Instruction execution decision control system adaptive to energy storage type robot

Abstract

The invention belongs to the technical field of robot instruction execution decision control, and particularly discloses an instruction execution decision control system adaptive to an energy storage type robot. The system comprises a machine instruction receiving module, a machine instruction analysis module, a robot state monitoring module, a robot instruction confirmation module, a robot execution monitoring module, a robot execution confirmation module, a database and a robot feedback terminal. According to the method, the state information of each robot is considered, instruction confirmation of each to-be-executed instruction is carried out, the effectiveness of instruction execution of the energy storage robot is improved, the reliability of instruction execution is guaranteed, meanwhile, according to the instruction information of each to-be-executed instruction and the state information of each robot, instruction confirmation of each to-be-executed instruction is carried out, and the reliability of the energy storage robot is improved. Therefore, the execution efficiency of the overall instruction execution is improved, and the efficiency of the instruction execution of the energy storage robot is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot instruction execution decision control, and relates to an instruction execution decision control system suitable for an energy storage robot. Background Art

[0002] The command execution decision control adapted to energy storage robots is a mechanism that determines the action plan based on comprehensive judgments of multiple factors such as its own energy storage status, task priority, and external environment when the robot performs actions according to the received commands.

[0003] For example, the Chinese patent with publication number CN116257288A discloses a method, device, equipment and medium for executing instructions. The disclosed embodiment relates to a method, device, equipment and medium for executing instructions, wherein the method comprises: in response to receiving the latest control instruction, judging whether the preset instruction execution condition is met according to the situation of the currently received control instruction to be executed; if the instruction execution condition is not met, waiting to receive the next control instruction, and executing all the currently received control instructions to be executed if the next control instruction is not received within the specified time; if the instruction execution condition is met, executing part or all of the currently received control instructions to be executed. The above method can effectively improve the user's device usage experience.

[0004] The above existing technologies have the following deficiencies: 1. Currently, execution confirmation is only performed based on the preset instruction execution conditions of the instructions to be executed, that is, the state of the energy storage robot executing the instructions is not taken into consideration, and the effectiveness of the energy storage robot's instruction execution cannot be guaranteed, thereby failing to guarantee the reliability of instruction execution, and at the same time resulting in a waste of time and resources, reducing the execution efficiency of instructions.

[0005] 2. When the energy storage robot's instructions are currently executed, no further energy storage robot selection analysis is performed based on the instruction information of each instruction, which reduces the overall execution efficiency of the instructions and the efficiency of the energy storage machine's instruction execution.

[0006] 3. The current analysis of energy storage robots mainly focuses on the execution process of instructions, that is, there is a lack of in-depth analysis of the completion qualification index after the execution of instructions, which makes it impossible to comprehensively evaluate the task execution effect of the energy storage robot. At the same time, it is impossible to timely discover potential problems in the energy storage robot during the execution of instructions, thus affecting the reliability and stability of the energy storage robot's instruction execution. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an instruction execution decision control system suitable for an energy storage robot is proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an instruction execution decision control system suitable for an energy storage robot, including: a machine instruction receiving module, used to receive various instructions to be executed sent by a control panel.

[0009] The machine instruction analysis module is used to perform instruction analysis on each instruction to be executed according to each instruction to be executed sent by the control panel, and obtain instruction information of each instruction to be executed.

[0010] The robot status monitoring module is used to monitor the status information of each robot, and the status information includes operation status information and energy storage status information.

[0011] The robot instruction analysis module is used to confirm the instructions to be executed according to the instruction information of each instruction to be executed and the status information of each robot, and obtain the execution robot of each instruction to be executed.

[0012] The robot execution monitoring module is used to record the execution robot of each instruction to be executed as each execution robot, and monitor the execution information of each execution robot when executing the instruction to be controlled, and the execution information includes temperature information, position information and operation information.

[0013] The robot execution confirmation module is used to confirm the execution qualification index of each execution robot based on the execution information when each execution robot executes the control instruction.

[0014] A database is used to store a map of the robot's working area and store the maximum allowable temperature of the robot's motors.

[0015] The robot feedback terminal is used to provide corresponding feedback based on the execution qualification index of each robot.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention considers the status information of each robot to confirm the instructions to be executed, thereby avoiding the current deficiency of not considering the status of the energy storage robot when executing instructions, improving the effectiveness of the energy storage robot's instruction execution, thereby ensuring the reliability of instruction execution, and avoiding the waste of time and resources, thereby improving the execution efficiency of instructions.

[0017] (2) The present invention performs instruction confirmation for each instruction to be executed based on the instruction information of each instruction to be executed and the status information of each robot, and then obtains the execution robot of each instruction to be executed, thereby improving the execution efficiency of the overall instruction execution. At the same time, through in-depth analysis of the instruction information, the execution order of the instructions is optimized, thereby improving the efficiency of the energy storage robot's instruction execution.

[0018] (3) The present invention confirms the execution qualification index of each executing robot according to the execution information of each robot when executing the control instruction, thus breaking the deficiency of lack of in-depth analysis of the completion qualification index after the current instruction execution, and more comprehensively evaluating the instruction execution effect of the energy storage robot. At the same time, it improves the timeliness of discovering potential problems of the energy storage robot in the process of executing the task, thereby improving the reliability and stability of the energy storage robot's instruction execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0021] Figure 2 It is a schematic diagram of the connection of the instruction confirmation steps of each instruction to be executed in the present invention.

[0022] Figure 3 It is a connection diagram of the execution qualification index confirmation step of each robot of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides an instruction execution decision-making control system suitable for an energy storage robot, the system comprising: a machine instruction receiving module, a machine instruction analysis module, a robot state monitoring module, a robot instruction confirmation module, a robot execution monitoring module, a robot execution confirmation module, a database and a robot feedback terminal.

[0025] In the above, the machine instruction analysis module is respectively connected to the machine instruction receiving module and the robot instruction confirmation module, the robot instruction confirmation module is respectively connected to the robot status monitoring module, the database and the robot execution monitoring module, and the robot execution confirmation module is also respectively connected to the robot execution monitoring module and the robot feedback terminal.

[0026] The machine instruction receiving module is used to receive each instruction to be executed sent by the control panel.

[0027] The machine instruction analysis module is used to perform instruction analysis on each instruction to be executed according to each instruction to be executed sent by the control panel, and obtain instruction information of each instruction to be executed.

[0028] Exemplarily, the instruction analysis of each to-be-executed instruction includes: matching and comparing each to-be-executed instruction sent by the control panel with instructions of corresponding priority scores of each pre-set execution control type, to obtain instruction scores of each execution control type in each to-be-executed instruction sent by the control panel, wherein the instruction control types include safety-related instructions, critical operation instructions, and routine task instructions.

[0029] The priority scores of security-related instructions, key operation instructions, and routine task instructions in each pending instruction sent by the control panel are denoted as ρ i , ρ′ i and ρ″ i , i is the number of the instruction to be executed, i=1,2,......,m.

[0030] The comprehensive priority score δ of each pending instruction sent by the statistical control panel i , δ i =ρ i *q1+ρ′ i *q2+ρ″ i *q3, q1+q2+q3=1, q1, q2 and q3 are the weights of the set safety-related instruction priority scores, key operation instruction priority scores and routine task instruction priority scores respectively, q1+q2+q3=1, q1>q2>q3.

[0031] It should be added that safety-related instructions are directly related to the life safety of personnel and the safety of equipment and property. If safety-related instructions are not executed as a priority, serious accidents may occur. Key operation instructions are usually the key steps to complete the task and play an important role in the success of the task. If key operation instructions cannot be executed in time, task delays may occur. However, the execution of safety-related instructions is a key factor in ensuring the stable operation of the entire system. Therefore, the weight of the priority score of safety-related instructions is set to be greater than the weight of the priority score of key operation instructions. Safety routine task instructions are usually some daily and repetitive tasks, which have a relatively small impact on the overall task. Therefore, q1>q2>q3 is set. For the convenience of analysis, q1 can be specifically taken as 0.5, q2 can be specifically taken as 0.3, and q3 can be specifically taken as 0.2.

[0032] In a specific embodiment, if a control panel sends an instruction that is both a safety-related instruction and a critical operation instruction and a regular task instruction, for example, the safety-related instruction priority score is 95 points, the critical operation instruction priority score is 80 points, and the regular task instruction priority score is 70 points, the weight of the safety type priority score is 0.5, the weight of the emergency type priority score is 0.3, and the weight of the environment type priority score is 0.2.

[0033] The comprehensive priority score of the command sent by the control panel is 95×0.5+80×0.3+70×0.2=47.5+24+14=85.5 points.

[0034] The to-be-executed instructions sent by the control panel are sorted according to their comprehensive priority scores, and the sorted to-be-executed instructions are used as instruction information of the to-be-executed instructions.

[0035] The robot status monitoring module is used to monitor the status information of each robot, and the status information includes operation status information and energy storage status information.

[0036] It should be supplemented that the operation status information includes: the operation type of each robot and the position information of each standby robot.

[0037] It should be supplemented that the operation types include: running and standby.

[0038] It should be supplemented that the position information of each standby robot includes: the position coordinates of each standby robot.

[0039] It should also be added that the position coordinates of each standby robot are obtained by positioning using a global positioning system installed inside the robot.

[0040] The robot instruction confirmation module is used to confirm the instructions to be executed according to the instruction information of each instruction to be executed and the status information of each robot, and obtain the execution robot of each instruction to be executed.

[0041] See also Figure 2 As shown, exemplarily, the instruction confirmation of each instruction to be executed includes: G1, extracting the first-ranked instruction to be executed from the instruction information of each instruction to be executed, and recording it as the first instruction to be executed, and then extracting the position requirement information, motion requirement information and energy storage requirement information of the first instruction to be executed.

[0042] G2. Extract the operation type of each robot from the operation status information of each robot. If the operation status of the robot is a pending instruction operation, it is recorded as a standby robot.

[0043] G3. Extract the position information of each standby robot from the running status information of each standby robot, and then calculate the path compliance of each standby robot j is the standby robot number, j=1,2,......,n.

[0044] Furthermore, the counting of the path conformity of each standby robot includes: G3-1, extracting the position coordinates of the first instruction to be executed from the position requirement information of the first instruction to be executed.

[0045] G3-2. Extract the position coordinates of each standby robot from the position information of each standby robot.

[0046] G3-3, extract the robot work area map from the database, and then locate the shortest path between the first instruction to be executed and each standby robot, denoted as d j .

[0047] G3-4. Count the path compliance of each standby robot D is the distance threshold for setting the reference.

[0048] It should be added that D is extracted from the instruction execution decision control technical specification table of the energy storage robot.

[0049] In a specific embodiment, assuming that the area to which the first instruction to be executed belongs is a rectangle, the coordinates of the lower left corner of the position coordinate of the first instruction to be executed are (10, 10), and the coordinates of the upper right corner are (20, 20).

[0050] There are three standby robots. The position coordinates of robot A1 are (8,8), the position coordinates of robot A2 are (15,15), and the position coordinates of robot A3 are (25,25).

[0051] There are obstacles distributed in the working environment, and the corresponding positions on the robot's working area map are marked as obstacles.

[0052] Set the distance threshold D to 5.

[0053] The A* algorithm is used to search for the shortest path on the map. Assuming that there are obstacles, the shortest path distance Then the path compliance of robot A1 is calculated

[0054] The A* algorithm is used to search for the shortest path on the map. Assuming that there are obstacles, the shortest path distance Then count the path compliance of robot A2

[0055] The A* algorithm is used to search for the shortest path on the map. Assume that due to obstacles, the shortest path distance is d.A3 =6, and then calculate the path compliance of robot A3

[0056] G4. Extract the motion state information of each standby robot from the energy storage state information of each standby robot, and then calculate the motion compliance of each standby robot

[0057] Furthermore, the counting of motion compliance of each standby robot includes: G4-1, extracting a required speed of the first instruction to be executed from the motion requirement information of the first instruction to be executed, and recording it as v0.

[0058] G4-2. Extract the maximum speed of each standby robot from the motion state information of each standby robot and record it as v j .

[0059] G4-3. Count the speed compliance of each standby robot ε j , Δv is the speed difference of the set reference.

[0060] It should be added that Δv is extracted from the instruction execution decision control technical specification table of the energy storage robot.

[0061] G4-4, extract the required acceleration of the first instruction to be executed from the motion requirement information of the first instruction to be executed, and extract the maximum acceleration of each standby robot from the motion state information of each standby robot, and then according to ε j The statistical method of is similar to the statistical method to obtain the acceleration compliance of each standby robot, and it is recorded as ε′ j .

[0062] G4-5, from ε j and ε′ j The minimum value is selected as the motion compliance of each standby robot and recorded as

[0063] In a specific embodiment, if the required speed of the first robot to be executed is v0=3, and the required acceleration is a0=1.5, the reference speed difference and acceleration difference are set to Δv=1 and Δa=0.5 respectively.

[0064] There are three standby robots. The maximum speed of standby robot A1 is The maximum speed of standby robot A2 is The maximum speed of standby robot A3 is

[0065] Statistics on the speed compliance of standby robot A1

[0066] Statistics on the speed compliance of standby robot A2

[0067] Statistics on the speed compliance of the standby robot A3

[0068] The maximum acceleration of standby robot A1 is The maximum acceleration of standby robot A2 is The maximum acceleration of standby robot A3 is

[0069] Statistics on the acceleration compliance of standby robot A1

[0070] Statistics on the acceleration compliance of standby robot A2

[0071] Statistics on the acceleration compliance of the standby robot A3

[0072] Statistics on the motion compliance of standby robot A1

[0073] Statistics on the motion compliance of the standby robot A2

[0074] Statistics on the motion compliance of the standby robot A3

[0075] G5. Extract the energy storage information of each standby robot from the energy storage status information of each standby robot, and then calculate the energy storage compliance of each standby robot φ j .

[0076] Furthermore, the statistics of energy storage compliance of each standby robot include: G5-1, extracting the estimated required energy consumption of the first instruction to be executed from the energy storage requirement information of the first instruction to be executed, and recording it as E0.

[0077] G5-2. Extract the current remaining energy of each standby robot from the energy storage information of each standby robot and record it as E j .

[0078] G5-3. If the current remaining energy of a standby robot is less than the estimated energy consumption of the first instruction to be executed, the energy storage compliance of the standby robot is recorded as 0. Otherwise, the energy storage compliance of the standby robot is recorded as 1. Then the energy storage compliance of each standby robot is obtained and recorded as φ j .

[0079] G6. Count the instruction compliance of each standby robot η j ,

[0080] The embodiment of the present invention considers the status information of each robot and performs instruction confirmation for each instruction to be executed, thereby avoiding the current deficiency of not considering the status of the energy storage robot when executing instructions, improving the effectiveness of the energy storage robot's instruction execution, thereby ensuring the reliability of instruction execution, and avoiding the waste of time and resources, thereby improving the execution efficiency of instructions.

[0081] G7. Filter out the standby robot with the maximum value from the instruction compliance of each standby robot as the execution robot of the first instruction to be executed.

[0082] G8. Analyze and obtain the execution robots of each instruction to be executed in the same manner as the analysis method of the execution robot of the first instruction to be executed.

[0083] The embodiment of the present invention performs instruction confirmation for each instruction to be executed based on the instruction information of each instruction to be executed and the status information of each robot, and then obtains the execution robot of each instruction to be executed, thereby improving the execution efficiency of the overall instruction execution. At the same time, through in-depth analysis of the instruction information, the execution order of the instructions is optimized, thereby improving the efficiency of the energy storage robot's instruction execution.

[0084] The robot execution monitoring module is used to record the execution robots of each instruction to be executed as each execution robot, and monitor the execution information of each execution robot when executing the instruction to be controlled, and the execution information includes temperature information, position information and operation information.

[0085] It should be supplemented that the temperature information includes: the temperature of each electrical component at each monitoring time point when each robot executes the control instruction, the position information includes: the three-dimensional coordinates of each robot when it reaches the instruction location, and the operation information includes: the posture angle of each robot in the three coordinate axis directions of its target posture.

[0086] It should be supplemented that the temperature of each electrical component is monitored by a temperature sensor installed on each electrical component, the three-dimensional coordinates of each robot when it reaches the command location is monitored by a global positioning system installed inside the robot, and the posture angle of each robot's target posture in the three coordinate axis directions is monitored by an inclinometer.

[0087] The robot execution confirmation module is used to confirm the execution qualification index of each execution robot according to the execution information when each execution robot executes the control instruction.

[0088] See also Figure 3As shown, illustratively, the confirmation of the execution qualification index of each execution robot includes: N1, extracting the temperature of each electrical component of each execution robot at each monitoring time point when executing the control instruction from the temperature information when each execution robot executes the control instruction, and then calculating the temperature qualification index θ of each execution robot u , u is the execution robot number, u=1,2,......,w.

[0089] Furthermore, the temperature qualification index of each executing robot is calculated, including: N1-1, extracting the temperature of the motor of each executing robot at each monitoring time point when executing the control instruction from the temperature of each electrical component at each monitoring time point when each executing robot executes the control instruction, and then extracting the maximum motor temperature of each executing robot, recorded as C u .

[0090] N1-2. With the monitoring time point as the horizontal axis and the motor temperature as the vertical axis, construct the temperature change curve of each execution robot when executing the control instruction.

[0091] N1-3. Extract the maximum allowable temperature of the robot motor from the database, and construct a reference line in the temperature change curve with the maximum allowable temperature of the robot motor.

[0092] N1-4. Extract the length of the curve segment above the reference line from the temperature change curve of each execution robot when executing the control instruction, and record it as L u and the length of the temperature variation curve is recorded as L' u .

[0093] N1-5. Count the motor temperature qualification index of each execution robot K and C' are the abnormal temperature ratio and temperature of the set reference respectively.

[0094] It should be added that K and C′ are both extracted from the instruction execution decision control technical specification table of the energy storage robot.

[0095] It should be added that, if the execution robot has 5 monitoring time points, namely t1, t2, t3, t4 and t5, in the process of executing control instructions, the temperature of the execution robot's motor at each monitoring time point is: 40°C, 45°C, 50°C, 48°C, 46°C.

[0096] With the time point as the horizontal axis and the motor temperature as the vertical axis, the temperature change curve of the execution robot is connected by (t1,40), (t2,45), (t3,50), (t4,48) and (t5,46).

[0097] The maximum permissible temperature of the robot motor, 49°C, is extracted from the database. A reference line of this temperature is drawn in the temperature change curve. The length of the curve segment above the reference line and the length of the temperature change curve are then located, thereby obtaining the abnormal temperature ratio:

[0098] A temperature of 50°C was selected from the temperatures of the motor of the actuator robot at each monitoring time point.

[0099] When the reference abnormal temperature ratio K is set to 0.3 and the reference temperature C′ is set to 60°C, the motor temperature qualification index of the robot is statistically executed.

[0100] N1-6, extract the temperature of the controller and the battery at each monitoring time point when each executing robot executes the control instruction from the temperature of each electrical component at each monitoring time point when each executing robot executes the control instruction, and then The controller temperature qualification index and battery temperature qualification index of each execution robot are obtained by the same statistical method and recorded as and

[0101] N1-7, from and The minimum value is selected as the temperature qualification index of each execution robot and recorded as θ u .

[0102] N2. Extract the three-dimensional coordinates of each executing robot when it reaches the command location from the position information of each executing robot when executing the control command, and then calculate the position deviation qualification index θ′ of each executing robot u .

[0103] Furthermore, the statistical position deviation qualification index of each executing robot includes: N2-1, marking the three-dimensional coordinates of each executing robot at the time of reaching the instruction location as (x u ,y u ,z u ).

[0104] N2-2, extract the target three-dimensional coordinates corresponding to each executing robot from the control instructions of each instruction to be executed, denoted as (x′ u ,y′ u ,z′ u ).

[0105] N2-3. Count the deviation distance of each robot ξ u ,

[0106] In a specific embodiment, if the three-dimensional coordinates of the executing robot at the command location are (9.8m, 9.9m, 4.8m), and the target three-dimensional coordinates are (10m, 10m, 5m), the deviation distance of the statistical executing robot is

[0107] N2-4. Count the position deviation qualification index θ′ of each executing robot u , ξ′ is the deviation distance of the set reference, and e is a natural constant.

[0108] It should be added that ξ′ is extracted from the instruction execution decision control technical specification table of the energy storage robot.

[0109] N3. According to the operation information of each execution robot when executing the command to be controlled, the operation qualification index θ″ of each execution robot is calculated. u .

[0110] Furthermore, the statistical operation qualification index of each execution robot includes: N3-1, extracting the posture angles of the three coordinate axis directions corresponding to the actual posture of each execution robot from the operation information when each execution robot executes the control instruction, and recording them as α u , β u and γ u .

[0111] It should be added that α represents the attitude angle along the X-axis direction, β represents the attitude angle along the Y-axis direction, and γ represents the attitude angle along the Z-axis direction.

[0112] N3-2. Extract the attitude angles of the three coordinate axes of the target attitude of each robot from the control instructions to be executed, and record them as α′ u , β′ u and γ′ u , and then according to θ′ u The statistical method is similar to the statistical method to obtain the operation qualification index of each execution robot, and it is recorded as θ″ u .

[0113] N4. Count the execution qualification index of each execution robot u , l u =θ u *g1+θ′ u *g2+θ″ u *g3, g1, g2 and g3 are the weights of the set temperature qualification index, position deviation qualification index and operation qualification index respectively, g1+g2+g3=1, g3>g2>g1.

[0114] It should be added that the operation qualification index usually reflects the accuracy of the robot in performing specific operation tasks. Unqualified operation will directly lead to task failure or serious problems in product quality. The position deviation qualification index reflects the positioning accuracy of the robot in the workspace. Although the position deviation will not directly lead to task failure, it will affect the efficiency and accuracy of the task. The temperature qualification index mainly reflects the thermal stability of the robot in the process of performing tasks. In most cases, as long as the temperature is within a reasonable range, it will not have a significant impact on the direct completion of the task. Therefore, g3>g2>g1 is set. For the convenience of analysis, g1 can be specifically taken as 0.2, g2 can be specifically taken as 0.3, and g3 can be specifically taken as 0.5.

[0115] The embodiment of the present invention confirms the execution qualification index of each executing robot according to the execution information of each robot when executing the control instruction, thus breaking the deficiency of lack of in-depth analysis of the completion qualification index after the current instruction is executed, and more comprehensively evaluating the instruction execution effect of the energy storage robot. At the same time, it improves the timeliness of discovering potential problems of the energy storage robot in the process of executing the task, thereby improving the reliability and stability of the energy storage robot's instruction execution.

[0116] The database is used to store a map of the robot's working area and a maximum allowable temperature of the robot's motor.

[0117] The robot feedback terminal is used to provide corresponding feedback according to the execution qualification index of each robot.

[0118] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An instruction execution decision control system adapted for an energy storage robot, characterized in that: The system includes: A machine instruction receiving module, used for receiving various instructions to be executed sent by the control panel; The machine instruction analysis module is used to analyze each instruction to be executed according to each instruction to be executed sent by the control panel, and obtain instruction information of each instruction to be executed; The robot status monitoring module is used to monitor the status information of each robot, including operation status information and energy storage status information; The robot instruction confirmation module is used to confirm the instructions to be executed according to the instruction information of each instruction to be executed and the status information of each robot, and obtain the execution robot of each instruction to be executed; A robot execution monitoring module is used to record the execution robot of each instruction to be executed as each execution robot, and monitor the execution information of each execution robot when executing the instruction to be controlled, and the execution information includes temperature information, position information and operation information; The robot execution confirmation module is used to confirm the execution qualification index of each execution robot according to the execution information when each execution robot executes the control instruction; A database for storing a map of the robot's working area and storing the maximum permissible temperature of the robot's motor; The robot feedback terminal is used to provide corresponding feedback based on the execution qualification index of each robot.

2. According to claim 1, a command execution decision control system adapted for an energy storage robot is characterized in that: The instruction analysis of each instruction to be executed includes: Match and compare each to-be-executed instruction sent by the control panel with the instructions of the corresponding priority scores of each pre-set execution control type, and obtain the instruction scores of each execution control type in each to-be-executed instruction sent by the control panel, wherein the instruction control type includes security-related instructions, key operation instructions and routine task instructions; The priority scores of security-related instructions, key operation instructions, and routine task instructions in each pending instruction sent by the control panel are denoted as ρ i , i ′ and ρ i ″, i is the number of the instruction to be executed, i=1,2,......,m; The comprehensive priority score δ of each pending instruction sent by the statistical control panel i , δ i =ρ i *q1+ρ i ′*q2+ρ i ″*q3, q1+q2+q3=1, q1, q2 and q3 are the weights of the set safety-related instruction priority score, key operation instruction priority score and routine task instruction priority score, q1+q2+q3=1, q1>q2>q3; The to-be-executed instructions sent by the control panel are sorted according to their comprehensive priority scores, and the sorted to-be-executed instructions are used as instruction information of the to-be-executed instructions.

3. The command execution decision control system adapted for an energy storage robot according to claim 2, characterized in that: The step of performing instruction confirmation of each instruction to be executed includes: G1. Extract the first-ranked instruction to be executed from the instruction information of each instruction to be executed, and record it as the first instruction to be executed, and then extract the position requirement information, motion requirement information and energy storage requirement information of the first instruction to be executed; G2. Extract the operation type of each robot from the operation status information of each robot. If the operation status of the robot is a pending instruction operation, it is recorded as a standby robot; G3. Extract the position information of each standby robot from the running status information of each standby robot, and then calculate the path compliance of each standby robot j is the standby robot number, j = 1, 2, ..., n; G4. Extract the motion state information of each standby robot from the energy storage state information of each standby robot, and then calculate the motion compliance of each standby robot G5. Extract the energy storage information of each standby robot from the energy storage status information of each standby robot, and then calculate the energy storage compliance of each standby robot φ j ; G6. Count the instruction compliance of each standby robot η j , G7. Filter out the standby robot with the maximum value from the instruction compliance of each standby robot as the execution robot of the first instruction to be executed; G8. Analyze and obtain the execution robots of each instruction to be executed in the same manner as the analysis method of the execution robot of the first instruction to be executed.

4. The command execution decision control system adapted for an energy storage robot according to claim 3, characterized in that: The counting of the path conformity of each standby robot includes: extracting the position coordinates of the first instruction to be executed from the position requirement information of the first instruction to be executed; Extracting the position coordinates of each standby robot from the position information of each standby robot; Extract the robot work area map from the database, and then locate the shortest path between the first instruction to be executed and each standby robot, denoted as d j ; Statistics on the path compliance of each standby robot D is the distance threshold for setting the reference.

5. The command execution decision control system adapted for an energy storage robot according to claim 3, characterized in that: The counting of the motion compliance of each standby robot includes: Extract the required speed of the first instruction to be executed from the motion requirement information of the first instruction to be executed, and record it as v0; The maximum speed of each standby robot is extracted from the motion state information of each standby robot and recorded as v j ; Count the speed compliance of each standby robot ε j , Δv is the speed difference of the set reference; The required acceleration of the first instruction to be executed is extracted from the motion requirement information of the first instruction to be executed, and the maximum acceleration of each standby robot is extracted from the motion state information of each standby robot, and then according to ε j The statistical method of is similar to the statistical method to obtain the acceleration compliance of each standby robot, and it is recorded as ε′ j ; From ε j and ε′ j The minimum value is selected as the motion compliance of each standby robot and recorded as 6. The command execution decision control system adapted for an energy storage robot according to claim 3, characterized in that: The counting of energy storage compliance of each standby robot includes: Extracting the estimated required energy consumption of the first instruction to be executed from the energy storage requirement information of the first instruction to be executed, and recording it as E0; The current remaining energy of each standby robot is extracted from the energy storage information of each standby robot and recorded as E j ; If the current remaining energy of a standby robot is less than the estimated energy consumption of the first instruction to be executed, the energy storage compliance of the standby robot is recorded as 0. Otherwise, the energy storage compliance of the standby robot is recorded as 1. Then the energy storage compliance of each standby robot is obtained and recorded as φ j .

7. The command execution decision control system adapted for an energy storage robot according to claim 1, characterized in that: The confirmation of the execution qualification index of each execution robot includes: N1. Extract the temperature of each electrical component of each executing robot at each monitoring time point when executing the control instruction from the temperature information of each executing robot when executing the control instruction, and then calculate the temperature qualification index θ of each executing robot u , u is the execution robot number, u=1,2,......,w; N2. Extract the three-dimensional coordinates of each executing robot when it reaches the command location from the position information of each executing robot when executing the control command, and then calculate the position deviation qualification index θ′ of each executing robot u ; N3. According to the operation information of each execution robot when executing the command to be controlled, the operation qualification index θ″ of each execution robot is calculated. u ; N4. Count the execution qualification index of each execution robot u , l u =θ u *g1+θ′ u *g2+θ″ u *g3, g1, g2 and g3 are the weights of the set temperature qualification index, position deviation qualification index and operation qualification index respectively, g1+g2+g3=1, g3>g2>g1.

8. The command execution decision control system adapted for an energy storage robot according to claim 7, characterized in that: The counting of the temperature qualification index of each execution robot includes: The temperature of the motor of each executing robot at each monitoring time point when executing the control instruction is extracted from the temperature of each electrical component at each monitoring time point when each executing robot executes the control instruction, and then the maximum motor temperature of each executing robot is extracted from it, which is recorded as C u ; With the monitoring time point as the horizontal axis and the motor temperature as the vertical axis, a temperature change curve of each execution robot when executing the control instruction is constructed; Extracting the maximum allowable temperature of the robot motor from the database, and constructing a reference line in the temperature variation curve with the maximum allowable temperature of the robot motor; The length of the curve segment above the reference line is extracted from the temperature change curve of each execution robot when executing the control instruction, and recorded as L u and the length of the temperature variation curve is recorded as L u ′; Count the motor temperature qualification index of each execution robot K and C′ are the abnormal temperature ratio and temperature of the set reference, respectively; The temperature of the controller and the battery at each monitoring time point when each executing robot executes the control instruction is extracted from the temperature of each electrical component at each monitoring time point when each executing robot executes the control instruction, and then The controller temperature qualification index and battery temperature qualification index of each execution robot are obtained by the same statistical method and recorded as and from and The minimum value is selected as the temperature qualification index of each execution robot and recorded as θ u .

9. The command execution decision control system adapted for an energy storage robot according to claim 7, characterized in that: The statistical position deviation qualification index of each execution robot includes: The three-dimensional coordinates of each robot when it reaches the command location are marked as (x u ,y u ,z u ); Extract the target three-dimensional coordinates of each executing robot from the control instructions of each instruction to be executed, and record them as (x′ u ,y′ u ,z′ u ); Count the deviation distance ξ of each execution robot u , Statistical position deviation qualification index θ′ of each executing robot u , ξ′ is the deviation distance of the set reference, and e is a natural constant.

10. The command execution decision control system adapted for an energy storage robot according to claim 7, characterized in that: The counting of the operation qualification index of each execution robot includes: The posture angles of the three coordinate axes corresponding to the actual posture of each executing robot are extracted from the operation information when each executing robot executes the control command, and are recorded as α u , β u and γ u ; Extract the attitude angles of the three coordinate axes of the target attitude of each robot from the control instructions to be executed, and record them as α′ u , β′ u and γ′ u , and then according to θ′ u The statistical method is similar to the statistical method to obtain the operation qualification index of each execution robot, and it is recorded as θ″ u .

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