Composite Micro Energy System and Its Energy Control Method, Device, and Storage Medium

Abstract

An energy control method and device, a storage medium, and a composite micro energy system for a composite micro energy system. The composite micro energy system includes a micro energy collection module, an energy storage module, and a power supply module. The method includes: identifying characteristic information of each type of energy collected by the micro energy collection module; inputting the characteristic information of each type of energy and the required electric power of the load into a decision tree model to obtain a decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy; and controlling the direction of each type of energy according to the decision label to transmit at least a part of each type of energy to the power supply module and / or the energy storage module. By adopting the solution of the present invention, the direction of various types of energy in the composite micro energy system can be accurately determined, and the energy utilization rate can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy, and in particular, to a composite micro energy system, an energy control method, device, and storage medium thereof. Background Art

[0002] A composite micro energy system is a system that can collect the energy generated by multiple micro energy sources and store and / or release various energies (for example, supply power to an external load). In the prior art, for the energy collected by the composite micro energy system, a set of logic threshold control strategies are usually artificially formulated based on theoretical analysis and engineering experience to determine the direction of the energy.

[0003] It can be understood that the external environment is variable, and the energy situation collected by the composite micro energy system is usually greatly affected by the external environment. For example, seasonal changes and day-night alternation may cause the solar energy collected by the system to be constantly changing. In addition, the demands of users using the composite micro energy are also variable. For example, the energy required by an external load in different situations usually varies greatly. Since the logic threshold control strategy in the prior art is artificially formulated and the cost of changing this logic threshold control strategy is relatively high, this artificially formulated logic threshold control strategy is rarely changed once it is determined. Therefore, the method in the prior art cannot make the direction of the energy in the composite micro energy system adapt to different situations such as a variable external environment and actual demands.

[0004] Therefore, there is an urgent need for an energy control method for a composite micro energy system that can more accurately determine the direction of various energies in the composite micro energy system in different situations and improve energy utilization efficiency. Summary of the Invention

[0005] The technical problem solved by the present invention is to provide an energy control method for a composite micro energy system that can more accurately determine the direction of various energies in the composite micro energy system in different situations and improve energy utilization efficiency.

[0006] To solve the above technical problems, an embodiment of the present invention provides an energy control method for a composite micro energy system. The composite micro energy system includes a micro energy collection module, an energy storage module, and a power supply module. The micro energy collection module is used to collect at least one type of energy. The energy storage module is used to store the electric energy converted from the energy. The power supply module is used to supply power to a load. The method includes: identifying the characteristic information of each type of energy collected by the micro energy collection module; inputting the characteristic information of each type of energy and the required electric power of the load into a decision tree model to obtain a decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy; controlling the direction of each type of energy according to the decision label so as to transmit at least a part of each type of energy to the power supply module and / or the energy storage module; wherein, the decision tree model is trained and generated by using a plurality of first energy samples as training data.

[0007] Optionally, the characteristic information includes one or more of the following: the type of energy and the electric power that the energy can be converted into.

[0008] Optionally, the method for obtaining the decision label for each type of energy further includes: inputting the state of charge of the energy storage module, the characteristic information of each type of energy, and the required electric power of the load into the decision tree model together to obtain the decision label for each type of energy.

[0009] Optionally, the method for generating the decision tree model includes: obtaining a plurality of first energy samples, each first energy sample including the required electric power of the load, at least one sample energy and its characteristic information, and a decision label; using the plurality of first energy samples as the training data to train and generate the decision tree model.

[0010] Optionally, before using the plurality of first energy samples as the training data to train and generate the decision tree model, the method further includes: determining the range of values that the required electric power of the load can take and the range of values that the characteristic information of each sample energy can take; screening the plurality of first energy samples according to the characteristic information of each sample energy in each first energy sample and the required electric power of the load corresponding to the first energy sample to obtain the first energy samples for generating the decision tree model.

[0011] Optionally, before inputting the characteristic information of each type of energy and the required electric power of the load into the decision tree model, the method further includes: obtaining a plurality of second energy samples, each second energy sample including the required electric power of the load, at least one type of sample energy and its characteristic information, and a preset decision label, wherein the plurality of second energy samples are independent of the plurality of first energy samples; inputting the characteristic information of each type of sample energy in each second energy sample and the required electric power of the load corresponding to this second energy sample into the decision tree model to obtain the model decision label of each type of sample energy in this second energy sample; comparing the model decision label of each type of sample energy in each second energy sample with the preset decision label, and calculating the proportion of the number of second energy samples in which the model decision label of each type of sample energy is consistent with the preset decision label in the total number of all second energy samples. If the proportion is less than a first preset threshold, pruning processing is performed on the decision tree model to update the decision tree model.

[0012] Optionally, the energy storage module includes a plurality of energy storage elements, and the energy storage module is further configured to supply power to the load. The method further includes: obtaining the state of charge of the plurality of energy storage elements; according to the electric power required to be provided by the energy storage module and the state of charge of the plurality of energy storage elements, using a fuzzy control algorithm to obtain the power supply ratio of the plurality of energy storage elements, wherein the electric power required to be provided by the energy storage module is the difference between the required electric power of the load and the electric power that can be converted by the micro energy harvesting module; determining the output electric power of each energy storage element according to the power supply ratio and the required electric power of the load.

[0013] Optionally, the plurality of energy storage elements include supercapacitors.

[0014] Optionally, using a fuzzy control algorithm to obtain the power supply ratio of the plurality of energy storage elements according to the electric power required to be provided by the energy storage module and the state of charge of the plurality of energy storage elements includes: determining the power range to which the electric power required to be provided by the energy storage module belongs according to the electric power required to be provided by the energy storage module, and determining the state of charge range to which the state of charge of each energy storage element belongs according to the state of charge of each energy storage element; querying the fuzzy control rule table according to the power range and the state of charge range of each energy storage element to determine the power supply ratio range of each energy storage element; defuzzifying the power supply ratio ranges of the respective energy storage elements to obtain the power supply ratio of each energy storage element; wherein the fuzzy control rule table is used to describe the mapping relationship between the power range and the state of charge range and the power supply ratio range.

[0015] Optionally, the micro energy harvesting module includes a plurality of micro energy harvesters, and the method further includes: obtaining first feedback information for indicating whether a micro energy harvester is replaced; judging whether a micro energy harvester is replaced according to the first feedback information, and if so, obtaining the energy-convertible electric power of each replaced micro energy harvester; comparing the energy-convertible electric power of each replaced micro energy harvester with a second preset threshold; if the energy-convertible electric power of any one of the replaced micro energy harvesters does not exceed the second preset threshold, retraining to generate the decision tree model.

[0016] Optionally, the energy storage module includes a plurality of energy storage elements, and the method further includes: obtaining second feedback information for indicating whether an energy storage element is replaced; judging whether an energy storage element is replaced according to the second feedback information, and if so, obtaining the state of charge of each replaced energy storage element; comparing the upper limit of the state of charge of each replaced energy storage element with a third preset threshold; if the upper limit of the state of charge of any one of the replaced energy storage elements does not exceed the third preset threshold, retraining to generate the decision tree model.

[0017] To solve the above technical problems, an embodiment of the present invention further provides an energy control device for a composite micro energy system. The composite micro energy system includes a micro energy harvesting module, an energy storage module, and a power supply module. The micro energy harvesting module is used to harvest at least one type of energy, the energy storage module is used to store the electric energy after the energy conversion, and the power supply module is used to supply power to a load. The device includes: an identification module for identifying the characteristic information of each type of energy harvested by the micro energy harvesting module; a classification module for inputting the characteristic information of each type of energy and the electric power required by the load into a decision tree model to obtain a decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy; a transmission control module for controlling the direction of each type of energy according to the decision label so as to transmit at least a part of each type of energy to the power supply module and / or the energy storage module; wherein, the decision tree model is generated by training using a plurality of first energy samples as training data.

[0018] An embodiment of the present invention further provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the above energy control method for the composite micro energy system are executed.

[0019] An embodiment of the present invention further provides a composite micro energy system, which includes: a micro energy harvesting module for harvesting at least one type of energy; an energy storage module for storing the electric energy after the energy conversion; a power supply module for supplying power to a load; and a controller for executing the steps of the above energy control method for the composite micro energy system.

[0020] Compared with the prior art, the technical solution of the embodiment of the present invention has the following beneficial effects:

[0021] In the solution of the embodiment of the present invention, since the decision tree model is trained and generated using a plurality of first energy samples, the trained and generated decision tree model can learn the relationship and law between the characteristic information of the energy, the power required by the load, and the decision label of the energy in the first energy samples. By inputting the characteristic information of each identified energy and the required electric power of the load into the trained and generated decision tree model, the decision label of each energy can be determined according to the current characteristic information of each energy and the required electric power of the load, and then the flow direction of each energy can be controlled according to the decision label. Thus, the flow direction of the energy can be controlled according to the characteristic information of each energy and the required electric power of the load, which can make the flow direction of the energy adapt to the characteristic information of the energy currently collected by the micro energy harvesting module and the required electric power of the load, so as to more accurately determine the flow direction of the energy and improve the utilization rate of the energy.

[0022] Furthermore, in the solution of the embodiment of the present invention, before training and generating the decision tree model, a plurality of first energy samples are screened according to the range of possible values of the determined characteristic information and the range of possible values of the required electric power of the load to obtain the first energy samples for generating the decision tree model, which can ensure the accuracy of the first energy samples used as training data, so that the trained and generated decision tree model is more accurate.

[0023] Furthermore, in the solution of the embodiment of the present invention, before the user uses the decision tree model to control the flow direction of the energy, the trained and generated decision tree model is tested using second energy samples independent of the first energy samples. When the proportion of the number of second energy samples whose model decision labels of each sample energy obtained by the test are consistent with the preset decision labels in the total number of all second energy samples is less than the first preset threshold, pruning processing is performed on the decision tree model to calibrate the decision tree model, so that the decision tree model can adapt to the current actual requirements, and thus more accurately determine the flow direction of the energy.

[0024] Furthermore, in the solution of the embodiment of the present invention, based on the fuzzy control algorithm, the power supply ratio of each energy storage element is determined according to the electric power required to be provided by the energy storage module and the state of charge of each energy storage element, which can avoid repeatedly using individual energy storage elements to supply power to the power supply module, so as to avoid problems such as premature aging and loss of the individual energy storage element caused by repeated charging and discharging. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic structural diagram of a composite micro energy system in an embodiment of the present invention.

[0026] Figure 2It is a schematic flowchart of an energy control method for the first composite micro energy system in an embodiment of the present invention.

[0027] Figure 3 It is a schematic structural diagram of a decision tree model in an embodiment of the present invention.

[0028] Figure 4 It is a schematic flowchart of an energy control method for the second composite micro energy system in an embodiment of the present invention.

[0029] Figure 5 It is a schematic flowchart of an energy control method for the third composite micro energy system in an embodiment of the present invention.

[0030] Figure 6 It is a schematic structural diagram of an energy control device for a composite micro energy system in an embodiment of the present invention. Detailed implementation manners

[0031] As described in the background art, there is an urgent need for an energy control method for a composite micro energy system, which can accurately determine the flow direction of various energies in the composite micro energy system under different conditions and improve the energy utilization rate.

[0032] As mentioned above, the inventors of the present invention have found through research that in the prior art, for the energy collected by the composite micro energy system, a set of logic threshold control strategies are usually artificially formulated based on theoretical analysis and engineering experience to determine the energy flow direction. It can be understood that the external environment is variable and the energy situation collected by the composite micro energy system is usually greatly affected by the external environment. For example, seasonal changes and day-night alternation may cause the solar energy collected by the system to be constantly changing. In addition, the demands of users using the composite micro energy are also variable. For example, the energy required by external loads under different conditions usually has large differences. Since the logic threshold control strategy in the prior art is artificially formulated and the cost of changing this logic threshold control strategy is relatively high, this artificially formulated logic threshold control strategy is rarely changed once it is determined. Therefore, the method in the prior art cannot make the energy flow direction in the composite micro energy system adapt to different situations such as the variable external environment and actual demands.

[0033] To solve the above technical problems, an embodiment of the present invention provides a control method for a composite micro-energy system. In the solution of the embodiment of the present invention, since the decision tree model is trained and generated using a plurality of first energy samples, the trained decision tree model can learn the relationship and law between the characteristic information of the energy, the power required by the load, and the decision label of the energy in the first energy samples. Inputting the characteristic information of each identified energy and the required electric power of the load into the trained decision tree model, the decision label of each energy can be determined according to the current characteristic information of each energy and the required electric power of the load, and then the flow direction of each energy can be controlled according to the decision label. Thus, the flow direction of the energy can be controlled according to the characteristic information of each energy and the required electric power of the load. By such a method, the flow direction of the energy can be adapted to the characteristic information of the energy currently collected by the micro-energy collection module and the required electric power of the load, so that the flow direction of the energy can be accurately determined and the energy utilization rate can be improved.

[0034] To make the above objects, features, and beneficial effects of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings.

[0035] Reference Figure 1 , Figure 1 is a schematic structural diagram of a composite micro-energy system in an embodiment of the present invention. The following combines Figure 1 to give a non-limiting description of the composite micro-energy system applicable to the embodiment of the present invention.

[0036] Figure 1 The shown composite micro-energy system may include: a micro-energy collection module 11, an energy storage module 12, a power supply module 13, and a controller 14.

[0037] The micro-energy collection module 11 is used to collect at least one kind of energy, and the at least one kind of energy can be any kind of energy. The energy can be electric energy, or any energy that can be converted into electric energy, such as solar energy, electromagnetic energy, and vibration energy, etc., but is not limited thereto.

[0038] The micro-energy collection module 11 may include a plurality of micro-energy collectors, and the micro-energy collectors can be used to collect various forms of energy. In addition, the collected various forms of energy can be converted into electric energy.

[0039] Specifically, different types of energy usually have different micro-energy collectors. For example, the micro-energy collectors may include solar energy collectors (such as: solar panels), vibration energy collectors, radio frequency energy collectors, etc. Among them, the solar energy collector is a micro-energy collector for collecting solar energy, the vibration energy collector is a micro-energy collector for collecting vibration energy, and the radio frequency energy collector is a micro-energy collector for collecting electromagnetic energy, but is not limited thereto.

[0040] The micro energy harvesting module 11 can be coupled to the energy storage module 12 to transfer the harvested energy (or the converted electrical energy) to the energy storage module 12, or can be coupled to the power supply module 13 to transfer the harvested energy (or the converted electrical energy) to the power supply module 13, but is not limited thereto.

[0041] The energy storage module 12 is used to store the electrical energy after energy conversion. Specifically, the energy harvested by the micro energy harvesting module 11 can be converted into electrical energy and then transferred to the energy storage module 12 for storage. It should be noted that the energy stored in the energy storage module 12 is usually electrical energy.

[0042] The energy storage module 12 can include multiple energy storage elements. The energy storage elements can refer to elements used to store electrical energy. For example, they can be lithium batteries and / or supercapacitors, but are not limited thereto. The embodiments of the present invention do not impose any limitations on the number and types of the energy storage elements.

[0043] The energy storage module 12 can be coupled to the power supply module 13 and can transfer the stored electrical energy to the power supply module 13.

[0044] The power supply module 13 is used to be coupled to an external load and supply power to the external load. The electrical energy obtained by the power supply module 13 can come from the micro energy harvesting module 11, or from the energy storage module 12, or from both of the above. In other words, the electrical energy obtained by the power supply module 13 can only come from the micro energy harvesting module 11, can also only come from the energy storage module 12, or can come from both the micro energy harvesting module 11 and the energy storage module 12, but is not limited thereto.

[0045] The controller 14 can be used to control the direction of the energy harvested by the micro energy harvesting module in the composite micro energy system.

[0046] Specifically, the controller 14 can be coupled to the micro energy harvesting module 11 and is used to control the direction of each type of energy harvested by the micro energy harvesting module 11. More specifically, the controller 14 can be used to control whether the energy harvested by the micro energy harvesting module 11 is transferred to the energy storage module 12, or to the power supply module 13, or to both of the above at the same time.

[0047] Further, the controller 14 can also be coupled to the energy storage module 12 and is used to control whether the electrical energy stored in the energy storage module 12 needs to be transferred to the power supply module 13. More specifically, the controller 14 can also be coupled to each energy storage element in the energy storage module 12 and is used to control the electrical energy transferred from each energy storage element to the power supply module 13.

[0048] Further, the controller 14 may also be coupled to the power supply module 13 to obtain the current power supply demand, where the power supply demand refers to the electric power that the composite micro - energy system currently needs to supply externally. For example, it may be the electric power required by the load.

[0049] The composite micro - energy system may further include a monitoring module 15. The monitoring module 15 may be coupled to the micro - energy collection module 11 to monitor whether there is any abnormality in the micro - energy collection module 11. More specifically, the monitoring module 15 may be used to monitor whether there is any abnormality in each micro - energy collector. For example, whether various parameters of the micro - energy collector (such as peak power of output energy, etc.) are abnormal, and whether there are problems such as cracking, damage, or occlusion of the micro - energy collector itself.

[0050] Further, the monitoring module 15 may also be coupled to the energy storage module 12 to monitor whether there is any abnormality in the energy storage module 12. More specifically, the monitoring module 15 may be used to monitor whether there is any abnormality in each energy storage element in the energy storage module 12. For example: whether various parameters of the energy storage element (such as upper limit of stored energy, etc.) are abnormal, and aging of the energy storage element itself.

[0051] Reference Figure 2 , Figure 2 is a schematic flowchart of an energy control method for a composite micro - energy system in an embodiment of the present invention. The energy control method may be executed by a controller. The controller may be any suitable terminal with data receiving and processing capabilities. For example, it may be a computer, a sensing analyzer, etc., but is not limited thereto. The controller may be disposed inside the composite micro - energy system, and the controller is coupled to each device in the composite micro - energy system; or, the controller may be disposed outside the composite micro - energy system and remotely coupled to each device in the composite micro - energy system, but is not limited thereto. The composite micro - energy system may be a system capable of collecting the energy generated by multiple micro - energies, storing and / or releasing various energies. The composite micro - energy system may include a micro - energy collection module, an energy storage module, and a power supply module, but is not limited thereto. In a non - restrictive embodiment of the present invention, the composite micro - energy system is Figure 1 the composite micro - energy system shown.

[0052] Figure 2 The energy control method for the composite micro - energy system shown may include the following steps:

[0053] Step S201: Identify the characteristic information of each type of energy collected by the micro - energy collection module;

[0054] Step S202: Input the characteristic information of each type of energy and the required electric power of the load into the decision tree model to obtain the decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy; among them, the decision tree model is trained and generated using multiple first energy samples as training data.

[0055] Step S203: Control the direction of each type of energy according to the decision label to transmit at least a part of each type of energy to the power supply module and / or the energy storage module.

[0056] In the specific implementation of step S201, the characteristic information of each type of energy collected by the micro energy collection module can be obtained and identified at a preset time interval, and the preset time interval can be pre-configured. Among them, the characteristic information refers to the characteristics used to describe one or more aspects of the energy. For example, the characteristic information may include the type of energy and the electric power that the energy can be converted into, but is not limited thereto.

[0057] Specifically, the micro energy collection module may include multiple micro energy collectors. Since the types of energy are different and the micro energy collectors are also different, the type of energy can be determined according to the micro energy collector from which each type of energy comes. More specifically, each micro energy collector may have an energy identifier, and the micro energy collectors that collect the same type of energy may have the same energy identifier, and the type of energy can be determined by identifying the energy identifiers of each micro energy collector.

[0058] Furthermore, the convertible electric power of each type of energy in the micro energy collection module can also be obtained. The convertible electric power of the energy refers to the efficiency at which the energy can be converted into electric energy. Specifically, the convertible electric power of each type of energy in the micro energy collection module can be obtained through components such as sensors installed in the micro energy collection module. More specifically, the electric power that the energy in each micro energy collector can be converted into can also be obtained.

[0059] In the specific implementation of step S202, the required electric power of the external load can be obtained at a preset time interval, and the characteristic information of each type of energy and the required electric power of the load are input into the decision tree model to obtain the decision label for each type of energy.

[0060] Among them, the decision label can be used to indicate the direction of the energy. For example, it is transmitted to the power supply module and / or transmitted to the energy storage module. In a non-limiting embodiment of the present invention, the energy storage elements in the energy storage module include lithium batteries and supercapacitors, and the direction of the energy may include one or more of the following: transmitted to the power supply module, transmitted to the lithium battery, and transmitted to the supercapacitor.

[0061] It should be noted that the correlation relationship among the characteristic information of the energy, the required electric power of the load, and the decision label is time-related, and this correlation relationship may change over time. Specifically, the characteristic information of the energy and the required electric power of the load can belong to the same time interval, and the obtained decision label is also the trend of the energy within this time interval. That is, for each time interval, for the current characteristic information of each type of energy and the required electric power of the load, the current decision label of each type of energy is obtained.

[0062] In a non-limiting embodiment of the present invention, the state of charge of the energy storage module can be input into the decision tree model together with the characteristic information of each type of energy and the required electric power of the load to obtain the decision label of each type of energy. In other words, the decision label of each type of energy is determined based on the state of charge of the energy storage module, the characteristic information of the energy, and the required electric power of the load. Among them, the state of charge of the energy storage module also has a time-related correlation relationship with the characteristic information of the energy, the required electric power of the load, and the decision label. Compared with the scheme of determining the decision label of each type of energy based on the characteristic information of each type of energy and the required electric power of the load, the scheme of inputting the state of charge of the energy storage module into the decision tree model together with the characteristic information of each type of energy and the required electric power of the load to obtain the category label decision label of each type of energy can make the trend of the energy more in line with the actual needs of the hybrid micro-energy system and improve the utilization rate of the energy.

[0063] More specifically, the state of charge of each energy storage element in the energy storage module can be input into the decision tree model together with the characteristic information of the energy and the required electric power of the load. That is, the decision label of the energy can be determined by the characteristic information of the energy, the required electric power of the load, and the state of charge of each energy storage element that has a time-related correlation relationship with it.

[0064] Furthermore, the decision tree model is trained and generated using a plurality of first energy samples as training data. Each first energy sample can include the required electric power of the load, at least one sample energy, and the characteristic information and decision label of each sample energy. The sample energy can be selected from the energy collected by the micro-energy collection module. For example, it can be solar energy, vibration energy, electromagnetic energy, etc., but is not limited thereto. Each sample energy has characteristic information and a decision label, and the characteristic information and decision label of different sample energies can be different.

[0065] It should be noted that the required electric power of the load included in each first energy sample has a time-related correlation relationship with this first energy sample. More specifically, the required electric power of the load has a time-related correlation relationship with each sample energy in this first energy sample.

[0066] In a non-limiting embodiment of the present invention, each first energy sample may further include the state of charge of the energy storage module. The state of charge of the energy storage module may also have a time-related association with this first energy sample. More specifically, the state of charge of the energy storage module may include the states of charge of a plurality of energy storage elements. It should be noted that the specific content of the first energy sample depends on the input data of the decision tree model.

[0067] Furthermore, for each combination mode of the sample energy, all possible directions of each energy in this combination mode need to be included in the plurality of first energy samples. Herein, the combination mode refers to the types of sample energy in each first energy sample. Taking the combination mode of solar energy and vibration energy as an example, in the first energy sample with sample energy being solar energy and vibration energy, it is necessary to include the first energy samples in which the decision labels of solar energy are all optional decision labels, and the first energy samples in which the decision labels of vibration energy are all optional decision labels.

[0068] In a non-limiting embodiment of the present invention, considering that some of the first energy samples may be obtained when there are abnormalities in the composite micro-energy system, in order to ensure the accuracy of the decision tree model, it is necessary to screen the plurality of first energy samples.

[0069] Specifically, the range of possible values of the characteristic information of each sample energy and the range of possible values of the required electric power of the load can be determined first. The aforementioned range of possible values can be received from the outside or pre-stored in the controller. Among them, the range of possible values of the characteristic information of each sample energy can be the range of possible values of the energy identifier, or the range of possible values of the electric power that each energy can be converted into, but is not limited thereto.

[0070] Furthermore, it is judged whether the characteristic information of each first energy sample meets the range of possible values of the characteristic information, and it is judged whether the required electric power of the load corresponding to each first energy sample meets the range of possible values of the required electric power of the load. If both aspects are met, it can be judged that this first energy sample can be used to train and generate the decision tree model.

[0071] In a non-limiting embodiment of the present invention, the range of the state of charge of the energy storage module can also be determined, and the plurality of first energy samples are screened according to the state of charge of the energy storage module corresponding to the first energy sample, so as to obtain the first energy sample for generating the decision tree model.

[0072] Thus, by setting the range of possible values of the characteristic information and the range of possible values of the required electric power of the load, the plurality of first energy samples can be screened, the accuracy of the training data is improved, and the decision tree model is made more accurate.

[0073] Further, the method of using multiple first energy samples to train and generate a decision tree model can be any existing appropriate algorithm. For example, it can be the Classification and Regression Tree (CART) algorithm. That is, the decision tree model can be recursively constructed according to the principle of minimizing the Gini index.

[0074] Reference Figure 3 , Figure 3 is a schematic diagram of a decision tree model in an embodiment of the present invention. Figure 3 The shown decision tree model includes nodes and directed edges. Among them, the nodes include internal nodes 31 and leaf nodes 32. Among them, the internal nodes 31 represent various preset attributes, and the preset attributes can be determined according to the input of the decision tree model. For example: the type of energy, the convertible electric power of the energy, and the electric power required by the load, etc. The leaf nodes 32 can represent multiple decision labels.

[0075] Among them, each internal node 31 corresponds to an attribute test, and each internal node 31 contains multiple first energy samples that are divided into the child nodes of this internal node 31 according to the results of the attribute test. Starting from the topmost internal node among the internal nodes 31, the multiple first energy samples contained in each internal node 31 are recursively classified until the preset condition for stopping recursion is reached to obtain multiple leaf nodes 32. The preset condition for stopping recursion can be that the number of multiple first energy samples in each internal node 31 is less than the preset number, or the Gini index of the multiple first energy samples contained in each internal node 31 is less than the preset index, etc., but it is not limited to this.

[0076] Specifically, for the sample set D contained in each internal node 31, the sample set D includes multiple first energy samples, the sample set D has K sample subsets, and the following formula is used to calculate the Gini index of the sample set D:

[0077]

[0078] Among them, Gini(D) is the Gini index of the sample set D, and K is the number of child nodes of the internal node 31. is the probability that the first energy sample in the sample set D is classified into the sample subset D k of.

[0079] Further, there are multiple possible values for the attribute A corresponding to each internal node 31. For each possible value a, according to the test result of each first energy sample regarding the attribute A = a being "yes" or "no", the sample set D is split into two parts, the sample subset D1 and the sample subset D2, and the following formula is used to calculate the Gini index of the sample set D when A = a:

[0080]

[0081] Among them, Gini(D,a) is the Gini index when the value of attribute A of the internal node 31 is a, Gini(D1) is the Gini index of the sample subset D1, and Gini(D2) is the Gini index of the sample subset D2. is the probability that the first energy sample in the sample set D is assigned to the sample subset D1. is the probability that the first energy sample in the sample set D is assigned to the sample subset D2. The Gini index Gini(D,a) can represent the uncertainty when dividing the sample set D according to the value of attribute A of the first energy sample being a. The larger the Gini index Gini(D,a), the greater the uncertainty of this division method.

[0082] For different values a of attribute A, calculate the corresponding Gini index Gini(D,a) respectively, select the value a with the smallest Gini index, and divide the first energy samples of the internal node 31 into the sample sets of two child nodes according to the value a.

[0083] Furthermore, recursively call the above steps for the two child nodes until the preset condition for stopping recursion is met, thereby generating a decision tree model.

[0084] Furthermore, in the solution of the embodiment of the present invention, after training and generating a decision tree model, the obtained decision tree model can also be tested. The testing method can be any appropriate method that can be used to test the accuracy of the generated decision tree model. For example, the S-fold cross-validation method can be used for testing, but it is not limited to this.

[0085] Specifically, before training the decision tree model, multiple first energy samples can be divided into a training sample set and a test sample set. After training and generating a decision tree model using the first energy samples in the training sample set, input the required electric power of the load and the characteristic information of each sample energy of each first energy sample in the test sample set into the decision tree model to obtain the model decision label of each sample energy in the first energy sample. Compare the model decision label of each sample energy in each first energy sample with the decision label of this sample energy, and calculate the proportion of the number of first energy samples with consistent model decision labels and decision labels of each sample energy in all first energy samples. If the proportion is less than the preset proportion, it is determined that the decision tree model is inaccurate, and the decision tree model needs to be processed to improve the accuracy of the decision tree model. For example, pruning processing can be performed on the decision tree model. Through pruning processing, the decision tree model can be made more concise and overfitting can be prevented.

[0086] In a non-limiting embodiment of the present invention, before inputting the characteristic information of the energy and the required electric power of the load into the decision tree model, the decision tree model can also be calibrated. Specifically, the decision tree model is calibrated using a second energy sample that is independent of the first energy sample. Here, "independent" means that there is no correlation between the first energy sample and the second energy sample. For example, the first energy sample is the sample selected during the training of the decision tree model, and the second energy sample is the sample selected during the calibration before the actual use of the decision tree model. More specifically, in different usage scenarios, the required electric power of the load varies greatly, and the training data of the decision tree model cannot cover the required electric power of the load in all scenarios. Therefore, before actually using the decision tree model to control the direction of the energy, the second energy sample can be selected according to the actual usage scenario to calibrate the decision tree model.

[0087] Specifically, a plurality of second energy samples can be obtained, and each second energy sample includes the required electric power of the load and at least one sample energy and its characteristic information and a preset decision label. For more descriptions about the second energy sample, reference can be made to the relevant descriptions about the first energy sample above, which will not be elaborated here.

[0088] Furthermore, the characteristic information of each sample energy in each second energy sample and the required electric power of the load corresponding to this second energy sample can be input into the decision tree model to obtain the model decision label of each sample energy in this second energy sample. It should be noted that if the input of the decision tree model also includes the state of charge of the energy storage module, the state of charge of the energy storage module corresponding to each second energy sample needs to be input into the decision tree model together.

[0089] Furthermore, the model decision label of each sample energy in each second energy sample is compared with the preset decision label, and the proportion of the number of second energy samples in which the model decision label of each sample energy is consistent with the preset decision label to the total number of all second energy samples is calculated. If the proportion is less than the first preset threshold, pruning processing is performed on the decision tree model to update the decision tree model. Here, the first preset threshold can be pre-configured. The method of pruning processing can be various existing appropriate methods.

[0090] Thus, in the solution of the embodiment of the present invention, before the user uses the decision tree model to control the direction of the energy, pruning processing is performed on the decision tree model to calibrate the decision tree model, which can make the decision tree model adapt to the current actual needs, so as to more accurately determine the direction of the energy.

[0091] Continue to refer to Figure 2, in the specific implementation of step S203, after obtaining the decision label of each type of energy, the flow direction of each type of energy can be controlled according to the decision label of the energy, so as to transmit at least a part of each type of energy to the power supply module and / or the energy storage module.

[0092] Specifically, controllable paths can be provided between the micro energy harvesting module and the power supply module and the energy storage module. Control signals corresponding to the decision labels of each type of energy can be generated. The control signals can be used to set the path between the micro energy harvesting module and the power supply module to be conductive or disconnected, and can also be used to set the path between the micro energy harvesting module and the energy storage module to be conductive or disconnected. More specifically, the control signals can be used to set the path between each micro energy harvester and the power supply module to be conductive or disconnected, and can also be used to set the path between each energy storage element and the power supply module to be conductive or disconnected.

[0093] Thus, in the solution of the embodiment of the present invention, the characteristic information of each type of energy identified and the required electric power of the load are input into the trained decision tree model. The decision label of each type of energy can be determined according to the current characteristic information of each type of energy and the required electric power of the load, and then the flow direction of each type of energy can be controlled according to the decision label. Therefore, the flow direction of energy can be controlled according to the current characteristic information of each type of energy and the required electric power of the load. Such a control process can be executed every preset time. By such a method, the flow direction of energy can be adapted to the characteristic information of the energy currently collected by the micro energy harvesting module and the situation of the required electric power of the load, so that the flow direction of energy can be accurately determined, and various types of energy can be fully utilized, improving the energy utilization rate.

[0094] Reference Figure 4 , Figure 4 shows the energy control method of the second composite micro energy system in the embodiment of the present invention. The method is used to control the power supply ratio of each energy storage element in the energy storage module to the power supply module. It should be noted that the energy storage module can also be used to transmit electric energy to the power supply module.

[0095] Preferably, when the power supply module supplies power to the load, the electric energy collected in the micro energy harvesting module can be transmitted to the power supply module first. When the electric power that can be converted from the energy in the micro energy harvesting module is lower than the electric power threshold, the micro energy harvesting module no longer transmits electric energy to the power supply module. At this time, the energy storage module can continue to transmit electric energy to the power supply module. This method of first supplying power by the micro energy harvesting module and then supplying power by the energy storage module can avoid problems such as rapid aging caused by frequent charging and discharging of the energy storage elements in the energy storage module.

[0096] Figure 4 The energy control method of the shown composite micro energy system can include the following steps:

[0097] Step S401: Obtain the state of charge of multiple energy storage elements;

[0098] Step S402: According to the electric power to be provided by the energy storage module and the state of charge of the multiple energy storage elements, use a fuzzy control algorithm to obtain the power supply ratios of the multiple energy storage elements, where the electric power to be provided by the energy storage module is the difference between the electric power required by the load and the electric power that can be converted by the micro energy harvesting module;

[0099] Step S403: Determine the output electric power of each energy storage element according to the power supply ratio and the electric power to be provided by the energy storage module.

[0100] In the specific implementation of step S401, the state of charge (SoC) of each energy storage element in the energy storage module can be obtained. Preferably, the energy storage element can include a supercapacitor, and the supercapacitor has the advantages of fast charge and discharge. It can be understood that long-term repeated large-current charge and discharge of a lithium battery will cause the lithium battery to age rapidly. Compared with the solution where the energy storage element only includes a lithium battery, using a supercapacitor as the energy storage element can enable the supercapacitor and the lithium battery to cooperate effectively.

[0101] In the specific implementation of step S402, the electric power to be provided by the energy storage module and the state of charge of each energy storage element are respectively fuzzified. Specifically, the power range to which the electric power to be provided by the energy storage module belongs can be determined according to the electric power to be provided by the energy storage module, and the charge range to which the state of charge of each energy storage element belongs can be determined according to the state of charge of each energy storage element.

[0102] Among them, fuzzifying the electric power to be provided by the energy storage module and the state of charge of each energy storage element can be various existing appropriate algorithms. Preferably, the membership value method can be used for fuzzification.

[0103] Specifically, the membership functions of the electric power to be provided by the energy storage module and the state of charge of each energy storage element can be determined first. The intervals of the membership functions can be uniform or non-uniform. The membership function can be a double S-shaped membership function, a triangular membership function, a Gaussian membership function, an S-shaped membership function, a trapezoidal membership function, etc., but is not limited thereto. Then, the power range is determined according to the electric power to be provided by the energy storage module and its corresponding membership function, and the charge range corresponding to each energy storage element is determined according to the state of charge of each energy storage element and its membership function.

[0104] It should be noted that the electric power to be provided by the energy storage module is the difference between the electric power required by the load and the electric power that can be converted from the energy of the micro energy harvesting module. More specifically, when the electric power that can be converted from the energy in the micro energy harvesting module is lower than the electric power threshold, the micro energy harvesting module no longer transmits electric energy to the power supply module. To extend the service life of the devices in the micro energy harvesting module, etc., the electric power that can be converted from the energy of the micro energy harvesting module can be subtracted by a preset fixed value and then used to calculate the electric power to be provided by the energy storage module. The preset fixed value can be the electric power threshold.

[0105] Further, query the fuzzy control rule table according to the power range and the charge range of each energy storage element to determine the power supply ratio range of each energy storage element. The fuzzy control rule table can be pre-configured and used to describe the mapping relationship between the power range, the charge range, and the power supply ratio range. The fuzzy control rule table can include a set of fuzzy conditional statements in the if-then structure, so that the corresponding power supply ratio range can be queried based on the input power range and charge range.

[0106] Further, defuzzify the power supply ratio range of each energy storage element to obtain the power supply ratio of each energy storage element. In other words, determine the power supply ratio of each energy storage element according to the power supply ratio range of each energy storage element. The defuzzification method can be any appropriate existing algorithm, such as the maximum membership degree method, the weighted average method, the centroid method, etc., but is not limited thereto. Preferably, an appropriate method can be selected according to the requirements of the composite micro energy system or the actual operating conditions, so as to convert the fuzzy power supply ratio range into an accurate power supply ratio. More specifically, the defuzzification method can be selected according to the performance requirements of the composite micro energy system. For example, when the composite micro energy system has a high requirement for the calculation speed of determining the power supply ratio, the maximum membership degree method or the weighted average method can be selected; when the composite micro energy system has a high requirement for the calculation accuracy of determining the power supply ratio, the centroid method can be selected, but is not limited thereto. In a non-limiting embodiment of the present invention, the centroid method is used to defuzzify the power supply ratio range of each energy storage element to obtain the power supply ratio of each energy storage element.

[0107] In the specific implementation of step S403, the output electric power of each energy storage element can be calculated and determined according to the power supply ratio of each energy storage element and the electric power to be provided by the negative energy storage module, and the power supply module can be powered according to the output electric power of each energy storage element. Thus, in the solution of the embodiment of the present invention, the purpose of extending the service life of the system can be achieved while meeting the power supply requirements of the load.

[0108] Reference Figure 5 , Figure 5 shows the energy control method of the third composite micro energy system in the embodiment of the present invention. Compared withFigure 2 The energy control method of the shown composite micro energy system Figure 5 The energy control method of the shown composite micro energy system may further include the following steps:

[0109] Step S204: Obtain first feedback information, where the first feedback information is used to indicate whether the micro energy collector has been replaced;

[0110] Step S205: Determine whether the micro energy collector has been replaced according to the first feedback information. If so, obtain the energy convertible electric power of each replaced micro energy collector;

[0111] Step S206: Compare the energy convertible electric power of each replaced micro energy device collector with the second preset threshold. If the energy convertible electric power of any replaced micro energy collector does not exceed the second preset threshold, retrain and generate the decision tree model.

[0112] In the specific implementation of step S204, the energy convertible electric power in each micro energy collector can be obtained, and the energy convertible electric power in each micro energy collector is compared with the preset second preset threshold. When the energy convertible electric power in any micro energy collector does not exceed the second preset threshold, it can be determined that the micro energy collector is abnormal. More specifically, when the time that the energy convertible electric power in any micro energy collector continuously does not exceed the second preset threshold exceeds the first preset time, it can be determined that the micro energy collector is abnormal. The second preset threshold and the first preset time can be pre-configured.

[0113] Further, when it is determined that the micro energy collection device is abnormal, a first alarm message can be sent, and the first alarm message can indicate the abnormal micro energy collection device so that the user can replace it.

[0114] Further, the first feedback information can be received from the outside, and the first feedback information can be used to indicate whether the user has replaced the micro energy collector.

[0115] In the specific implementation of step S205, it can be determined whether the micro energy collector has been replaced according to the received first feedback information. In addition, if the first feedback information is not obtained after the second preset time, it can be determined that the micro energy collector has not been replaced.

[0116] Further, after it is determined that the micro energy collector has been replaced, the energy convertible electric power of the replaced micro energy collector can be obtained. Specifically, what can be obtained is the upper limit of the energy convertible electric power within the first preset time after the micro energy collector is replaced.

[0117] In the specific implementation of step S206, the energy-convertible electric power of the replaced micro energy harvester can be compared with a second preset threshold. If the energy-convertible electric power of any replaced micro energy harvester does not exceed the second preset threshold, the decision tree model can be retrained to enable the decision tree model to adapt to the composite micro energy system after replacing the micro energy harvester, realizing self-correction of the decision tree model.

[0118] Therefore, in the solution of the embodiment of the present invention, it is determined that it is not necessarily necessary to retrain and generate a decision tree model after replacing the micro energy harvester. Instead, the energy-convertible electric power and the second preset threshold are compared again to determine whether there is still an abnormal situation after replacement. If the abnormal situation disappears after replacing the micro energy harvester, there is no need to retrain and generate a decision tree model. Compared with retraining and generating a decision tree model every time the micro energy harvester is replaced, the solution in the embodiment of the present invention is more efficient.

[0119] In addition, compared with not regenerating the decision tree model after replacing the micro energy harvester, the solution in the embodiment of the present invention monitors the replaced micro energy harvester again, which can ensure timely detection of whether there is an abnormality in the replaced micro energy harvester, thereby ensuring the normal operation of the composite micro energy system.

[0120] In a non-limiting embodiment of the present invention, if it is determined according to the first feedback information that the micro energy harvester has not been replaced, the decision tree model can also be retrained to enable the decision tree model to adapt to the abnormal situation of the micro energy harvester, realizing self-correction of the decision tree model.

[0121] Specifically, multiple third energy samples can be obtained, and the decision tree model can be retrained using the third energy samples. Each third energy sample can include the power required by the load, at least one sample energy and its characteristic information, and a decision label. The characteristic information of the sample energy in each third energy sample meets the characteristic requirements of the energy collected by the micro energy harvester when the micro energy harvester is abnormal. For example, the energy-convertible electric power of the sample energy in each third energy sample does not exceed the second preset threshold. More specific content about retraining and generating the decision tree model using the third energy samples can refer to the above description about training and generating the decision tree model using the first energy samples, and will not be elaborated here.

[0122] In another non - restrictive embodiment of the present invention, considering that the model, type, etc. of the micro - energy harvester may change before and after replacement, resulting in the original decision - tree model being inapplicable to the replaced micro - energy harvester, it is also possible to calculate the difference between the electric power that can be converted from the energy of each replaced micro - energy harvester and the standard value of the electric power that can be converted from the energy of this micro - energy harvester, and compare the difference with a preset first - difference threshold. If the difference is greater than the first - difference threshold, it can be shown that there are significant differences between the replaced micro - energy harvester and the micro - energy harvester before replacement. At this time, a decision - tree model can be retrained and generated.

[0123] Among them, the standard value of the electric power that can be converted from the energy can be determined according to the micro - energy harvester. The standard values of the electric power that can be converted from the energy of different micro - energy harvesters can be different. More specifically, the standard values of the electric power that can be converted from the energy of different models of micro - energy harvesters can be different. For the replaced micro - energy harvester, the standard value of the electric power that can be converted from the energy can be calculated based on the electric power that can be converted from the energy of this micro - energy harvester within the third preset time before replacement. For example, it can be taking the median value of the electric power that can be converted from the energy within the third preset time as the standard value of the electric power that can be converted from the energy, but it is not limited to this.

[0124] In another non - restrictive embodiment of the present invention, it is also possible to obtain the state of charge of each energy - storage element and compare the state of charge of each energy - storage element with a preset third - preset threshold. When the state of charge of any one energy - storage element does not exceed the third - preset threshold, it can be determined that this energy - storage element is abnormal. More specifically, when the time during which the state of charge of any one energy - storage element continuously does not exceed the third - preset threshold exceeds the first - preset duration, it can be determined that this energy - storage element is abnormal. The third - preset threshold can be pre - configured.

[0125] Furthermore, when it is determined that this energy - storage element is abnormal, a second alarm message can be sent. The second alarm message can indicate the energy - storage element where the abnormality occurs, so that the user can replace it.

[0126] Furthermore, second feedback information can be received from the outside. The second feedback information can be used to indicate whether the user has replaced the energy - storage element.

[0127] Furthermore, it can be determined whether the energy - storage element has been replaced according to the received second feedback information. In addition, if the second feedback information is not obtained after the second preset time, it can be determined that the energy - storage element has not been replaced.

[0128] Further, after determining that the energy storage element has been replaced, the state of charge of the replaced energy storage element can be obtained. Specifically, the upper limit of the state of charge within a first preset time after the replacement of the energy storage element can be obtained. More specifically, the upper limit of the state of charge of the energy storage element within the first preset time can be obtained.

[0129] Further, the state of charge of each replaced energy storage element can be compared with the third preset threshold. If the state of charge of any replaced energy storage element does not exceed the third preset threshold, the decision tree model can be retrained.

[0130] In a non-limiting embodiment of the present invention, considering that the model, type, etc. of the energy storage element may change before and after replacement, resulting in the original decision tree model being inapplicable to the replaced energy storage element, the difference between the state of charge of each replaced energy storage element and the standard value of the state of charge of this energy storage element can also be calculated, and the difference is compared with a preset second difference threshold. If the difference is greater than the second difference threshold, it can indicate that there is a large difference between the replaced energy storage element and the energy storage element before replacement. At this time, the decision tree model can be retrained.

[0131] Among them, the standard value of the state of charge can be determined according to the energy storage element. The standard values of the state of charge of different energy storage elements can be different. More specifically, the standard values of the state of charge of different models of energy storage elements can be different. For the replaced energy storage element, the standard value of its state of charge can be calculated based on the state of charge of this energy storage element within a third preset time before replacement. For example, the intermediate value of the state of charge within the third preset time can be taken as the standard value of the state of charge, but it is not limited thereto.

[0132] More specific content regarding determining whether to retrain the decision tree model based on the second feedback information can be referred to the relevant description above regarding Figure 5 and will not be elaborated here.

[0133] Refer to Figure 6 , Figure 6 which is an energy control device of a composite micro energy system in an embodiment of the present invention. The device may include:

[0134] An identification module 61 for identifying the characteristic information of each type of energy collected by the micro energy collection module;

[0135] A classification module 62 for inputting the characteristic information of each type of energy and the required electric power of the load into the decision tree model to obtain a decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy;

[0136] A transmission control module 63, configured to control the flow direction of each type of energy according to the decision label, so as to transmit at least a part of each type of energy to the power supply module and / or the energy storage module;

[0137] Wherein, the decision tree model is generated by training with a plurality of first energy samples as training data.

[0138] For the principle, working mode and beneficial effects of the energy control device of the composite micro energy system in the embodiments of the present invention, please refer to the relevant descriptions of the energy control method of the composite micro energy system above, and will not be elaborated here.

[0139] Reference Figure 1 , an embodiment of the present invention further provides a composite micro energy system, which may include: a micro energy collection module 11, configured to collect at least one type of energy; an energy storage module 12, configured to store the electric energy after the energy conversion; a power supply module 13, configured to supply power to a load; and a controller 14, configured to execute the steps of the energy control method of the above-mentioned composite micro energy system.

[0140] Wherein, the controller may be coupled to a memory storing a computer program, and the controller may read the computer program in the memory and execute the steps of the energy control method of the above-mentioned composite micro energy system by running the computer program. It should be noted that the controller may be a processor independent of the memory, or a terminal integrated with a memory and a processor, but is not limited thereto.

[0141] For the principle, structure, working mode and beneficial effects of the composite micro energy system in the embodiments of the present invention, please refer to the relevant descriptions of the energy control method of the composite micro energy system above, and will not be elaborated here.

[0142] An embodiment of the present invention also discloses a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and the computer program can execute the steps of the above method when running. The storage medium may include ROM, RAM, a magnetic disk or an optical disc, etc. The storage medium may also include a non-volatile memory or a non-transitory memory, etc.

[0143] Among them, the processor can be a central processing unit (CPU for short), and the processor can also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), field programmable gate arrays (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0144] It should be noted that the controller may further include a memory, and the memory can be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM for short), a programmable read-only memory (PROM for short), an erasable programmable read-only memory (EPROM for short), an electrically erasable programmable read-only memory (EEPROM for short), or a flash memory. The volatile memory can be a random access memory (RAM for short), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM for short) are available, such as static random access memory (SRAM for short), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM for short), double data rate synchronous dynamic random access memory (DDR SDRAM for short), enhanced synchronous dynamic random access memory (ESDRAM for short), synchlink dynamic random access memory (SLDRAM for short), and direct rambus random access memory (DR RAM for short).

[0145] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0146] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not indicate the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0147] In several embodiments provided in the present application, it should be understood that the disclosed methods, devices, and systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of the units is only a logical function division, and there can be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0148] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can be physically included separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a hardware plus software functional unit.

[0150] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit stored in a storage medium includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute some steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs that can store program codes.

[0151] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0152] The term "a plurality of" appearing in the embodiments of this application refers to two or more.

[0153] The descriptions such as first and second appearing in the embodiments of this application are only for illustration and to distinguish the described objects, without an order, and do not represent a special limitation on the number of devices in the embodiments of this application, and cannot constitute any limitation to the embodiments of this application.

[0154] The term "connection" appearing in the embodiments of this application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and this application does not make any limitation on this.

[0155] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims.

Claims

1. An energy control method for a composite micro - energy system, characterized in that, The composite micro - energy system includes a micro - energy collection module, an energy storage module, and a power supply module. The micro - energy collection module is used to collect at least one type of energy. The energy storage module is used to store the electric energy after the energy conversion. The power supply module is used to supply power to the load. The method includes: Identifying the characteristic information of each type of energy collected by the micro - energy collection module; Inputting the state of charge of the energy storage module, the characteristic information of each type of energy, and the electric power required by the load into a decision tree model to obtain a decision label for each type of energy, where the decision label is used to indicate the direction of each type of energy; Controlling the direction of each type of energy according to the decision label to transmit at least a part of each type of energy to the power supply module and / or the energy storage module; Wherein, the decision tree model is generated by training with a plurality of first energy samples as training data; Wherein, the characteristic information includes: the type of energy and the electric power that the energy can be converted into. The electric power that the energy can be converted into refers to the efficiency of converting the energy into electric energy; Wherein, a controllable path is provided between the micro - energy collection module and the power supply module and the energy storage module. Controlling the direction of each type of energy according to the decision label to transmit at least a part of each type of energy to the power supply module and / or the energy storage module includes: Generating a corresponding control signal according to the decision label of each type of energy. The control signal is used to set the path between the micro - energy collection module and the power supply module to be conductive or disconnected, and to set the path between the micro - energy collection module and the energy storage module to be conductive or disconnected; The method for generating the decision tree model includes: Obtaining a plurality of first energy samples, each first energy sample including the electric power required by the load, at least one sample energy and its characteristic information, and a decision label; Using the plurality of first energy samples as the training data to train and generate the decision tree model.

2. The energy control method for a composite micro - energy system according to claim 1, characterized in that, Before using the plurality of first energy samples as the training data to train and generate the decision tree model, the method further includes: Determining the range of values that the electric power required by the load can take and the range of values that the characteristic information of each sample energy can take; Screening the plurality of first energy samples according to the characteristic information of each sample energy in each first energy sample and the electric power required by the load corresponding to this first energy sample to obtain the first energy samples for generating the decision tree model.

3. The energy control method for a composite micro - energy system according to claim 1, characterized in that, Before inputting the characteristic information of each type of energy and the electric power required by the load into the decision tree model, the method further includes: Obtaining a plurality of second energy samples, each second energy sample including the electric power required by the load, at least one sample energy and its characteristic information, and a preset decision label, where the plurality of second energy samples are independent of the plurality of first energy samples; Inputting the electric power required by the load and the characteristic information of each sample energy in each second energy sample into the decision tree model to obtain the model decision label of each sample energy in this second energy sample; Compare the model decision labels of each sample energy in each second energy sample with the preset decision labels, and calculate the proportion of the number of second energy samples in which the model decision labels of each sample energy are consistent with the preset decision labels in the total number of second energy samples. If the proportion is less than the first preset threshold, prune the decision tree model to update the decision tree model.

4. The energy control method for a composite micro - energy system according to claim 1, characterized in that, The energy storage module includes a plurality of energy storage elements. The energy storage module is also used to supply power to the load. The method further includes: Obtain the state of charge of a plurality of energy storage elements; according to the electric power that the energy storage module needs to provide and the state of charge of the plurality of energy storage elements, use a fuzzy control algorithm to obtain the power supply ratio of the plurality of energy storage elements, where the electric power that the energy storage module needs to provide is the difference between the electric power required by the load and the electric power that can be converted by the micro energy harvesting module. Determine the output electric power of each energy storage element according to the power supply ratio and the electric power that the energy storage module needs to provide.

5. The energy control method for a composite micro - energy system according to claim 4, characterized in that, The plurality of energy storage elements include supercapacitors.

6. The energy control method for a composite micro - energy system according to claim 4, characterized in that, Using a fuzzy control algorithm to obtain the power supply ratio of a plurality of energy storage elements according to the electric power that the energy storage module needs to provide and the state of charge of the plurality of energy storage elements includes: Determine the power range to which the electric power that the energy storage module needs to provide belongs according to the electric power that the energy storage module needs to provide, and determine the state of charge range to which the state of charge of each energy storage element belongs according to the state of charge of each energy storage element. Query the fuzzy control rule table according to the power range and the state of charge range of each energy storage element to determine the power supply ratio range of each energy storage element. Defuzzify the power supply ratio ranges of the respective energy storage elements to obtain the power supply ratios of the respective energy storage elements. Wherein, the fuzzy control rule table is used to describe the mapping relationship between the power range and the state of charge range and the power supply ratio range.

7. The energy control method for a composite micro - energy system according to claim 1, characterized in that, The micro energy harvesting module includes a plurality of micro energy harvesters. The method further includes: Obtain first feedback information, where the first feedback information is used to indicate whether a micro energy harvester is replaced. Judge whether a micro energy harvester is replaced according to the first feedback information. If so, obtain the electric power that can be converted by each replaced micro energy harvester. Compare the electric power that can be converted by each replaced micro energy harvester with a second preset threshold. If the electric power that can be converted by any one of the replaced micro energy harvesters does not exceed the second preset threshold, retrain and generate the decision tree model.

8. The energy control method of the composite micro energy system according to claim 1, characterized in that, The energy storage module includes a plurality of energy storage elements. The method further includes: Obtain second feedback information, where the second feedback information is used to indicate whether an energy storage element is replaced. Judge whether an energy storage element is replaced according to the second feedback information. If so, obtain the state of charge of each replaced energy storage element. Compare the upper limit of the state of charge of each replaced energy storage element with a third preset threshold. If the upper limit of the state of charge of any one of the replaced energy storage elements does not exceed the third preset threshold, retrain and generate the decision tree model.

9. An energy control device for a composite micro energy system, characterized in that, The composite micro - energy system includes a micro - energy collection module, an energy storage module, and a power supply module. The micro - energy collection module is used to collect at least one type of energy. The energy storage module is used to store the electric energy after the conversion of the energy. The power supply module is used to supply power to a load. The device includes: An identification module, which is used to identify the characteristic information of each type of energy collected by the micro - energy collection module; A classification module, which is used to input the state of charge of the energy storage module, the characteristic information of each type of energy, and the required electric power of the load into a decision tree model to obtain a decision label for each type of energy. The decision label is used to indicate the direction of each type of energy; A transmission control module, which is used to control the direction of each type of energy according to the decision label, so as to transmit at least a part of each type of energy to the power supply module and / or the energy storage module; Among them, the decision tree model is trained and generated by using a plurality of first energy samples as training data; Among them, the characteristic information includes: the type of energy and the electric power that the energy can be converted into. The electric power that the energy can be converted into refers to the efficiency of converting the energy into electric energy; Among them, a controllable path is provided between the micro - energy collection module and the power supply module and the energy storage module. The transmission control module is used to generate a corresponding control signal according to the decision label of each type of energy. The control signal is used to set the path between the micro - energy collection module and the power supply module to be conductive or disconnected, and to set the path between the micro - energy collection module and the energy storage module to be conductive or disconnected; The method for generating the decision tree model includes: Obtaining a plurality of first energy samples, each first energy sample including the required electric power of the load, at least one sample energy and its characteristic information, and a decision label; Using the plurality of first energy samples as the training data to train and generate the decision tree model.

10. A storage medium, on which a computer program is stored, characterized in that, When the computer program is run by a processor, it executes the steps of the energy control method of the composite micro - energy system according to any one of claims 1 to 8.

11. A composite micro energy system, characterized in that, The system includes: A micro - energy collection module, which is used to collect at least one type of energy; An energy storage module, which is used to store the electric energy after the conversion of the energy; A power supply module, which is used to supply power to a load; A controller, which is used to execute the steps of the energy control method of the composite micro - energy system according to any one of claims 1 to 8.

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