio; According to the quantity ratio and the initial function status, the current photovoltaic state is set for the corresponding photovoltaic mobile cluster, wherein the current photovoltaic state includes: a qualified state and an unqualified state; Determine the sufficient power of the photovoltaic mobile cluster in a qualified state, and at the same time, determine the insufficient power of the photovoltaic mobile cluster in an unqualified state; If the sufficient power is greater than or equal to the insufficient power, the current photovoltaic mobile cluster remains unchanged; If the sufficient power is less than the insufficient power, then all low-power photovoltaic panels are sorted by size, and photovoltaic panels that meet the replacement conditions are selected for replacement.

[0074] In this embodiment, the power vector = {electrical energy generated by each photovoltaic panel}.

[0075] In this embodiment, the initial functional condition = the sum of the electric energy production powers of all photovoltaic panels in the functional vector / (the maximum electric energy power of a single photovoltaic panel under ideal weather conditions×the number of photovoltaic panels).

[0076] In this embodiment, low power refers to a power that generates electric energy less than half of the maximum electric energy of a single photovoltaic panel under ideal weather conditions.

[0077] In this embodiment, the first quantity refers to the quantity of low-power photovoltaic panels, and the quantity ratio=first quantity / second quantity.

[0078] In this embodiment, the value corresponding to the current photovoltaic state = quantity ratio × initial functional condition, and the square root of the two is greater than 0.5, which is considered qualified, otherwise, it is considered unqualified.

[0079] In this embodiment, the total power corresponding to the preliminary required power / the number of all photovoltaic panels = the average power of a single panel, and the sufficient power = the total power of the photovoltaic mobile cluster in a qualified state - the average power of a single panel × the number of photovoltaic panels in the cluster.

[0080] Insufficient power = average power of a single panel × number of photovoltaic panels in the cluster - total power of the photovoltaic mobile cluster in the unqualified state.

[0081] In this embodiment, the replacement condition refers to that the current low power of the corresponding photovoltaic panel is less than the power threshold.

[0082] The beneficial effect of the above technical solution is: by determining the initial functional status of the mobile centralization and combining the ratio of the number of low-power photovoltaic panels to the number of all photovoltaic panels, the photovoltaic status is set for the cluster, thereby achieving continued optimization of the cluster and ensuring the adequacy of the power supply to meet the electricity demand as much as possible.

[0083] Embodiment 7: The present invention provides a photovoltaic power generation container, which is used to execute any of the photovoltaic panel movement control optimization methods.

[0084] Embodiment 8: The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of any one of the photovoltaic panel movement control optimization methods.

[0085] Embodiment 9: The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the photovoltaic panel movement control optimization methods.

[0086] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A photovoltaic panel movement control optimization method, characterized in that: include: Step 1: Obtain the preliminary power requirement according to the user activity scene, and at the same time, capture the future weather information of the user activity scene to build a weather matrix, and analyze the weather matrix to determine several variables affecting the lighting and the influence weight of each variable; Step 2: Analyze the required power according to the variables and the influence weights and in combination with the power conversion efficiency of the single-block model of the photovoltaic panel to obtain the preliminary number of required photovoltaic panels; Step 3: According to the equivalent modeling method and the principle of minimum number of clusters, a photovoltaic mobile cluster based on a single block model is constructed; Step 4: Control each photovoltaic panel in each photovoltaic mobile cluster to move to the position that best receives light to construct a power vector, and analyze all power vectors to optimize the corresponding photovoltaic mobile cluster.

2. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: The optimization is to configure new photovoltaic panels and / or replace original photovoltaic panels to the corresponding photovoltaic mobile cluster.

3. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: Get the initial power required based on the user's activity scenario, including: Analyze the user activity scenario to obtain a number of scenario targets and a category of electrical equipment based on each scenario target; Based on the category of electrical equipment and the number of equipment, estimated usage time, and rated power of each type of electrical equipment, a first power consumption corresponding to the scenario target is obtained; According to the type and rated power of the electrical equipment, the estimated power loss is obtained by matching from the type-rated-loss database; Based on all first power consumption and estimated power loss, the preliminary power requirement is obtained.

4. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: The weather matrix is ​​analyzed to determine several variables that affect the lighting and the influence weight of each variable, including: Obtaining a predefined indicator set of the future weather information; Normalizing the weather matrix to obtain a normalized matrix; Determine that each predefined indicator in the predefined indicator set draws an analysis curve based on the normalized matrix; Determine the fluctuation coefficient of the analysis curve, and at the same time, determine the first difference values ​​at adjacent moments in sequence starting from the second moment point until the Nth moment point; Determine a compensation coefficient based on the first difference value of each adjacent moment and the unit change amount at the corresponding adjacent moment, and assign it to the left time point immediately adjacent to the adjacent moment; According to the fluctuation coefficient, the normalized value of the original curve at each left time point and the assigned compensation coefficient, the future state of the corresponding predefined indicator is obtained; Matching the future state with a state impact database corresponding to a predefined indicator to obtain an impact coefficient; The predefined indicators with an impact coefficient greater than the preset coefficient are retained and regarded as the first indicators; Based on the influence coefficients of all the first indicators, the influence weight of each first indicator is calculated, wherein the indicator variable of each first indicator is regarded as a variable that affects the illumination.

5. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: Obtain a preliminary number of PV panels required, including: Obtain several photovoltaic panel decisions based on each weather condition from a historical database, and establish a power supply relationship between each photovoltaic panel decision and the weather condition; Trace the overlap of decisions based on different photovoltaic panels under each weather condition, and at the same time, trace the overlap of all electricity supply relationships under each weather condition; Determine the adjustment probability of the photovoltaic panel under each weather condition according to the decision overlap and the relationship overlap; Unlock and obtain the future weather type from the future weather information, and match the adjusted probabilities under all weather conditions to obtain the type probability of each future weather type; Select the photovoltaic panel adjustment strategies with the maximum probability and the second maximum probability from the type probabilities, and regard them as the first strategy and the second strategy respectively; The first strategy is analyzed to obtain a first increase amount of the photovoltaic panel to be adjusted and a first initial amount that is originally set; At the same time, the second strategy is analyzed to obtain the adjusted second increase amount of the photovoltaic panel and the second initial amount originally set; Based on the first increase amount, the first initial amount, the second increase amount, and the second initial amount, a recommended power conversion efficiency of a single photovoltaic panel is obtained; Based on each variable and the influence weight of each variable, determine the efficiency loss factor for the single block model; Adjusting the power conversion efficiency based on all efficiency loss factors to obtain a final conversion efficiency; Based on the final conversion efficiency, the theoretical light energy received by the single-block model under the future weather information, and combined with the required power, the preliminary number of required photovoltaic panels is obtained.

6. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: Construct a photovoltaic mobile cluster based on a single block model, including: Decomposing the preliminary number according to the minimum cluster number principle to obtain a number of decomposed groups; The disassembled group is equivalently constructed into a single-block model according to the equivalent modeling method to obtain a photovoltaic mobile cluster.

7. The photovoltaic panel movement control optimization method according to claim 1, characterized in that: All power vectors are analyzed to optimize the corresponding photovoltaic mobile cluster, including: When the photovoltaic panel moves to the best position to receive light, the power generated by each photovoltaic panel per unit time is actually captured, and a power vector based on the corresponding photovoltaic mobile cluster is constructed; Analyze each power vector to obtain the initial energy supply of the corresponding photovoltaic mobile cluster, and at the same time, lock the first number of low-power photovoltaic panels in the corresponding power vector, and combine the second number of all photovoltaic panels in the corresponding photovoltaic mobile cluster to obtain a quantity ratio; According to the quantity ratio and the initial function status, the current photovoltaic state is set for the corresponding photovoltaic mobile cluster, wherein the current photovoltaic state includes: a qualified state and an unqualified state; Determine the sufficient power of the photovoltaic mobile cluster in a qualified state, and at the same time, determine the insufficient power of the photovoltaic mobile cluster in an unqualified state; If the sufficient power is greater than or equal to the insufficient power, the current photovoltaic mobile cluster remains unchanged; If the sufficient power is less than the insufficient power, then all low-power photovoltaic panels are sorted by size, and photovoltaic panels that meet the replacement conditions are selected for replacement.

8. A photovoltaic power generation container, characterized in that: Used to execute the photovoltaic panel movement control optimization method described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the photovoltaic panel movement control optimization method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the photovoltaic panel movement control optimization method according to any one of claims 1 to 7.

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