Solid-state battery intelligent management system for cluster-oriented agricultural unmanned aerial vehicle

Abstract

This invention relates to the field of drone battery management technology and discloses an intelligent management system for solid-state batteries of agricultural drones for swarm operations. The system includes a detection module, a detection data acquisition module, a drone status assessment module, a battery module status assessment module, a grouping module, a module unit data acquisition module, and an analysis and control module. The detection module obtains drone detection information data; the detection data acquisition module collects module detection information data; the drone status assessment module determines whether the drone is functioning normally; the battery module status assessment module obtains the state index of the battery module; the grouping module divides each battery module into multiple module units; the module unit data acquisition module collects unit information data; and the analysis and control module regulates the charging and discharging state of each module unit. This ensures that battery modules with similar state indices are grouped into the same module unit, significantly reducing the number and time of equalization adjustments and improving the overall operating efficiency of the battery management system.

Description

Technical Field

[0001] This invention relates to the field of drone battery management technology, and more specifically to an intelligent management system for solid-state batteries of agricultural drones for swarm operations. Background Technology

[0002] With the rapid development of drone technology, the low-altitude economy is moving from concept to reality. Breakthroughs in drone technology have not only reduced the cost and barriers to low-altitude flight, but also expanded the application boundaries of the low-altitude economy through its intelligent, clustered, and long-endurance characteristics. In particular, in the agricultural field, drones have been deeply integrated into the entire agricultural production cycle from sowing to harvesting, playing an indispensable and crucial role.

[0003] In drone swarm operation scenarios, the battery management system is a core component that ensures the continuous and stable operation of drones, and its performance directly affects the efficiency and safety of swarm operations. Traditional drone battery management solutions generally adopt a centralized equalization strategy, that is, uniformly regulate the charging and discharging of all individual batteries.

[0004] However, as the scale of drone swarms continues to expand, the number of batteries increases exponentially. The practice of independently monitoring and controlling each battery cell leads to a sharp increase in system computational complexity and a significant decline in balancing efficiency, making it difficult to meet the stringent real-time requirements of large-scale swarm operations. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management system for solid-state batteries in agricultural drones for swarm operations, thereby solving the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A solid-state battery intelligent management system for agricultural drones operating in swarms, the system comprising:

[0008] The detection module is used to perform preset detection actions on the UAV before executing cluster operations to obtain UAV detection information data;

[0009] Multiple detection data acquisition modules, one-to-one with the battery module, are used to collect module detection information data of each corresponding battery module during the drone's execution of preset detection actions;

[0010] The drone status assessment module is used to analyze drone detection information data to obtain the drone health index; and to determine whether the drone status is normal based on the drone health index.

[0011] The battery module status assessment module is used to analyze the detection information data of each module when the drone is in normal condition, and obtain the status index of each battery module.

[0012] The grouping module is used to analyze the state index of each battery module according to preset grouping rules and divide each battery module into several module units.

[0013] The module unit data acquisition module is used to collect unit information data of each module unit when executing cluster jobs;

[0014] The analysis and control module is used to analyze the unit information data, generate a module unit balancing strategy, and regulate the charging and discharging state of each module unit according to the module unit balancing strategy.

[0015] As a further aspect of the present invention: the UAV detection information data includes the UAV's detection trajectory, detection action completion rate, and detection action completion time.

[0016] As a further aspect of the present invention, the working process of the system is as follows:

[0017] S1: Before performing cluster operations, the UAV performs preset detection actions through the detection module to obtain the UAV's detection trajectory, detection action completion rate, and detection action completion time; and the detection data acquisition module collects module detection information data of each corresponding battery module during the UAV's execution of preset detection actions.

[0018] S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion rate to obtain the drone health index; and determines whether the drone's status is normal based on the drone health index; if the status is normal, proceed to step S3; otherwise, perform maintenance on the drone.

[0019] S3: The battery module status assessment module analyzes the detection information data of each module to obtain the status index of each battery module.

[0020] S4: The state index of each battery module is analyzed by the grouping module according to the preset grouping rules, and each battery module is divided into several module units.

[0021] S5: The module unit data acquisition module collects unit information data of each module unit when executing cluster operations;

[0022] S6: The analysis and control module analyzes the unit information data, generates a module unit balancing strategy, and regulates the charging and discharging state of each module unit according to the module unit balancing strategy.

[0023] As a further aspect of the present invention: the module detection information data includes the voltage, current and power consumption of the corresponding battery module.

[0024] As a further aspect of the present invention: through the formula:

[0025] ;

[0026] Calculate the health index of drones ;

[0027] in, As the first judgment function, when hour, ;when hour, ; The degree of overlap between the detection trajectory in this test and the preset detection trajectory; Preset overlap; This refers to the completion rate of the testing actions in this inspection. The preset detection action completion rate; This refers to the completion time of the detection action in this test; This is the preset time for the detection action to complete.

[0028] As a further aspect of the present invention: the process of determining whether the drone's status is normal is as follows:

[0029] when At that time, the drone's status was abnormal;

[0030] when At that time, the drone was in normal condition.

[0031] As a further aspect of the present invention: the process for obtaining the state index of any battery module is as follows:

[0032] S10: Obtain the voltage and current of the battery module during the drone's current preset detection action through the detection data acquisition module;

[0033] S20: By analyzing the voltage and current of the battery module during the drone's execution of the preset detection action, the internal resistance deviation index of the battery module is obtained;

[0034] S30: The battery module's power consumption and percentage of power consumption during the drone's current preset detection action are obtained and analyzed through the detection data acquisition module to obtain the battery consumption deviation index.

[0035] S40: The state index of the battery module is obtained by analyzing the internal resistance deviation index and the battery consumption deviation index.

[0036] As a further aspect of the present invention: In step S20, the formula is used:

[0037] ;

[0038] Calculate the internal resistance deviation index of any battery module ;

[0039] in, For any battery module; where, For the second judgment function, when hour, ;when hour, ; This is the start time of the drone's current detection action; This is the end time of the drone's current detection action; The curve showing the voltage change of the battery module over time during the drone's inspection operation; The curve showing the change in current of the battery module over time during the drone's inspection operation; A preset curve showing the voltage change of the battery module over time during the drone's detection operation; The preset curve showing the change in current of the battery module over time during the drone's detection operation; This is the allowable error value for internal resistance.

[0040] As a further aspect of the present invention: In step S30, the formula is used:

[0041] ;

[0042] Calculate the battery consumption deviation index for any battery module ;

[0043] in, This refers to the power consumption of the battery module during the drone's testing operation. This is the rated capacitance of the battery module; The preset percentage of power consumed by the battery module for the drone to perform detection actions; This represents the allowable error value for power consumption.

[0044] As a further aspect of the present invention: In step S40, the formula is used:

[0045] ;

[0046] Calculate the state index of any battery module ;

[0047] in, This is the first weighting coefficient; This is the second weighting coefficient; This is the first preset constant; This is the second preset constant.

[0048] As a further aspect of the present invention: the preset grouping rule is as follows:

[0049] S100: The module unit spacing width is obtained by analyzing the state index of each battery module;

[0050] S200: Group the battery modules according to the spacing width of the module units.

[0051] As a further aspect of the present invention: In step S100, the formula is used:

[0052] ;

[0053] Calculate the module unit spacing width ;

[0054] in, Preset module unit spacing width; This is the first preset constant; This represents the total number of battery modules. This is the average state index of all battery modules in the drone.

[0055] The beneficial effects of this invention are:

[0056] This invention utilizes a detection module to perform preset detection actions on the drone before swarm operations, obtaining drone detection information data. Each detection data acquisition module collects module detection information data for each corresponding battery module during the preset detection actions. A drone status assessment module analyzes the drone detection information data to obtain a drone health index, and determines whether the drone's status is normal based on the drone's health index. When the drone's status is normal, a battery module status assessment module analyzes the detection information data for each module to obtain a status index for each battery module. Finally, a grouping module divides each battery module into several groups according to preset grouping rules. The system consists of several modules; a module unit data acquisition module collects unit information data from each module unit during cluster operations; finally, an analysis and control module analyzes the unit information data to generate a module unit balancing strategy and regulates the charging and discharging state of each module unit according to the strategy. This allows battery modules with similar state indices to be grouped into the same module unit, and during balancing adjustments, a balancing strategy can be formulated based on the common characteristics of the batteries in the group. It eliminates the need for frequent fine-tuning of each individual battery; only macroscopic control of the entire group is required. This significantly reduces the number and time required for balancing adjustments, improving the overall operating efficiency of the battery management system. Attached Figure Description

[0057] The invention will now be further described with reference to the accompanying drawings.

[0058] Figure 1 This is a system module framework diagram of one embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 As shown, in one embodiment, an intelligent management system for solid-state batteries in agricultural drones for swarm operations is provided. This system is applicable to solid-state batteries, which include several battery modules connected in series. Each battery module includes several individual cells connected in parallel. The system includes:

[0061] The detection module is used to perform preset detection actions on the UAV before executing cluster operations to obtain UAV detection information data;

[0062] Multiple detection data acquisition modules, one-to-one with the battery module, are used to collect module detection information data of each corresponding battery module during the drone's execution of preset detection actions;

[0063] The drone status assessment module is used to analyze drone detection information data to obtain the drone health index; and to determine whether the drone status is normal based on the drone health index.

[0064] The battery module status assessment module is used to analyze the detection information data of each module when the drone is in normal condition, and obtain the status index of each battery module.

[0065] The grouping module is used to analyze the state index of each battery module according to preset grouping rules and divide each battery module into several module units.

[0066] The module unit data acquisition module is used to collect unit information data of each module unit when executing cluster jobs;

[0067] The analysis and control module is used to analyze the unit information data, generate a module unit balancing strategy, and regulate the charging and discharging state of each module unit according to the module unit balancing strategy.

[0068] Through the above technical solution, this embodiment uses a detection module to perform preset detection actions before executing cluster operations, obtaining drone detection information data; each detection data acquisition module collects module detection information data of the corresponding battery modules during the drone's preset detection actions; the drone status assessment module analyzes the drone detection information data to obtain a drone health index; and determines whether the drone's status is normal based on the drone health index; when the drone's status is normal, the battery module status assessment module analyzes the module detection information data to obtain a status index for each battery module; the grouping module divides each battery module into several module units according to preset grouping rules; the module unit data acquisition module collects unit information data of each module unit during cluster operations; finally, the analysis and control module analyzes the unit information data, generates a module unit balancing strategy, and regulates the charging and discharging status of each module unit according to the module unit balancing strategy; the battery module status index intuitively reflects the current status of the battery module, enabling operators or the system to... A clear understanding of the performance level of each battery module facilitates timely intervention; it effectively avoids operational interruptions or malfunctions caused by potential battery module problems, ensuring the initial stability of cluster operations; by dividing battery modules into several groups based on their state indices, modules with similar state indices are grouped into the same module unit. During equalization adjustments, equalization strategies can be tailored to the common characteristics of the group of batteries. For example, for a group of batteries with good health and low internal resistance, the upper limit of the charging current can be appropriately increased to accelerate the charging speed; while for a group of batteries with slightly poor health and high internal resistance, the charging current can be reduced to avoid overcharging and damage to the batteries. This targeted adjustment method can more efficiently achieve equalization of the batteries within the battery pack; because the health states of batteries in the same group are similar, the differences in parameters such as charge and voltage between them are relatively small; therefore, during the equalization adjustment process, it is not necessary to frequently perform fine adjustments on each battery, but only macroscopic control of the entire group is required. This greatly reduces the number and time of equalization adjustments, improving the overall operational efficiency of the battery management system.

[0069] As one embodiment of the present invention, the UAV detection information data includes the UAV's detection trajectory, detection action completion rate, and detection action completion time;

[0070] It should be noted that the detection trajectory, detection action completion rate, and detection action completion time of the drone are existing technologies and will not be described in detail here.

[0071] As one embodiment of the present invention, the working process of the system is as follows:

[0072] S1: Before performing cluster operations, the UAV performs preset detection actions through the detection module to obtain the UAV's detection trajectory, detection action completion rate, and detection action completion time; and the detection data acquisition module collects module detection information data of each corresponding battery module during the UAV's execution of preset detection actions.

[0073] S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion rate to obtain the drone health index; and determines whether the drone's status is normal based on the drone health index; if the status is normal, proceed to step S3; otherwise, perform maintenance on the drone.

[0074] S3: The battery module status assessment module analyzes the detection information data of each module to obtain the status index of each battery module.

[0075] S4: The state index of each battery module is analyzed by the grouping module according to the preset grouping rules, and each battery module is divided into several module units.

[0076] S5: The module unit data acquisition module collects unit information data of each module unit when executing cluster operations;

[0077] S6: The unit information data is analyzed by the analysis and control module to generate a module unit balancing strategy and adjust the charging and discharging state of each module unit according to the module unit balancing strategy.

[0078] Through the above technical solution, this embodiment first uses a detection module to perform preset detection actions before executing cluster operations, obtaining the drone's detection trajectory, detection action completion rate, and detection action completion time; then, a detection data acquisition module collects module detection information data for each corresponding battery module during the drone's execution of the preset detection actions; next, a drone status assessment module analyzes the drone's detection trajectory and detection action completion rate to obtain a drone health index; and then determines whether the drone's status is normal based on the drone health index; if the status is normal, the battery module status assessment module analyzes the detection information data of each module to obtain the status of each battery module. The battery management system first analyzes the state index of each battery module according to preset grouping rules, then divides each battery module into several module units. Next, the module unit data acquisition module collects unit information data from each module unit during cluster operations. Finally, the analysis and control module analyzes the unit information data, generates a module unit balancing strategy, and regulates the charging and discharging state of each module unit based on this strategy. During the balancing process, frequent fine-tuning of each battery is unnecessary; only macroscopic control of the entire group is required. This significantly reduces the number and time required for balancing adjustments, improving the overall efficiency of the battery management system.

[0079] As one embodiment of the present invention, the module detection information data includes the voltage, current and power consumption of the corresponding battery module;

[0080] It should be noted that the methods for obtaining the voltage, current, and power consumption of each battery module are existing technologies and will not be described in detail here.

[0081] As one embodiment of the present invention, the formula is as follows:

[0082] ;

[0083] Calculate the health index of drones ;

[0084] in, As the first judgment function, when hour, ;when hour, ; The degree of overlap between the detection trajectory in this test and the preset detection trajectory; Preset overlap; This refers to the completion rate of the testing actions in this inspection. The preset detection action completion rate; This refers to the completion time of the detection action in this test; The preset completion time for the detection action;

[0085] The process for determining whether a drone is in normal condition is as follows:

[0086] when At that time, the drone's status was abnormal;

[0087] when At that time, the drone was in normal condition;

[0088] Through the above technical solution, this embodiment This is the difference between the overlap between the detected trajectory and the preset detection trajectory and the preset overlap; in the formula... In the first judgment function In It refers to This is used to determine whether the overlap between the detection trajectory and the preset detection trajectory exceeds a preset overlap; when This indicates that the detection trajectory in this test has a high degree of overlap with the preset detection trajectory. The UAV performed well in executing the preset actions during this flight mission, accurately flying along the planned route and meeting the mission's requirements for trajectory accuracy. ;when This indicates that the overlap between the detected trajectory and the preset trajectory is low, meaning the UAV performed poorly in executing the preset actions during this flight mission, deviating significantly from the planned route and failing to meet the mission's accuracy requirements. ; This is the difference between the completion rate of the detection action in this test and the preset completion rate of the detection action; in the formula... In the first judgment function In It refers to This is used to determine whether the completion rate of the detection action in this test exceeds the preset completion rate of the detection action; when This indicates that the detection actions were completed to a high degree, and the UAV performed well in executing the preset actions during this flight mission, accurately performing the planned actions and meeting the mission's accuracy requirements. ;when This indicates that the completion rate of the detection actions in this test was low. The UAV performed poorly in executing the preset actions during this flight mission, failing to execute them accurately as planned and thus not meeting the mission's accuracy requirements. ; The ratio of the completion time of the preset detection action to the completion time of the detection action in this test is considered. A larger ratio indicates a shorter completion time for the detection action, meaning the UAV performed the action efficiently and quickly, accurately completing the preset detection action. This reflects the UAV's good performance and stable system status, which is beneficial for improving overall task execution efficiency and allows it to be assigned more complex and time-critical tasks in subsequent missions. Conversely, a smaller ratio indicates a longer completion time for the detection action, suggesting lower execution efficiency for the UAV in this flight mission. This may be due to issues such as slow system response or insufficient power, requiring a comprehensive inspection of the UAV's hardware and software systems to identify and improve factors affecting execution efficiency, ensuring efficient completion of subsequent tasks.

[0089] It should be noted that the preset overlap degree Preset detection action completion rate and preset detection action completion time These are preset values, obtained based on experience, and will not be detailed here.

[0090] It should be noted that the overlap between the preset detection trajectory, the detection trajectory of this test, and the preset detection trajectory is... And the completion rate of the detection actions in this test The process of obtaining it is existing technology and will not be described in detail here.

[0091] As one embodiment of the present invention, the process of obtaining the state index of any battery module is as follows:

[0092] S10: Obtain the voltage and current of the battery module during the drone's current preset detection action through the detection data acquisition module;

[0093] S20: By analyzing the voltage and current of the battery module during the drone's execution of the preset detection action, the internal resistance deviation index of the battery module is obtained;

[0094] S30: The battery module's power consumption and percentage of power consumption during the drone's current preset detection action are obtained and analyzed through the detection data acquisition module to obtain the battery consumption deviation index.

[0095] S40: By analyzing the internal resistance deviation index and the battery consumption deviation index, the state index of the battery module is obtained.

[0096] Through the above technical solution, this embodiment first acquires the voltage and current of the battery module during the drone's current preset detection action using a detection data acquisition module; then, by analyzing the voltage and current of the battery module during the drone's current preset detection action, the internal resistance deviation index of the battery module is obtained; next, the power consumption and percentage of power consumption of the battery module during the drone's current preset detection action are acquired and analyzed using the detection data acquisition module to obtain the battery consumption deviation index; finally, by analyzing the internal resistance deviation index and the battery consumption deviation index, the state index of the battery module is obtained; and by performing the swarm operation before... Drones in normal condition are precisely screened and rationally grouped, allowing battery modules with similar condition indices to be grouped into the same module unit. During balancing adjustments, balancing strategies can be formulated based on the common characteristics of the batteries in the group. This targeted adjustment method can achieve more efficient balancing of the batteries within the battery pack. Since the health status of batteries in the same group is similar, the differences in parameters such as charge and voltage between them are relatively small. Therefore, during the balancing adjustment process, it is not necessary to frequently perform fine adjustments on each battery; only macroscopic control of the entire group is required. This greatly reduces the number of balancing adjustments and the time required, improving the overall operational efficiency of the battery management system.

[0097] As one embodiment of the present invention, in step S20, the formula is:

[0098] ;

[0099] Calculate the internal resistance deviation index of any battery module ;

[0100] in, For any battery module; where, For the second judgment function, when hour, ;when hour, ; This is the start time of the drone's current detection action; This is the end time of the drone's current detection action; The curve showing the voltage change of the battery module over time during the drone's inspection operation; The curve showing the change in current of the battery module over time during the drone's inspection operation; A preset curve showing the voltage change of the battery module over time during the drone's detection operation; The preset curve showing the change in current of the battery module over time during the drone's detection operation; This is the allowable error value for internal resistance;

[0101] Through the above technical solution, this embodiment This is the cumulative internal resistance value of the battery module during the drone's current testing operation. This represents the average internal resistance of the battery module during the drone's testing operation. This is the preset cumulative internal resistance value of the battery module during the drone's detection operation. The preset average internal resistance of the battery module during the drone's detection operation; This is the absolute value of the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's current testing operation. This is the absolute value of the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's current testing operation, and the difference between the allowable internal resistance error value; (in the formula...) In the middle, the second judgment function In It refers to , It refers to This is used to determine whether the absolute value of the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's current detection operation exceeds the allowable error value for internal resistance; when This indicates that the absolute value of the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's testing operation exceeded the allowable error value for internal resistance; therefore... The greater the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's testing operation, the greater the increase in the internal resistance of the battery module, and the worse the condition of the battery module. The internal resistance deviation index of the battery module... The larger; when If the absolute value of the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's testing operation does not exceed the allowable error value for internal resistance, the battery module is in normal condition. =0;

[0102] It should be noted that the voltage change curve of the battery module over time during this drone's testing operation is shown. The curve showing the change in current of the battery module over time during the drone's testing operation. The method of acquisition is based on existing technology and will not be detailed here; the preset voltage change curve of the battery module over time during the drone's detection operation. The preset curve of the current of the battery module over time during the drone's detection operation. This is a preset value, obtained when the battery module is brand new and in good condition; the allowable error value for internal resistance. These are preset values, obtained based on experience, and will not be detailed here.

[0103] As one embodiment of the present invention, in step S30, the formula is:

[0104] ;

[0105] Calculate the battery consumption deviation index for any battery module ;

[0106] in, This refers to the power consumption of the battery module during the drone's testing operation. This is the rated capacitance of the battery module; The preset percentage of power consumed by the battery module for the drone to perform detection actions; This is the allowable error value for power consumption;

[0107] Through the above technical solution, this embodiment The preset power consumption for performing a detection action on the battery module when it is brand new and in good condition; This is the absolute value of the difference between the power consumption of the battery module during the current test performed by the drone and the preset power consumption when the battery module is in a brand new and healthy state during the test. The absolute value of the difference between the power consumption of the battery module during this test operation and the preset power consumption when the battery module is in a brand new and healthy state, and the difference between the absolute value of the power consumption tolerance value; (in the formula...) In the middle, the second judgment function In It refers to , It refers to This is used to determine whether the absolute value of the difference between the power consumption of the battery module during the current detection action performed by the drone and the preset power consumption when the battery module is brand new and in good condition exceeds the allowable error value for power consumption; when This indicates that the absolute value of the difference between the power consumption of the battery module during this test and the preset power consumption when the battery module is brand new and in good condition exceeds the allowable error value for power consumption; therefore... The greater the power consumption of the battery module during the drone's testing operation, the worse the battery module's condition; this is indicated by the battery consumption deviation index. The larger; when If the absolute value of the difference between the power consumption of the battery module during this test and the preset power consumption when the battery module is brand new and in good condition does not exceed the allowable error value for power consumption, then the battery module is in normal condition. ;

[0108] It should be noted that the battery module consumed power during this drone's testing operation. The method of obtaining the [specification] is existing technology and will not be detailed here; the rated capacitance of the battery module... The preset percentage of power consumption of the battery module is used when the drone performs detection actions. This is a preset value obtained when the battery module is brand new and in good condition; allowable error value for power consumption. These are preset values, obtained based on experience, and will not be detailed here.

[0109] In one embodiment of the present invention, in step S40, the formula is:

[0110] ;

[0111] Calculate the state index of any battery module ;

[0112] in, This is the first weighting coefficient; This is the second weighting coefficient; This is the first preset constant; This is the second preset constant;

[0113] Through the above technical solution, the battery consumption deviation index of the battery module in this embodiment is... The larger the internal resistance deviation index, the greater the internal resistance deviation index. A higher value indicates a worse state of the battery module; therefore, the state index of the battery module... The smaller the value, the better; similarly, the smaller the battery consumption deviation index of this battery module. The smaller the value, the lower the internal resistance deviation index. The smaller the value, the better the condition of the battery module; therefore, the state index of the battery module... The larger;

[0114] It should be noted that the first weighting coefficient Second weighting coefficient First preset constant Second preset constant These are preset values, obtained based on experience, and will not be detailed here.

[0115] As one embodiment of the present invention, the preset grouping rule is as follows:

[0116] S100: The module unit spacing width is obtained by analyzing the state index of each battery module;

[0117] S200: Group battery modules according to the spacing width of module units;

[0118] Through the above technical solution, this embodiment is illustrated for ease of understanding. If the module unit spacing width is 0.05, the battery module state index is in As a group; the battery module state index is in As a group; the battery module state index is in They form a group, and so on.

[0119] As one embodiment of the present invention, in step S100, the formula is:

[0120] ;

[0121] Calculate the module unit spacing width ;

[0122] in, Preset module unit spacing width; This is the first preset constant; This represents the total number of battery modules. The average state index of all battery modules in the drone;

[0123] Through the above technical solution, this embodiment The standard deviation of the state index of all battery modules of the drone; the standard deviation of the state index of all battery modules of the drone. The larger the value, the greater the difference in the state of the various battery modules in the drone. In this case, by reducing the spacing between module units, more precise monitoring and control of the battery state can be achieved, improving the granularity of battery state management. This helps to promptly detect and address abnormal differences between battery modules, thereby extending the overall battery life and improving the safety and stability of drone operation. The smaller the value, the smaller the difference in the state of each battery module of the drone. In this case, the spacing between module units can be appropriately increased to enable broader battery status monitoring and control. Under the premise of ensuring that the battery status is basically normal, the energy consumption and computing burden of the monitoring system can be reduced, and the system operating efficiency can be improved.

[0124] It should be noted that the preset module unit spacing width and the first preset constant These are preset values, obtained based on experience, and will not be detailed here; preset module unit spacing width. The range of values ​​is Module unit spacing width The range of values ​​is ;

[0125] It should be noted that the unit information data includes the electrical parameters of the module unit (such as average voltage, average current, average power consumption, etc.). The method of generating a module unit balancing strategy based on the electrical parameters of the module unit and regulating the charging and discharging state of each module unit according to the module unit balancing strategy is the same as the method of generating a battery module balancing strategy based on the electrical parameters of the battery module and regulating the charging and discharging state of each battery module according to the battery module balancing strategy in the prior art, and will not be described in detail here.

[0126] It should be noted that, when all battery modules of the drone are in brand new and healthy condition, the performance differences between the individual battery modules are within a preset range.

[0127] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An intelligent management system for solid-state batteries in agricultural drones for swarm operations, characterized in that, The system includes: The detection module is used to perform preset detection actions on the UAV before executing cluster operations to obtain UAV detection information data; Multiple detection data acquisition modules, one-to-one with the battery module, are used to collect module detection information data of each corresponding battery module during the drone's execution of preset detection actions; The drone status assessment module is used to analyze drone detection information data to obtain the drone health index; and to determine whether the drone status is normal based on the drone health index. The battery module status assessment module is used to analyze the detection information data of each module when the drone is in normal condition, and obtain the status index of each battery module. The grouping module is used to analyze the state index of each battery module according to the preset grouping rules, and divide each battery module into several module units so that battery modules with similar state indices are grouped into the same module unit. The module unit data acquisition module is used to collect unit information data of each module unit when executing cluster operations; The analysis and control module is used to analyze the unit information data of the module unit, generate a module unit balancing strategy, and regulate the charging and discharging state of each module unit according to the module unit balancing strategy. The process of obtaining the state index of any battery module includes: S30: The battery module's power consumption and percentage of power consumption during the drone's current preset detection action are obtained and analyzed through the detection data acquisition module to obtain the battery consumption deviation index. S40: By analyzing the internal resistance deviation index and the battery consumption deviation index, the state index of the battery module is obtained. In step S40, the formula is used: ； Calculate the state index of any battery module ; in, This is the first weighting coefficient; This is the second weighting coefficient; This is the first preset constant; This is the second preset constant; The internal resistance deviation index of any battery module; This represents the battery consumption deviation index for any battery module. The preset grouping rule is as follows: S100: The module unit spacing width is obtained by analyzing the state index of each battery module; S200: Group battery modules according to the spacing width of module units; In step S100, the formula is used: ； Calculate the module unit spacing width ; in, Preset module unit spacing width; This is the first preset constant; This represents the total number of battery modules. This is the average state index of all battery modules in the drone.

2. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 1, characterized in that, The drone detection information data includes the drone's detection trajectory, detection action completion rate, and detection action completion time.

3. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 2, characterized in that, The system operates as follows: S1: Before performing cluster operations, the UAV performs preset detection actions through the detection module to obtain the UAV's detection trajectory, detection action completion rate, and detection action completion time; and the detection data acquisition module collects module detection information data of each corresponding battery module during the UAV's execution of preset detection actions. S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion rate to obtain the drone health index; and determines whether the drone's status is normal based on the drone health index. If the status is normal, proceed to step S3; Otherwise, the drone should be inspected and repaired. S3: The battery module status assessment module analyzes the detection information data of each module to obtain the status index of each battery module. S4: The state index of each battery module is analyzed by the grouping module according to the preset grouping rules, and each battery module is divided into several module units. S5: The module unit data acquisition module collects unit information data of each module unit when executing cluster operations; S6: The analysis and control module analyzes the unit information data, generates a module unit balancing strategy, and regulates the charging and discharging state of each module unit according to the module unit balancing strategy.

4. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 3, characterized in that, The module detection information data includes the voltage, current, and power consumption of the corresponding battery module.

5. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 4, characterized in that, Through the formula: ； Calculate the health index of drones ; in, As the first judgment function, when hour, ;when hour, ; The degree of overlap between the detection trajectory in this test and the preset detection trajectory; Preset overlap; This refers to the completion rate of the testing actions in this inspection. The preset detection action completion rate; This refers to the completion time of the detection action in this test; This is the preset time for the detection action to complete.

6. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 5, characterized in that, The process for determining whether a drone is in normal condition is as follows: when At that time, the drone's status was abnormal; when At that time, the drone was in normal condition.

7. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 6, characterized in that, The process of obtaining the state index of any battery module also includes performing the following steps before step S30: S10: Obtain the voltage and current of the battery module during the drone's current preset detection action through the detection data acquisition module; S20: By analyzing the voltage and current of the battery module during the drone's execution of the preset detection action, the internal resistance deviation index of the battery module is obtained.

8. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 7, characterized in that, In step S20, the formula is used: ； Calculate the internal resistance deviation index of any battery module ; in, For any battery module; where, For the second judgment function, when hour, ;when hour, ; This is the start time of the drone's current detection action; This is the end time of the drone's current detection action; The curve showing the voltage change of the battery module over time during the drone's inspection operation; The curve showing the change in current of the battery module over time during the drone's inspection operation; A preset curve showing the voltage change of the battery module over time during the drone's detection operation; The preset curve showing the change in current of the battery module over time during the drone's detection operation; This is the allowable error value for internal resistance.

9. The intelligent management system for solid-state batteries of agricultural drones for swarm operations according to claim 8, characterized in that, In step S30, the formula is used: ； Calculate the battery consumption deviation index for any battery module ; in, This refers to the power consumption of the battery module during the drone's testing operation. This is the rated capacitance of the battery module; The preset percentage of power consumed by the battery module for the drone to perform detection actions; This represents the allowable error value for power consumption.

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