A wearable system planning and energy management method based on human needs

Abstract

The application discloses a wearable system planning and energy management method based on human needs, comprising the following steps: a wearable system is built with hardware, and a source network load storage collocation scheme based on a scene is planned; energy collection devices, energy storage devices and load devices in the classified types of equipment are modeled, and a behavior model of human needs of a wearer is established; a mapping from human needs to energy needs and information needs is established, and a device connection matrix and a power adjustment amount are generated; an energy management strategy is optimized based on human needs, and an optimal connection matrix and power adjustment amount are obtained. The application can fully utilize the collaborative complementation between the equipment and the wearer, improve the service quality of the wearable system and the comfort of the wearer. Meanwhile, the collaborative scheduling of the devices in the system, the efficient interaction with the wearer and the real-time response to the human needs are realized, and the application needs of multiple scenes of a set of systems can be met.

Description

Technical Field

[0001] This invention relates to the field of wearable system technology, and in particular to a wearable system planning and energy management method based on human needs. Background Technology

[0002] In recent years, wearable electronic devices have demonstrated enormous potential in applications such as health monitoring, human-computer interaction, and ubiquitous IoT, significantly changing our lifestyles. This progress has led to the growth of the wearable device market, but it has also increased individual power demands.

[0003] To reduce over-reliance on energy storage devices (such as batteries and supercapacitors), scholars both domestically and internationally have attempted to power electronic devices using wearable human energy harvesters. However, the output power of human energy harvesters is highly volatile and intermittent, and the operating mechanisms of different harvesters vary significantly. Therefore, the complementary and coordinated power supply of hybrid energy harvesters has gradually become a research focus, and based on this concept, some scholars have proposed self-powered wearable systems that are multi-energy complementary and have commensurate performance.

[0004] Multi-energy complementary and performance-matched self-powered wearable systems typically integrate multiple human energy harvesters, storage devices, control systems, and loads to form a small power network. Based on this body-based power network, various energy harvesters (such as photovoltaics and sweat generators) can work collaboratively, improving the reliability of energy supply. Furthermore, the control system can manage the generation, storage, and use of energy. For example, the system can select the proportion of energy output from the harvesters, control the charging and discharging rate of the energy storage devices, and adjust the real-time power of adjustable loads.

[0005] However, there is currently no unified design standard for self-powered wearable systems, nor a corresponding energy management method. Furthermore, the impact of wearer behavior and needs on energy management is difficult to quantify and therefore cannot be integrated into traditional energy management frameworks, resulting in low levels of intelligence and low-carbon features. Summary of the Invention

[0006] The purpose of this invention is to address the problem of low intelligence and low carbon footprint in wearable systems, and to provide a wearable system planning and energy management method based on human needs.

[0007] The technical problem of this invention is solved by the following technical solution:

[0008] This invention provides a wearable system planning and energy management method based on human needs, comprising the following steps:

[0009] S1. At the physical level, build the hardware for the wearable system and plan a scenario-based source-grid-load-storage combination scheme.

[0010] S2. At the information level, model the energy harvesting equipment, energy storage equipment and load equipment in the equipment classification type, and establish a behavioral model of the wearer's human body needs.

[0011] S3. Establish a mapping from human needs to energy and information needs, and generate a device connection matrix and power regulation.

[0012] S4. Optimize the energy management strategy based on human needs to obtain the optimal connection matrix and power regulation.

[0013] In some embodiments, in step S1, the wearable system generates a device configuration based on the scenario, device usage time, and personal needs, and works with the wearer of the system to complete the hardware setup before entering the practical application stage; the planning of the scenario-based source-grid-load-storage combination scheme includes classifying the internal devices of the wearable system and connecting the lines on the wearer according to the device classification type; the internal devices of the wearable system include energy harvesting devices, energy storage devices, load devices, and control devices.

[0014] In some embodiments, in step S2, the energy storage device has a capacity... Maximum power and minimum power The limitation is: capacity is Discharge power is Charging power is Where j = 1,...,J, This refers to the amount of charging power adjustment.

[0015] The power adjustment amount of the adjustable load of the load device is Adjusted power limit at Among them, adjustable load

[0016] The binding relationship between the independent energy system devices (IEDs) used to bind energy storage and load in the system is expressed as follows: Pa t B represents a collection of "battery-load combinations". IED and L IED These correspond to the energy storage and load components of the IED device, respectively. t = 1, ..., T represents the system's operating time, and j = 1, ..., J. IED and k = 1,...,K IED These represent the energy storage and load components of an IED device, respectively.

[0017] In some embodiments, the specific parameters involved in modeling the energy harvesting device, energy storage device, and load device in the device classification type in step S2 include: service reliability, which describes the likelihood of the energy storage device maintaining a connection. The load level H, used to describe a wearer's preference for a particular load, is defined as {h}. k :k=1,...,K ex} and estimated usage time of the load

[0018] In some embodiments, in step S2, the wearer's personal needs at time t in the established human needs behavior model are expressed as α. t :={α t,n :t=1,...,T,n=1,...,N}, use α t,n ∈{0,1} to determine whether there is a human need; where n=1,...,N represents the number and quantity of individual needs, and is assigned a weight W. t :={w t,n :t=1,...,T,n=1,...,N}.

[0019] In some embodiments, step S2 further includes detecting the wearer's plugging and unplugging behavior and correcting system parameters; the corrected system parameters are summarized as follows: Among them, connected device behavior Disconnect device behavior Weight W t The wearer's non-operational behavior is variable A. t .

[0020] In some embodiments, in step S3, device response models f for different requirements are generated. n Couple the body's needs with energy and information needs.

[0021] In some embodiments, in step S3, the generation of the device connection matrix Where J and K represent the amount of wearer grid energy storage and load, λ S , λ L θ S and θ L These represent the connection relationships from the data acquisition device to the energy storage device, from the data acquisition device to the load device, from the energy storage device to the energy storage device, and from the energy storage device to the load device, respectively; the generated power adjustment amount in, This refers to the amount of charging power adjustment. This is the adjustable load power adjustment amount.

[0022] In some embodiments, in step S3, it is necessary to connect the device matrix M. tand power regulation amount ζ t Multiple constraints are established; these constraints include a one-to-one correspondence between the operating load and the power supply, the inability of the battery to self-charge, the inability of the charged battery of a non-independent energy system device to supply the load, the inability of the load of an independent energy system device to be powered only by its own battery, and power level constraints.

[0023] In some embodiments, the optimization model for optimizing the energy management strategy in step S4 is as follows: Among them, f n For the device response model, W t As weight;

[0024] If the system still cannot meet the wearer's physical needs, the system will record the wearer's non-operational behavior A during the interaction with the wearer. t The optimization variables, incorporated into the aforementioned optimization model, guide wearers to regulate their personal behavior based on the updated optimization results.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention provides a wearable system planning and energy management method based on human needs. Through hardware construction, source-grid-load-storage (PGS) matching schemes, and the establishment of behavioral models, it maps human needs to energy and information requirements, ultimately obtaining the optimal connection matrix and power regulation. This fully leverages the synergistic complementarity between devices and wearers to improve system service quality and wearer comfort. Simultaneously, it enables coordinated scheduling of devices within the system, efficient interaction with wearers, and real-time response to human needs, meeting the multi-scenario application requirements of a single system.

[0027] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0028] Figure 1 This is a flowchart of a wearable system planning and energy management method based on human needs in an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the hardware relationships in the wearable system according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram illustrating the changes in outdoor temperature and solar panel output power over time in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram illustrating the proportion of individual needs in the first and second stages in an embodiment of the present invention;

[0032] Figure 5This is a schematic diagram illustrating the change of SOC (State of Charge) of a power bank over time in a wearable system according to an embodiment of the present invention. Detailed Implementation

[0033] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0034] An overview of the embodiments of the present invention is as follows:

[0035] A wearable system planning and energy management method based on human needs, such as Figure 1 As shown, it includes the following steps:

[0036] S1. At the physical level, build the hardware for the wearable system and plan a scenario-based source-grid-load-storage combination scheme.

[0037] Specifically, the wearable system generates device configurations based on scenarios, device usage time, and individual needs, and works with the wearer to complete hardware setup before entering the practical application stage; planning scenario-based source-grid-load-storage matching schemes includes classifying the devices inside the wearable system and connecting them to the wearer according to the device classification type; the devices inside the wearable system include energy harvesting devices, energy storage devices, load devices, and control devices.

[0038] Furthermore, at the physical level, hardware is built for wearable systems. For example... Figure 2 As shown, the wearable system mainly includes energy harvesting devices, energy storage devices, load devices, and control devices. In addition to these main devices, the system also includes electronic components such as circuit modules, boost / buck voltage regulator modules, etc.

[0039] Energy harvesting devices are connected to energy storage and load devices via wired or wireless connections on the human body to store or directly consume harvested energy.

[0040] Energy storage devices are connected to other energy storage devices and load devices via wired or wireless connections on the human body to balance the power between energy storage devices and provide stable power to the load.

[0041] The operating power of adjustable loads in the load equipment is controlled by the controller, while the power of non-adjustable loads cannot be changed and only accepts a stable power supply from the system or a disconnection operation;

[0042] By using electronic measurement modules, the operating status of the above-mentioned devices is collected and processed by the control equipment, providing data support for subsequent energy management; the wiring connections between the above-mentioned devices are all uniformly controlled by the control equipment, and the control methods include balancing the electricity between energy storage devices, switching different power sources for loads, adjusting variable load power, and changing demand weights.

[0043] S2. At the information level, model the energy harvesting equipment, energy storage equipment and load equipment in the equipment classification type, and establish a behavioral model of the wearer's human body needs.

[0044] Specifically, energy storage devices have a capacity Maximum power and minimum power The limitation is: capacity is Discharge power is Charging power is Where j = 1,...,J, This refers to the amount of charging power adjustment.

[0045] The power regulation of the adjustable load of the load device is Adjusted power limit at Among them, adjustable load

[0046] The binding relationship between the independent energy system devices (IEDs) used to bind energy storage and loads in the system is expressed as follows: Pa t B represents a collection of "battery-load combinations". IED and L IED These correspond to the energy storage and load components of the IED device, respectively. t = 1, ..., T represents the system's operating time, and j = 1, ..., J. IED and k = 1,...,K IED These represent the energy storage and load components of an IED device, respectively.

[0047] Specifically, in establishing the human needs behavior model, the wearer's personal needs at time t are expressed as α. t :={α t,n :t=1,...,T,n=1,...,N}, use α t,n ∈{0,1} to determine whether there is a human need; where n=1,...,N represents the number and quantity of individual needs, and is assigned a weight W. t :={w t,n :t=1,...,T,n=1,...,N}.

[0048] Specifically, this also includes detecting the wearer's plugging and unplugging behavior and correcting system parameters; the corrected system parameters are summarized as follows: Among them, connected device behavior Disconnect device behavior Weight W t The wearer's non-operational behavior is variable A. t .

[0049] Furthermore, the specific parameters involved in modeling the energy harvesting devices, energy storage devices, and load devices in the device classification types include: service reliability, which describes the likelihood of the energy storage device maintaining a connection. The load level H, used to describe a wearer's preference for a particular load, is defined as {h}. k :k=1,...,K ex} and estimated usage time of the load

[0050] Furthermore, the system models three main types of devices: energy harvesters, energy storage, and loads. In this embodiment, the system's operating time is divided into segments: t = 1, ..., T, and the harvesters, energy storage, and loads are numbered: i = 1, ..., I. ex ,j=1,...,J ex k = 1, ..., K ex The set of devices the system possesses at time t is: t=1,...,T,i=1,...,I ex ,j=1,...,J ex k = 1, ..., K ex}, where G, B, and L correspond to the set of energy harvesting, energy storage, and load devices, respectively.

[0051] For an energy harvesting device, assume its real-time output voltage is... Power generation capacity is Efficiency is For energy storage devices such as batteries, the battery's SOC (State of Charge) is... The charging and discharging power are respectively used and Indicated. Charge and discharge efficiencies are expressed as... and This indicates that energy storage devices generally have a capacity. Maximum and minimum power and Limitations. Therefore, the limitations can be described by the following formula:

[0052]

[0053]

[0054]

[0055] Assuming the charging power adjustment is and make in and These represent the minimum and maximum charging power limits, respectively. This defines service reliability. This describes the possibility of energy storage devices maintaining connectivity. In practical applications, low... Energy storage devices are more likely to detach from the system, so the system needs to de-prioritize them when allocating harvested energy. For load devices, their operating power is set to... Power limitation can be expressed as in and These represent the minimum and maximum operating power limits, respectively.

[0056] Define load level H:={h k :k=1,...,K ex This describes the wearer's usage preferences for a particular load. Furthermore, the wearer needs to provide the system with the estimated usage time for each load before use. The longest expected usage time is Based on the characteristics and functions of the load, they are divided into adjustable loads. (e.g., heat load) and non-adjustable loads (K A and K UA The sum is K ex Therefore, the power regulation of an adjustable load is defined as follows: Adjusted power limit at k = 1, ..., K A In addition, independent energy system devices within the system, such as mobile phones, are considered as bundled energy storage and loads. Therefore, this bundling relationship can be expressed as...

[0057]

[0058] Furthermore, a human needs behavior model needs to be established. Individual needs are dynamic and differentiated; wearers can have multiple needs simultaneously. According to Maslow's hierarchy of needs, these needs can be categorized into N types. At time t, the wearer's individual needs are expressed as α. t :={α t,n Given a variable t = 1, ..., T, n = 1, ..., N, and make α t,n We use the range ∈{0,1} to determine if there is a requirement. α t The value of W is determined by analyzing sensor data and wearer input requirements and weights. t :={w t,nThe weights W are obtained from the sequence t = 1, ..., T, n = 1, ..., N. The wearer can change the weights W at any time. t To obtain the required services and improve the wearable experience. Furthermore, the wearer's non-operational behaviors also affect system operation, which is represented here by variable A. t To describe this effect (A) t This will be directly reflected in the sensor data.

[0059] S3. Establish a mapping from human needs to energy and information needs, and generate a device connection matrix and power regulation.

[0060] Specifically, by generating device response models f for different needs. n Couple the body's needs with energy and information needs.

[0061] Specifically, generate a device connection matrix. Where J and K represent the amount of wearer grid energy storage and load, λ S , λ L θ S and θ L These represent the connection relationships from the data acquisition device to the energy storage device, from the data acquisition device to the load device, from the energy storage device to the energy storage device, and from the energy storage device to the load device, respectively; generating power regulation quantities. in, This refers to the amount of charging power adjustment. This is the adjustable load power adjustment amount.

[0062] Furthermore, a device connection matrix M is required. t and power regulation amount ζ t Establish multiple constraints; these constraints include a one-to-one correspondence between operating load and power supply, the inability of batteries to self-charge, the inability of charged batteries of non-independent energy system equipment to supply loads, the requirement that the load of independent energy system equipment can only be powered by its own battery, and power level constraints.

[0063] Furthermore, the system updates its device database by detecting plugging and unplugging behavior and adjusting system parameters. Wearer-initiated device plugging and unplugging changes the available devices in the system, thus affecting subsequent energy management strategies. Therefore, the following device connection behavior is defined:

[0064] and disconnect device behavior in and These represent the connected and disconnected sets of energy harvesters, respectively. After testing is complete, the device information within the system can be described as follows:

[0065] in This represents the set of energy harvesters after testing. and Therefore, the total influence of human factors can be summarized as follows:

[0066]

[0067] Furthermore, establish human body needs α t To energy demand β t and information demand γ t The mapping of human needs α depends on the system's internal functional devices and information in its device database (such as device type, operating mode, current status, and demand response model). Then, a rule-based approach (or other alternative methods) is used to map the human needs α. t Converted into energy demand β t and information demand γ t By analyzing β t and γ t The system extracts a list of devices to be used from the existing device database:

[0068] And generate the corresponding demand response model f n .

[0069] Furthermore, the connection matrix M is generated. t and power regulation amount ζ t According to D t We can obtain the connection matrix for four types of devices: from the data collector to the energy storage λ. S From the collector to the load λ L Energy storage to energy storage θ S Energy storage to load θ L The overall connection matrix

[0070]

[0071] Power regulation It consists of two parts: charging power and adjustable load power regulation. M is one of the two main optimization variables. t and ζ t The generation of this also generated the following multiple constraints.

[0072] Each operating load is matched with a power supply:

[0073] The battery cannot charge itself.

[0074] The battery being charged cannot power the load (non-independent power system equipment):

[0075] Independent energy system equipment can only be powered by its own battery:

[0076] Power level constraints:

[0077]

[0078]

[0079]

[0080]

[0081] In addition to the common limitations mentioned above, some constraints need to be added based on the inherent characteristics of the equipment. For example, loads that require stable voltage and current power supply cannot be directly connected to the data acquisition unit.

[0082] S4. Optimize the energy management strategy based on human needs to obtain the optimal connection matrix and power regulation.

[0083] Specifically, the optimization model for optimizing energy management strategies: Among them, f n For the device response model, W t As weight;

[0084] If the system still cannot meet the wearer's physical needs, the system will record the wearer's non-operational behavior A during the interaction with the wearer. t The optimization variables, incorporated into the aforementioned optimization model, guide wearers to regulate their personal behavior based on the updated optimization results.

[0085] Furthermore, human needs assessment. The optimization objective of energy management strategies is the degree to which the system meets individual needs. Here, a weighted device demand response model f is used. n This expresses the degree to which demand n is satisfied. Therefore, the optimization model for energy management strategy can be written as: By solving the above optimization problem, the system can obtain the optimal connection matrix and power regulation to improve service quality. If the system performance still does not meet the requirements, the system will assign individual behavior A... t Integrate these variables into the optimization variables, and use the optimization results to guide wearers in regulating their personal behavior.

[0086] like Figure 1 As shown, a wearable system and energy management method based on human needs includes the following steps:

[0087] (1) Configure the internal devices of the wearable system based on requirements. Based on the application scenario, usage time and personal needs, the system needs to complete the internal device configuration with the wearer before entering the practical stage;

[0088] (2) Input personal needs. The wearer transmits their weighted personal needs to the system control device through the interactive interface. These needs will serve as the optimization target for energy management in the next stage until the needs are updated or the device is discontinued;

[0089] (3) Adjust energy management strategies in real time based on environmental and human condition. During use, the system will dynamically assess the degree of demand satisfaction based on the received environmental and human sensor data, thereby adjusting the energy management strategy to achieve optimal performance;

[0090] (4) Based on resource sufficiency, the system chooses between timely adjustment or long-term planning. Before implementing a correction strategy, the system first assesses the remaining resource sufficiency if the strategy is adopted. If resources are insufficient, the system tends to adjust in real time, i.e., directly adopt the correction strategy. If resources are sufficient, the system tends to plan for the long term, adding the weights of potential needs or comfort-enhancing needs to the original needs, and resolving the energy management strategy.

[0091] (5) Changes in needs based on scenarios and personal preferences. In practical applications, wearers may have new needs for system services due to changes in the environment or personal needs. In this case, wearers can update their personal needs in the interactive interface, which will enable the system to change its optimization goals and energy management strategies to provide services that are more in line with their current needs;

[0092] The beneficial effects of a wearable system planning and energy management method based on human needs according to an embodiment of the present invention are as follows:

[0093] (1) The design method of wearable system based on human needs proposed in this invention has guiding significance for the future development of wearable systems, provides new ideas, and accelerates the standardization process.

[0094] (2) The energy management method proposed in this invention performs unified scheduling and planning of all energy information resources within the system, concentrates resources to balance the energy supply and demand of all equipment within the system, and overcomes the energy bottleneck effect of individual equipment.

[0095] (3) The energy management method proposed in this invention takes into account the influence of individual needs and behaviors, provides high-quality energy services to wearers, guides wearers’ behavior, further enhances the device’s responsiveness to individual needs, and also increases individual participation in energy management.

[0096] Example:

[0097] like Figure 3 As shown, this assumes the wearer plans to engage in outdoor activities in cold weather. The entire activity would take approximately 5 hours and requires features such as heating, communication, and health monitoring.

[0098] Step S1: Equip the wearable system with a hanging foldable solar panel (collector), a power bank (energy storage), a mobile phone (independent energy system device), a heating pad (load), a sensor (load), and a controller (load).

[0099] Step S2: Model the device and generate device constraints, including power limits and operating modes. Additionally, based on the wearer's input, it can be determined that due to the cold weather during the first phase (10:00 to 14:00), the wearer's needs are distributed as follows: Figure 4 As shown. This stage focuses on battery life and warmth, with the weighting of battery life, warmth, low carbon footprint, safety, and other requirements as follows: Figure 4 As shown, the ratio is 0.5:0.3:0.1:0.05:0.05; During the second phase (14:00 to 15:00), the weather warms up and the sun becomes stronger. This phase prioritizes low-carbon living, with the weighting of factors such as range, warmth, low carbon footprint, safety, and other requirements being as follows: Figure 4 As shown, the values ​​are 0.15:0.15:0.6:0.05:0.05.

[0100] Step S3: Before generating the energy management strategy, the system uses a rule-based approach to map individual needs to energy and information requirements, and derives corresponding functional devices and demand response models. This is illustrated by three main requirements: battery life, thermal comfort, and low carbon footprint.

[0101] like Figure 4 As shown, the first requirement is critical equipment endurance, which necessitates sufficient energy storage to continuously power critical equipment. The system can respond in three ways: increasing capacity, reducing energy consumption, and balancing internal supply and demand. Achieving these goals requires the collaboration of all equipment within the system. The first step is to estimate the remaining endurance of the load. And assess the gap between its actual usage time and the expected usage time. Based on the connection matrix and power regulation, the battery's charging power can be calculated. and discharge power To estimate the remaining battery life

[0102]

[0103]

[0104]

[0105] In the estimation, it is assumed that the subsequent power generation and power consumption will remain constant, that is, there is Then, the difference G between remaining battery life and expected battery life under load. k It can be calculated that:

[0106]

[0107] Finally, taking into account the impact of load levels, the formula for evaluating system battery life requirements is as follows:

[0108]

[0109] like Figure 4 As shown, the second item is thermal comfort, which requires the system to provide heat to the body parts; the corresponding functional device is a heating pad. Therefore, the level of thermal comfort is highly related to the operating power of the heating pad, and the relationship between a single device and thermal comfort can be expressed as:

[0110]

[0111] in It is determined by equipment characteristics, environmental data, and personal preferences. The formula for assessing an individual's total thermal comfort requirements is as follows:

[0112]

[0113] like Figure 4 As shown, the third item is low carbon, which is directly related to solar power generation and load power consumption. The low carbon demand response formula is shown below:

[0114]

[0115] The fourth item, safety, and the fifth item, other relevance, remain unchanged and are therefore disregarded.

[0116] Step S4: Based on the above optimization model, the system can provide specific energy management strategies at different times. For example, at 13:00, due to the cold weather and limited solar radiation, individual heating needs are higher. Therefore, the operating power of the heating pad is adjusted to P. heating =18.2W. Since only the energy storage system can provide high-power supply, and the power bank has 31% SOC remaining, the power bank powers the heating pad, while the phone battery powers itself, the controller, and the sensors. The energy management strategy at 13:00 is as follows:

[0117]

[0118] At 13:30, as the temperature gradually warmed up and the power bank's SOC remained at approximately 10%, the system adjusted the heating pad's operating power to P. heating =5W, operating at the lowest heating power. In this mode, the phone battery powers the heating pad via the OTG (On-The-Go) interface. The energy management strategy at this time is as follows:

[0119]

[0120] As solar radiation gradually increases, the need for heating is not high. After 2 PM, the wearer modified their personal needs, such as... Figure 4 As shown, low carbon emissions are given a higher weight. At 14:05, the power bank's SOC was around 8%, while the phone battery's SOC was 43%. The system chose to continue using the heating pad at the lowest heating power P. heating =5W operation, with the phone powering all loads. At this time, the solar panel can generate approximately 10W of power. The system connects the solar panel to a power bank to supplement energy for later use. The energy management strategy at this time is as follows:

[0121]

[0122] In traditional methods, energy flow is relatively fixed. A power bank only supplies power to the heating pad at its rated power, and a phone battery only powers the phone. This power supply method leads to a mismatch between operating power and demand, as well as uneven resource distribution. Applying wearable systems, guided by individual needs and coordinating the operation of various devices, allows for a redistribution of resources within a unified framework. Simulation results show... Figure 5 As shown, the power bank's battery life (with SOC = 5% as the cutoff time) has been increased from 12:45 to 14:30, extending the heating pad's operating time by approximately 64%, while the optimization target value based on individual needs has been increased by approximately 23%.

[0123] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A method for wearable system planning and energy management based on human needs, characterized by, The steps include the following: S1. At the physical level, build the hardware for the wearable system and plan a scenario-based source-grid-load-storage combination scheme. S2. At the information level, model the energy harvesting equipment, energy storage equipment and load equipment in the equipment classification type, and establish a behavioral model of the wearer's human body needs. S3. Establish a mapping from human needs to energy and information needs, and generate a device connection matrix and power regulation. S4. Optimize the energy management strategy based on human needs to obtain the optimal connection matrix and power regulation amount; In step S3, device response models with different requirements are generated. Couple human needs with energy and information needs; the generating device connection matrix ,in and Represents the amount of grid energy storage and load on the wearer's grid. , , and These represent the connection relationships from the data acquisition device to the energy storage device, from the data acquisition device to the load device, from the energy storage device to the energy storage device, and from the energy storage device to the load device, respectively; the generated power adjustment amount ,in, This refers to the amount of charging power adjustment. The adjustable load power adjustment amount; In step S3, a device connection matrix needs to be established. and power regulation Establish multiple constraints; the multiple constraints include one-to-one correspondence between operating load and power supply, battery cannot self-charge, the rechargeable battery of non-independent energy system equipment cannot supply load, the load of independent energy system equipment can only be powered by its own battery, and power level constraints. In step S4, the energy management strategy is optimized using the following optimization model: ;in, For the device response model, As weight; If the system still cannot meet the wearer's physical needs, the system will record the wearer's non-operational behaviors during the interaction process. The optimization variables, incorporated into the aforementioned optimization model, guide wearers to regulate their personal behavior based on the updated optimization results.

2. The method according to claim 1, characterized in that, In step S1, the wearable system generates device configurations based on scenarios, device usage time, and individual needs, and works with the wearer of the system to complete hardware setup before entering the practical application stage; the planning of scenario-based source-grid-load-storage matching schemes includes classifying the devices inside the wearable system and connecting the lines on the wearer according to the device classification type; the devices inside the wearable system include energy harvesting devices, energy storage devices, load devices, and control devices.

3. The method according to claim 1, characterized in that, In step S2, the energy storage device has a capacity... Maximum power and minimum power The limitation is: capacity is The discharge power is Charging power is ,in, , This refers to the amount of charging power adjustment. This refers to the battery's state of charge. and These represent the charging and discharging power, respectively. The power adjustment amount of the adjustable load of the load device is The adjusted power limit is , ;in, and These represent the minimum and maximum operating power limits, respectively, for adjustable loads. ; The binding relationship between the independent energy system devices (IEDs) used to bind energy storage and load in the system is expressed as follows: ;in This represents a collection of "battery-load combinations". and These correspond to the energy storage and load components of the IED device, respectively. This represents the system's runtime. and These represent the energy storage and load components of an IED device, respectively.

4. The method according to claim 1 or 3, characterized in that, In step S2, the specific parameters involved in modeling the energy harvesting devices, energy storage devices, and load devices in the device classification types include: service reliability, which describes the likelihood of the energy storage device maintaining a connection. Load level is used to describe a wearer's usage preference for a particular load. and estimated usage time of the load .

5. The method according to claim 1, characterized in that, In step S2, the wearer in establishing the human body demand behavior model... Personal needs expressed at any moment ,use Determine if there is a human need; among which These represent the individual's needs, numbered and quantity, and assigned weights. .

6. The method according to claim 1, characterized in that, Step S2 further includes detecting the wearer's plugging and unplugging behavior and correcting system parameters; the corrected system parameters are summarized as follows: Among them, the behavior of the connected device Disconnect device behavior Weight The wearer's non-operational behavior is a variable. , and These represent the sets of energy harvesters that are connected and disconnected, respectively.

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