Agricultural unmanned aerial vehicle solid-state battery intelligent management system for cluster operation

Abstract

The invention relates to the technical field of unmanned aerial vehicle battery management, and discloses an agricultural unmanned aerial vehicle solid-state battery intelligent management system for cluster operation, which comprises a detection module, a detection data acquisition module, an unmanned aerial vehicle state evaluation module, a battery module state evaluation module, a grouping module, a module unit data acquisition module and an analysis control module, the detection module obtains unmanned aerial vehicle detection information data; the detection data acquisition module acquires module detection information data; the unmanned aerial vehicle state evaluation module judges whether the unmanned aerial vehicle is normal; the battery module state evaluation module obtains a state index of the battery module; the grouping module divides each battery module into a plurality of module units; the module unit data acquisition module acquires unit information data; the analysis control module regulates and controls the charging and discharging state of each module unit; therefore, the battery modules with similar state indexes are classified into the same module unit, the frequency and time of equalization adjustment are greatly reduced, and the operation efficiency of the whole battery management system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drone battery management, and in particular to an intelligent management system for solid-state batteries of agricultural drones for cluster operations. Background Art

[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 threshold of low-altitude flight, but also expanded the application boundaries of the low-altitude economy through features such as intelligence, clustering, and long flight time. Especially in the agricultural field, drones have been deeply integrated into the entire agricultural production cycle from sowing to harvesting, playing an indispensable and key role.

[0003] In drone swarm operation scenarios, the battery management system is a core component that ensures the continuous and stable operation of drones. Its performance directly affects the efficiency and safety of swarm operations. Traditional drone battery management solutions generally adopt a centralized balancing strategy, that is, unified charging and discharging regulation of all battery cells.

[0004] However, as the scale of drone clusters continues to expand, the number of batteries is increasing exponentially. The practice of independently monitoring and regulating each battery cell has caused the system's computational complexity to increase sharply and the efficiency of balancing regulation to drop significantly, making it difficult to meet the stringent real-time requirements of large-scale cluster operations. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent management system for solid-state batteries of agricultural drones for cluster operations to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent management system for solid-state batteries of agricultural drones for swarm operations, comprising:

[0008] The detection module is used to enable the drone to perform preset detection actions and obtain drone detection information data before performing cluster operations;

[0009] Multiple detection data collection modules, corresponding one to one with the battery modules, are used to collect module detection information data of each corresponding battery module when the drone performs a preset detection action;

[0010] The drone status assessment module is used to analyze the 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] A grouping module is used to analyze the status index of each battery module according to a preset grouping rule and divide each battery module into a number of module units;

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

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

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

[0016] As a further solution of the present invention: the working process of the system is:

[0017] S1: Before executing the cluster operation, the drone performs a preset detection action through the detection module to obtain the drone's detection trajectory, detection action completion degree, and detection action completion time; and the detection data collection module collects module detection information data of each corresponding battery module during the drone's execution of the preset detection action;

[0018] S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion to obtain a drone health index. The drone health index is then used to determine whether the drone is in a normal state. If the state is normal, the process proceeds to step S3; otherwise, the drone is repaired.

[0019] S3: Analyze the detection information data of each module through the battery module status evaluation module to obtain the status index of each battery module;

[0020] S4: Analyzing the status index of each battery module according to a preset grouping rule through the grouping module, and dividing each battery module into a plurality of module units;

[0021] S5: Collecting unit information data of each module unit through the module unit data collection module when executing cluster operations;

[0022] S6: Analyze the unit information data through the analysis control module, generate a module unit balancing strategy, and regulate the charge and discharge state of each module unit according to the module unit balancing strategy.

[0023] As a further solution 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 solution of the present invention: by formula:

[0025] ;

[0026] Calculating the Drone Health Index ;

[0027] in, is the first judgment function, when hour, ;when hour, ; The overlap between the detection trajectory of this detection and the preset detection trajectory; is the preset overlap; The completion degree of the detection action of this test; To preset the completion of the detection action; The completion time of the detection action of this detection; The preset detection action completion time.

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

[0029] when When the drone is in abnormal state;

[0030] when The drone is in normal status.

[0031] As a further solution of the present invention: the process of 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 execution of the preset detection action through the detection data acquisition module;

[0033] S20: Analyzing the voltage and current of the battery module during the drone's execution of the preset detection action to obtain an internal resistance deviation index of the battery module;

[0034] S30: Obtaining the power consumption and power consumption percentage of the battery module during the drone's execution of the preset detection action through the detection data acquisition module, and analyzing them to obtain a battery consumption deviation index;

[0035] S40: Obtaining a state index of the battery module by analyzing the internal resistance deviation index and the battery consumption deviation index.

[0036] As a further solution of the present invention: in step S20, by formula:

[0037] ;

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

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

[0040] As a further solution of the present invention: in step S30, by formula:

[0041] ;

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

[0043] in, The power consumption of the battery module when the drone performs the detection action this time; is the rated capacitance of the battery module; The preset percentage of power consumption of the battery module when the drone performs the detection action; It is the allowable error value of power consumption.

[0044] As a further solution of the present invention: in step S40, by formula:

[0045] ;

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

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

[0048] As a further solution of the present invention: the preset grouping rule is:

[0049] S100: Analyzing the status index of each battery module to obtain the module unit interval width;

[0050] S200: Grouping battery modules according to module unit interval widths.

[0051] As a further solution of the present invention: in step S100, by formula:

[0052] ;

[0053] Calculate module unit spacing width ;

[0054] in, The preset module unit spacing width; Preset constant for number one; is the total number of battery modules, It is the average status index of all battery modules of the drone.

[0055] Beneficial effects of the present invention:

[0056] The present invention uses a detection module to obtain drone detection information data before executing cluster operations. The detection data acquisition module collects module detection information data of each corresponding battery module during the drone's execution of the preset detection action. The drone status evaluation module analyzes the drone detection information data to obtain the drone health index. The drone health index is used to determine whether the drone status is normal. When the drone status is normal, the battery module status evaluation module analyzes the module detection information data to obtain the status index of each battery module. The grouping module groups each battery module into several groups according to the preset grouping rules. module units; then, the module unit data acquisition module collects the unit information data of each module unit when executing cluster operations; finally, the analysis and control module analyzes the unit information data, generates a module unit balancing strategy, and regulates the charge and discharge status of each module unit according to the module unit balancing strategy; so that battery modules with similar state indexes are classified into the same module unit, and when performing balancing adjustment, a balancing strategy can be formulated according to the common characteristics of the group of batteries; there is no need to frequently make fine adjustments to each battery, and only macro-control of the entire group is required; this greatly reduces the number and time of balancing adjustments, and improves the operating efficiency of the entire battery management system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0058] Figure 1 This is a system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] See also Figure 1 As shown, in one embodiment, an intelligent management system for solid-state batteries of agricultural drones for cluster operations is provided, which is applicable to solid-state batteries. The solid-state batteries include a plurality of battery modules, which are sequentially connected in series. The battery modules include a plurality of single cells, which are connected in parallel. The system includes:

[0061] The detection module is used to enable the drone to perform preset detection actions and obtain drone detection information data before performing cluster operations;

[0062] Multiple detection data collection modules, corresponding one to one with the battery modules, are used to collect module detection information data of each corresponding battery module when the drone performs a preset detection action;

[0063] The drone status assessment module is used to analyze the 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] A grouping module is used to analyze the status index of each battery module according to a preset grouping rule and divide each battery module into a number of module units;

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

[0067] An analysis and control module is used to analyze the unit information data, generate a module unit balancing strategy, and regulate the charge and discharge status of each module unit according to the module unit balancing strategy;

[0068] Through the above technical solution, this embodiment uses the detection module to obtain the drone detection information data by executing the preset detection action before executing the cluster operation; the detection information data of each corresponding battery module in the preset detection action of the drone is collected through each detection data acquisition module; the drone status evaluation module analyzes the drone detection information data to obtain the drone health index; and judges whether the drone status is normal based on the drone health index; when the drone status is normal, the battery module status evaluation module analyzes the detection information data of each module to obtain the status index of each battery module; the grouping module divides each battery module into several module units according to the preset grouping rules; and then the module unit data acquisition module collects the unit information data of each module unit when executing the cluster operation; finally, the analysis and control module analyzes the unit information data, generates a module unit balancing strategy and regulates the charge and discharge 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, so that the operator or the system can A clear understanding of the performance level of each battery module facilitates timely implementation of appropriate measures. This effectively avoids operational interruptions or failures caused by potential battery module problems, ensuring the initial stability of cluster operations. Battery modules are grouped into several modules based on their status index, allowing modules with similar status indices to be grouped together. During balancing adjustments, balancing strategies can be developed based on the common characteristics of the battery group. 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 speed up charging. For a group of batteries with slightly worse health and higher internal resistance, the charging current can be reduced to avoid damage from overcharging. This targeted adjustment method can more efficiently achieve balancing of each battery within the battery pack. Because the health status of batteries in the same group is similar, the differences in parameters such as power and voltage between them are relatively small. Therefore, during the balancing adjustment process, there is no need for frequent fine-tuning of each battery, only macro-control of the entire group is required. This greatly reduces the number and time of balancing adjustments and improves the operating efficiency of the entire battery management system.

[0069] As an embodiment of the present invention, the drone detection information data includes the drone's detection trajectory, detection action completion degree, and detection action completion time;

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

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

[0072] S1: Before executing the cluster operation, the drone performs a preset detection action through the detection module to obtain the drone's detection trajectory, detection action completion degree, and detection action completion time; and the detection data collection module collects module detection information data of each corresponding battery module during the drone's execution of the preset detection action;

[0073] S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion to obtain a drone health index. The drone health index is then used to determine whether the drone is in a normal state. If the state is normal, the process proceeds to step S3; otherwise, the drone is repaired.

[0074] S3: Analyze the detection information data of each module through the battery module status evaluation module to obtain the status index of each battery module;

[0075] S4: Analyzing the status index of each battery module according to a preset grouping rule through the grouping module, and dividing each battery module into a plurality of module units;

[0076] S5: Collecting unit information data of each module unit through the module unit data collection module when executing cluster operations;

[0077] S6: Analyze the unit information data through the analysis control module, generate a module unit balancing strategy, and regulate the charge and discharge state of each module unit according to the module unit balancing strategy;

[0078] Through the above technical solution, this embodiment first uses the detection module to perform a preset detection action before performing a cluster operation, and obtains the detection trajectory, detection action completion degree and detection action completion time of the drone; and uses the detection data acquisition module to collect the module detection information data of each corresponding battery module when the drone performs the preset detection action; then the drone status evaluation module analyzes the detection trajectory and detection action completion degree of the drone to obtain the drone health index; and judges whether the drone status is normal based on the drone health index; if the status is normal, the battery module status evaluation module analyzes the detection information data of each module to obtain the health index of each battery module. state index; then the grouping module analyzes the state index of each battery module according to the preset grouping rules, and divides each battery module into several module units; then, the module unit data acquisition module collects the unit information data of each module unit when executing cluster operations; finally, the analysis and control module analyzes the unit information data, generates the module unit balancing strategy and adjusts the charge and discharge state of each module unit according to the module unit balancing strategy; during the balancing adjustment process, there is no need to frequently make fine adjustments to each battery, only macro-control of the entire group is required; this greatly reduces the number and time of balancing adjustments, and improves the operating efficiency of the entire battery management system.

[0079] As an 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 method for obtaining the voltage, current and power consumption of each battery module is an existing technology and will not be described in detail here.

[0081] As an embodiment of the present invention, by formula:

[0082] ;

[0083] Calculating the Drone Health Index ;

[0084] in, is the first judgment function, when hour, ;when hour, ; The overlap between the detection trajectory of this detection and the preset detection trajectory; is the preset overlap; The completion degree of the detection action of this test; To preset the completion of the detection action; The completion time of the detection action of this detection; The preset detection action completion time;

[0085] The process of judging whether the drone status is normal is as follows:

[0086] when When the drone is in abnormal state;

[0087] when When , the drone is in normal state;

[0088] Through the above technical solution, this embodiment is the difference between the overlap between the detection trajectory of this detection and the preset detection trajectory and the preset overlap; in the formula In the first judgment function in Refers to , used to determine whether the overlap between the detection trajectory of this detection and the preset detection trajectory exceeds the preset overlap; when When , it means that the detection trajectory of this detection has a high degree of overlap with the preset detection trajectory. The drone performs well in executing the preset action in this flight mission and can fly accurately according to the established planned route, meeting the mission execution requirements for trajectory accuracy. ;when , it means that the detection trajectory of this detection has a low degree of overlap with the preset detection trajectory. The drone has performed poorly on the preset action in this flight mission, and has a large deviation from the established planned route, which cannot meet the mission execution requirements for trajectory accuracy. ; is the difference between the completion degree of the detection action of this detection and the completion degree of the preset detection action; in the formula In the first judgment function in Refers to , used to determine whether the completion degree of the detection action of this detection exceeds the preset detection action completion degree; when When , it means that the completion degree of the detection action of this test is high. The drone performs well in executing the preset action in this flight mission and can execute the action accurately according to the established action, meeting the requirements of the task execution for action accuracy. ;when , it means that the completion degree of the detection action of this test is low. The drone performs poorly on the preset action in this flight mission and cannot accurately execute the established action. It cannot meet the requirements of the task execution for action accuracy. ; It is the time ratio of the preset detection action completion time to the detection action completion time of this detection. The larger the time ratio, the shorter the detection action completion time of this detection, indicating that the drone has a high action execution efficiency in this detection and can complete the preset detection action quickly and accurately. This reflects the good performance and stable system status of the drone, which is conducive to improving the overall task execution efficiency. More complex and time-sensitive tasks can be assigned in subsequent task arrangements. On the contrary, if the time ratio is small, it means that the detection action completion time of this detection is long, indicating that the drone has a low action execution efficiency in this flight mission. There may be problems such as slow system response and insufficient power. It is necessary to conduct a comprehensive inspection of the drone's hardware and software systems to find out the factors affecting the action execution efficiency and improve them to ensure that subsequent tasks can be completed efficiently.

[0089] It should be noted that the preset overlap , preset detection action completion and preset detection action completion time It is a preset value, obtained based on experience, and will not be described in detail here;

[0090] It should be noted that the overlap between the preset detection trajectory, the detection trajectory of this detection and the preset detection trajectory And the completion degree of the detection action of this test The process of obtaining is prior art and will not be described in detail here.

[0091] As an 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 execution of the preset detection action through the detection data acquisition module;

[0093] S20: Analyzing the voltage and current of the battery module during the drone's execution of the preset detection action to obtain an internal resistance deviation index of the battery module;

[0094] S30: Obtaining the power consumption and power consumption percentage of the battery module during the drone's execution of the preset detection action through the detection data acquisition module, and analyzing them to obtain a battery consumption deviation index;

[0095] S40: Obtaining a status index of the battery module by analyzing the internal resistance deviation index and the battery consumption deviation index;

[0096] Through the above technical solution, this embodiment first obtains the voltage and current of the battery module during the drone's preset detection action through the detection data acquisition module; then, by analyzing the voltage and current of the battery module during the drone's preset detection action, the internal resistance deviation index of the battery module is obtained; then, by analyzing the power consumption and power consumption percentage of the battery module during the drone's preset detection action, the battery consumption deviation index is obtained; finally, by analyzing the internal resistance deviation index and the battery consumption deviation index, the state index of the battery module is obtained; by performing the cluster operation before performing the cluster operation, the battery module status index is obtained. UAVs in normal condition are accurately screened and reasonably grouped, so that battery modules with similar status indexes are classified into the same module unit. When performing balancing adjustments, balancing strategies can be formulated based on the common characteristics of this group of batteries. This targeted adjustment method can more efficiently achieve the balance of each battery in the battery pack. Since the health status of batteries in the same group is similar, the differences in parameters such as power and voltage between them are relatively small. Therefore, during the balancing adjustment process, there is no need to frequently make fine adjustments to each battery, only macro-control of the entire group is required. This greatly reduces the number and time of balancing adjustments and improves the operating efficiency of the entire battery management system.

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

[0098] ;

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

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

[0101] Through the above technical solution, this embodiment The accumulated internal resistance of the battery module during the drone's current detection process. The average internal resistance of the battery module during the drone's detection process. 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; The absolute value of the difference between the average internal resistance of the battery module during the drone's current detection operation and the preset average internal resistance; The absolute value of the difference between the average internal resistance of the battery module during the drone's detection process and the preset average internal resistance and the internal resistance allowable error value; in the formula In the second judgment function in Refers to , Refers to , 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 detection action exceeds the internal resistance allowable error value; when When , it means 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 detection action exceeds the internal resistance allowable error value; therefore The greater the difference between the average internal resistance of the battery module and the preset average internal resistance during the drone's detection operation, the greater the increase in the internal resistance of the battery module, the worse the state of the battery module, and the internal resistance deviation index of the battery module. The bigger; when , it means 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 detection action does not exceed the internal resistance allowable error value; the battery module is in normal condition. =0;

[0102] It should be noted that the voltage of the battery module changes with time during the drone's detection operation. The curve of the battery module's current changing over time during the drone's detection operation The acquisition method is the existing technology and will not be described in detail here; the voltage of the battery module changes with time during the drone's detection action. The preset change curve of the battery module's current over time during the drone's detection action The default value is obtained when the battery module is new and healthy. The internal resistance tolerance is It is a preset value obtained based on experience and will not be described in detail here.

[0103] As an embodiment of the present invention, in step S30, by formula:

[0104] ;

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

[0106] in, The power consumption of the battery module when the drone performs the detection action this time; is the rated capacitance of the battery module; The preset percentage of power consumption of the battery module when the drone performs the detection action; is the allowable error value of power consumption;

[0107] Through the above technical solution, this embodiment The preset power consumption for performing the detection action when the battery module is in a new and healthy state; The absolute value of the difference between the power consumption of the battery module when the drone performs the detection action and the preset power consumption of the battery module when performing the detection action in a new and healthy state; The difference between the absolute value of the difference between the power consumption of the battery module when the drone performs the detection action this time and the preset power consumption of the battery module when performing the detection action in a new and healthy state and the allowable error value of power consumption; in the formula In the second judgment function in Refers to , Refers to , used to determine whether the absolute value of the difference between the power consumption of the battery module when the drone performs the detection action this time and the preset power consumption of the battery module when performing the detection action in a new and healthy state exceeds the allowable error value of power consumption; when When , it means that the absolute value of the difference between the power consumption of the battery module when the drone performs the detection action this time and the preset power consumption of the battery module when performing the detection action in a new and healthy state exceeds the allowable error value of power consumption; therefore The greater the power consumption of the battery module during the drone's detection action, the worse the battery module status is, and the battery consumption deviation index is. The bigger; when When the battery module is in a normal state, the absolute value of the difference between the power consumption of the battery module when the drone performs the detection action and the preset power consumption of the battery module when performing the detection action in a new and healthy state does not exceed the allowable error value of power consumption; the battery module is in normal state. ;

[0108] It should be noted that the battery module consumes the power of the drone during the detection operation. The method of obtaining is the existing technology and will not be described in detail here; the rated capacitance of the battery module The preset power consumption percentage of the battery module when the drone performs the detection action The default value is obtained when the battery module is new and healthy; the allowable error value of power consumption It is a preset value obtained based on experience and will not be described in detail here.

[0109] As an embodiment of the present invention, in step S40, by formula:

[0110] ;

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

[0112] in, is the first weight coefficient; is the second weight coefficient; is the first preset constant; 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 larger the value, the worse the battery module condition. Therefore, the battery module condition index The smaller the value, the smaller the battery consumption deviation index of the battery module. The smaller the internal resistance deviation index The smaller the battery module is, the better the condition of the battery module is. The bigger;

[0114] It should be noted that the first weight coefficient , the second weight coefficient , the first preset constant and the second preset constant It is a preset value obtained based on experience and will not be described in detail here.

[0115] As an implementation manner of the present invention, the preset grouping rule is:

[0116] S100: Analyzing the status index of each battery module to obtain the module unit interval width;

[0117] S200: Grouping battery modules according to module unit interval width;

[0118] Through the above technical solution, this embodiment is explained by example for the convenience of understanding. If the module unit interval width is 0.05, the battery module status index is As a group; the battery module status index is As a group; the battery module status index is as a group, and so on.

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

[0120] ;

[0121] Calculate module unit spacing width ;

[0122] in, The preset module unit spacing width; Preset constant for number one; is the total number of battery modules, The average status index of all battery modules of the drone;

[0123] Through the above technical solution, this embodiment is the standard deviation of the status index of all battery modules of the drone; is the standard deviation of the status index of all battery modules of the drone The larger the value, the greater the difference in the status of the drone's battery modules. In this case, by reducing the width of the module unit interval, more accurate monitoring and control of the battery status can be achieved, which improves the precision of battery status management and helps to promptly detect and handle abnormal differences between battery modules, thereby extending the overall battery life and improving the safety and stability of drone operation. The standard deviation of the status index of all drone battery modules The smaller the value, the smaller the status difference of each battery module of the drone. In this case, the module unit interval width can be appropriately increased to carry out more extensive battery status monitoring and regulation. On the premise of ensuring that the battery status is basically normal, the energy consumption and computing burden of the monitoring system can be reduced, thereby improving the system operation efficiency.

[0124] It should be noted that the preset module unit interval width and preset constant No. It is a preset value, obtained based on experience, and will not be detailed here; the preset module unit interval width The value range is ; Module unit spacing width The value range 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 value, etc.). Generating a module unit balancing strategy based on the electrical parameters of the module unit and regulating the charge and discharge status of each module unit according to the module unit balancing strategy is the same as the method in the prior art of generating a battery module balancing strategy based on the electrical parameters of the battery module and regulating the charge and discharge status of each battery module according to the battery module balancing strategy, and will not be described in detail here.

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

[0127] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent management system for solid-state batteries of agricultural drones for cluster operations, characterized by: The system comprises: The detection module is used to enable the drone to perform preset detection actions and obtain drone detection information data before performing cluster operations; Multiple detection data collection modules, corresponding one to one with the battery modules, are used to collect module detection information data of each corresponding battery module when the drone performs a preset detection action; The drone status assessment module is used to analyze the 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; A grouping module is used to analyze the status index of each battery module according to a preset grouping rule and divide each battery module into a number of module units; The module unit data collection 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, generate a module unit balancing strategy and regulate the charge and discharge status of each module unit according to the module unit balancing strategy.

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

3. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 2 is characterized in that: The working process of the system is: S1: Before executing the cluster operation, the drone performs a preset detection action through the detection module to obtain the drone's detection trajectory, detection action completion degree, and detection action completion time; and the detection data collection module collects module detection information data of each corresponding battery module during the drone's execution of the preset detection action; S2: The drone status assessment module analyzes the drone's detection trajectory and detection action completion to obtain the drone health index. The drone health index is then used to determine whether the drone is in a normal state. If the status is normal, go to step S3; Otherwise, the drone will be overhauled; S3: Analyze the detection information data of each module through the battery module status evaluation module to obtain the status index of each battery module; S4: Analyzing the status index of each battery module according to a preset grouping rule through the grouping module, and dividing each battery module into a plurality of module units; S5: Collecting unit information data of each module unit through the module unit data collection module when executing cluster operations; S6: Analyze the unit information data through the analysis control module, generate a module unit balancing strategy, and regulate the charge and discharge 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 cluster operations according to claim 3 is 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 cluster operations according to claim 4 is characterized in that: By formula: ； Calculating the Drone Health Index ; in, is the first judgment function, when hour, ;when hour, ; The overlap between the detection trajectory of this detection and the preset detection trajectory; is the preset overlap; The completion degree of the detection action of this test; To preset the completion of the detection action; The completion time of the detection action of this detection; The preset detection action completion time.

6. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 5, characterized in that: The process of judging whether the drone status is normal is as follows: when When the drone is in abnormal state; when The drone is in normal status.

7. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 6, characterized in that: The process of obtaining the status index of any battery module is as follows: S10: Obtain the voltage and current of the battery module during the drone's execution of the preset detection action through the detection data acquisition module; S20: Analyzing the voltage and current of the battery module during the drone's execution of the preset detection action to obtain an internal resistance deviation index of the battery module; S30: Obtaining the power consumption and power consumption percentage of the battery module during the drone's execution of the preset detection action through the detection data acquisition module, and analyzing them to obtain a battery consumption deviation index; S40: Obtaining a state index of the battery module by analyzing the internal resistance deviation index and the battery consumption deviation index.

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

9. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 8, characterized in that: In step S30, by formula: ； Calculate the battery consumption deviation index of any battery module ; in, The power consumption of the battery module when the drone performs the detection action this time; is the rated capacitance of the battery module; The preset percentage of power consumption of the battery module when the drone performs the detection action; It is the allowable error value of power consumption.

10. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 9, characterized in that: In step S40, by formula: ； Calculate the status index of any battery module ; in, is the first weight coefficient; is the second weight coefficient; is the first preset constant; is the second preset constant.

11. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 10, characterized in that: The preset grouping rules are: S100: Analyzing the status index of each battery module to obtain the module unit interval width; S200: Grouping battery modules according to module unit interval widths.

12. The intelligent management system for solid-state batteries of agricultural drones for cluster operations according to claim 11, characterized in that: In step S100, by formula: ； Calculate module unit spacing width ; in, The preset module unit spacing width; Preset constant for number one; is the total number of battery modules, It is the average status index of all battery modules of the drone.

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