Analog control system based on cloud native SaaS

Abstract

The invention relates to the technical field of automatic control, and discloses an analog control system based on cloud native SaaS, which acquires analog data through a data acquisition module, generates an operation path through a path planning strategy, provides real-time operation path planning, ensures that equipment can execute tasks according to an optimal path, and improves the operation efficiency and precision. An abnormity judgment module is arranged to monitor equipment data in real time, the abnormal state of the equipment is judged through an abnormity judgment strategy, the authenticity of the abnormal state is judged through a true judgment strategy, and various abnormal states of the equipment are monitored and recognized in time; an instruction issuing module is arranged, if the equipment is abnormal and the equipment is true after being abnormal, an instruction is issued through an instruction adjusting strategy, the equipment problem is rapidly repaired, and a normal operation state is recovered; and the response display module receives the issued instruction and makes a response, and displays the content of the instruction, the heartbeat of the equipment, the position of the equipment, the allowance of the equipment and the operation path.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a simulation control system based on cloud-native SaaS. Background Art

[0002] By utilizing the elasticity and flexibility provided by the cloud computing platform, and using the microservice architecture to achieve modularity and scalability, computing resources can be dynamically adjusted according to demand. In combination with machine learning and data mining technology, pattern recognition, predictive analysis, and optimization suggestions can be performed. The simulation control system can be applied to industrial control and automation as well as smart city management. Here, the simulation control system is applied to agricultural robots to achieve smart farm management. However, most of the existing technologies do not solve the problem of how to determine the abnormal status of the equipment by obtaining simulation data and equipment monitoring data, and issue relevant instructions, respond to instructions in a timely manner, and perform visual processing.

[0003] For example, a Chinese patent with authorization announcement number CN102354167B discloses a building equipment simulation control system, including a cloud computing center, a remote control, a device control host and mechanical equipment, valve equipment and electrical equipment connected to the device control host; the device control host has a data access module and a local wireless communication module connected to the Internet; the device control host can be connected to the cloud computing center on the Internet through the Internet data access module or establish a communication connection with the remote control through the local wireless communication module; the remote control can be connected to the cloud computing center through the Internet or connected to the local wireless communication module of the device control host through a wireless communication protocol; the cloud computing center and the device control host are each pre-installed with a programmable controller simulation program; the two programmable controller simulation programs keep running synchronously; it has the advantages of energy saving, easy use and beautification of the environment, and is suitable for installation and use in enterprises, institutions and residences.

[0004] For example, a Chinese patent with authorization announcement number CN111142470B discloses an automated simulation control system and fault detection method for terminal equipment, which utilizes multiple touch-sensitive operation control panels docked to a PLC control architecture platform and multiple detection components corresponding to field equipment to automatically simulate the operating status parameters of field equipment; at the same time, the detection components running on the multiple docked touch-sensitive operation control panels are paired and affect each other, and the pairing influence parameters are detected by the touch-sensitive operation control panels and fed back to the PLC control architecture platform. The PLC control architecture platform is connected to a graphical user interface, and the comparison effect of the operating trajectory of the detection components corresponding to the terminal equipment is displayed on the graphical user interface, and the comparison effect is used to determine whether there is an abnormality in the working status of the multiple currently paired terminal equipment. The above scheme can be carried out for a long time and can promptly warn of possible fault phenomena in various terminal equipment.

[0005] The above patents have the problems raised by this background technology: the above-mentioned building equipment simulation control system controls the cloud computing center and the equipment control host through the programmable controller simulation program, and establishes a communication connection with the remote control through the wireless communication module; the above-mentioned terminal equipment automation simulation control system and fault detection method automatically simulate the operating status parameters of the field equipment through the touch-sensitive operation control panel and the detection parts corresponding to the field equipment, and feed back to the PLC control architecture station, which is connected to the graphical user interface and displays the comparison effect of the operating trajectory of the detection parts corresponding to the terminal equipment to determine whether there is an abnormality in the working status of multiple currently paired terminal equipment. The above two patents do not solve the problem of how to determine the abnormal state of the equipment by obtaining simulation data and equipment monitoring data, and issue relevant instructions, respond to instructions in time and perform visualization processing. To solve this problem, the present invention proposes a simulation control system based on cloud-native SaaS. Summary of the invention

[0006] In view of the above-mentioned problems existing in the prior art, the present invention proposes a simulation control system based on cloud-native SaaS.

[0007] Therefore, the object of the present invention is to provide a simulation control system based on cloud-native SaaS.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a data acquisition module, an abnormality judgment module, an instruction issuing module and a response display module; The data acquisition module is used to acquire simulation data and generate an operation path through a path planning strategy; The abnormality judgment module is used to monitor the device data in real time, and judge the abnormal state of the device through the abnormality judgment strategy, and judge the authenticity of the abnormal state through the authenticity judgment strategy; The abnormality judgment strategy includes: The abnormal state of the device is determined according to the abnormality determination formula, and the calculation formula of the abnormality determination formula is as follows: ; In the formula, Indicates the abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates the abnormal status of the device location. Indicates abnormal status of equipment reserve; like , then the device has no abnormality; like , then the device is abnormal, and the authenticity of the abnormal state is determined by the real judgment strategy; The truth judgment strategy includes: When a device is abnormal, the online status of the device is determined. If the device is not online, the abnormality of the device is false. If the device is online, the abnormal status of the device heartbeat, device location and device balance is determined. If the device heartbeat is abnormal, and the device location and device balance are not abnormal, the device abnormality is false; If the device heartbeat is abnormal, the device position or the device margin is abnormal, the device position or the device margin is adjusted. If the device does not become abnormal after the device position or the device margin is adjusted, the device abnormality is false; If the device is abnormal after adjusting the device position or device margin, then the device abnormality is true; If the device heartbeat is normal, but the device position or device margin is abnormal, adjust the device position or device margin. If the device does not become abnormal after adjusting the device position or device margin, the device abnormality is false. If the device is abnormal after adjusting the device position or device margin, then the device abnormality is true; The instruction issuing module is used to issue instructions through an instruction adjustment strategy if the device is abnormal and the device is abnormal is true; The response display module is used to receive the issued instruction and respond, and display the content of the instruction, device heartbeat, device location, device margin and operation path.

[0009] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, wherein: the simulation data includes basic site information, equipment data and operation path; The device data includes device number, device heartbeat, device location and device balance; The equipment location includes an operation point and a supply point; The operation path is generated by a path planning strategy, and the path planning strategy includes: The equipment position is determined according to the basic information of the site, the operation points and the supply points are sorted to form an operation list and a supply list, the shortest path of the equipment operation is calculated, and the equipment operates in sequence according to the shortest path of the equipment operation, the current position of the equipment and the equipment balance are monitored in real time, the consumption rate of the equipment balance is calculated, the exhaustion time of the equipment balance is predicted, and an alarm signal is generated. The operation list is dynamically adjusted according to the alarm signal, and the shortest path of equipment replenishment is calculated, and the equipment performs replenishment according to the shortest path of equipment supply.

[0010] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the function expression of the shortest path of the equipment operation is as follows: ; In the formula, Indicates the shortest path length of the equipment operation, The minimum value of the path representing the device operation, Indicates the total number of operating points, Indicates the current position of the slave device To Work points Path cost; The functional expression of the consumption rate of the equipment margin is as follows: ; In the formula, Indicates the rate at which the equipment reserve is consumed. Indicates the time interval Equipment surplus consumed within the facility; The function expression for predicting the exhaustion time of the equipment margin is as follows: ; In the formula, Indicates the time when the device's remaining power is exhausted. Indicates the device margin.

[0011] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the adjustment strategy of the job list includes: Configure a time threshold. If the device margin exhaustion time is greater than or equal to the time threshold, it means that the device margin is sufficient and no alarm signal is generated. If the equipment balance exhaustion time is less than the time threshold, an alarm signal is generated, the job list and supply list are updated, the shortest path between the equipment's current position and the job list is recalculated, the job point in the job list is selected according to the equipment balance, and the supply point in the supply list is returned to perform replenishment based on the equipment balance exhaustion time and the shortest path for equipment replenishment. After replenishment, the job list is updated and the shortest path between the equipment's current position and the job list is recalculated.

[0012] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the path planning strategy also includes: Generate an equipment list based on the equipment number, and execute the job list in sequence according to the equipment list. If the equipment executes the job, the remaining equipment in the equipment list will be on standby; if the equipment executes the replenishment, the remaining equipment in the equipment list will execute the job based on the equipment balance and equipment position.

[0013] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the instruction adjustment strategy includes: If the device heartbeat is normal, but the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, a restart command is issued; If the device heartbeat is abnormal, the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, the device operation is stopped and a reconfiguration instruction is issued; If the device becomes abnormal after being adjusted according to the restart command or reconfiguration command, the device operation is stopped, a diagnostic command is issued, and a technical staff is notified to handle the problem.

[0014] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the response display module is used to receive the issued instructions and respond, and visually display the content of the instructions, monitor the simulation data in real time, and visually display the device heartbeat, the device location, the device margin and the operation path, and visually display the abnormal status of the device.

[0015] The beneficial effects of the present invention are as follows: the present invention acquires simulation data through a data acquisition module, generates an operation path through a path planning strategy, provides real-time operation path planning, ensures that the equipment can perform tasks according to the optimal path, and improves operation efficiency and accuracy; the abnormal judgment module monitors the equipment data in real time, and judges the abnormal state of the equipment through an abnormal judgment strategy, and judges the authenticity of the abnormal state through a true judgment strategy, timely monitors and identifies various abnormal states of the equipment, and reduces downtime and cost losses caused by equipment abnormalities; an instruction issuing module is set, if the equipment is abnormal and the equipment abnormality is true, an instruction is issued through an instruction adjustment strategy, the equipment problem is quickly repaired, and the normal operation state is restored; the issued instruction is received and responded to through a response display module, and the content of the instruction, the equipment heartbeat, the equipment location, the equipment margin and the operation path are displayed, providing the technician with comprehensive equipment status information, and helping to monitor and manage the operation of the equipment in real time.

[0016] The present invention ensures that the system can quickly respond to real-time changes in the environment and equipment status through the combination of the data acquisition module and the abnormality judgment module, thereby improving the real-time and accuracy of equipment operation; the instruction issuing module cooperates with the abnormality judgment module to quickly judge and handle equipment abnormalities, reduce abnormality repair time, and improve equipment reliability and stability; the response display module displays through a clear interface, so that technicians can understand the equipment status and execution status in a timely manner, which helps to make quick decisions and adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 This is a system structure diagram of a simulation control system based on cloud-native SaaS in the present invention; Figure 2 This is a flow chart of a job list adjustment strategy for a simulation control system based on cloud-native SaaS in the present invention; Figure 3 This is a flow chart of a real judgment strategy for a simulation control system based on cloud-native SaaS in the present invention; Figure 4 This is a method flow chart of a simulation control method based on cloud-native SaaS of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1

[0022] In this embodiment, a system structure diagram of a simulation control system based on cloud native SaaS is provided, such as Figure 1 As shown, a simulation control system based on cloud-native SaaS includes a data acquisition module, an abnormality judgment module, an instruction issuing module and a response display module.

[0023] The agricultural robot in this embodiment refers to the equipment.

[0024] The data acquisition module is used to obtain simulation data, generate the operation path through the path planning strategy, and provide real-time operation path planning to ensure that the equipment can perform tasks according to the optimal path and improve operation efficiency and accuracy.

[0025] The simulation data includes basic site information, equipment data and operation path.

[0026] The device data includes the device number, device heartbeat, device location, and device margin.

[0027] Equipment locations include work points and supply points.

[0028] The operation path is generated through the path planning strategy, which includes: Determine the equipment location based on the basic site information, sort the operation points and supply points to form an operation list and a supply list, calculate the shortest path for the equipment operation, and the equipment will operate in sequence along the shortest path for the equipment operation. Monitor the current position of the equipment and the equipment balance in real time, calculate the consumption rate of the equipment balance, predict the time when the equipment balance will be exhausted, generate an alarm signal, dynamically adjust the operation list based on the alarm signal, and calculate the shortest path for the equipment replenishment, and the equipment will perform replenishment along the shortest path for the equipment supply.

[0029] The function expression of the shortest path of equipment operation is as follows: ; In the formula, Indicates the shortest path length of the equipment operation, The minimum value of the path representing the device operation, Indicates the total number of operating points, Indicates the current position of the slave device To Work points Path cost.

[0030] It should be explained that the shortest path for the above equipment operation is to find the shortest path that minimizes the path cost between the operation points.

[0031] The agricultural robot is spraying pesticides on the farm. The work point on the farm is the place where pesticides need to be sprayed, and the supply point on the farm is the place to replenish the power and medicine of the agricultural robot. The initial position, work point and supply point of the agricultural robot are determined according to the basic information of the farm site. The work point and supply point with the shortest path to the agricultural robot are used as the first work point and the first supply point, and so on to generate the initial work list and supply list, and control the agricultural robot to work in sequence according to the work list.

[0032] The function expression of the consumption rate of device margin is as follows: ; In the formula, Indicates the rate at which the equipment reserve is consumed. Indicates the time interval The equipment surplus consumed within.

[0033] The function expression for predicting the exhaustion time of the equipment margin is as follows: ; In the formula, Indicates the time when the device's remaining power is exhausted. Indicates the device margin.

[0034] The current position, oil remaining and pesticide remaining of the agricultural robot are monitored in real time, the consumption rate of the oil remaining and the consumption rate of the pesticide remaining are calculated respectively, and the depletion time of the oil remaining and the pesticide remaining is predicted.

[0035] The adjustment strategy of the job list is as follows: Figure 2 As shown, specifically including: Configure a time threshold. If the device margin exhaustion time is greater than or equal to the time threshold, it means that the device margin is sufficient and no alarm signal is generated. If the equipment balance exhaustion time is less than the time threshold, an alarm signal is generated, the job list and supply list are updated, the shortest path between the equipment's current position and the job list is recalculated, the job point in the job list is selected according to the equipment balance, and the supply point in the supply list is returned to perform replenishment based on the equipment balance exhaustion time and the shortest path for equipment replenishment. After replenishment, the job list is updated and the shortest path between the equipment's current position and the job list is recalculated.

[0036] When the depletion time of the remaining oil and the remaining medicine exceeds the preset time threshold, an alarm signal needs to be generated. First, the operation list and the supply list are updated according to the current position of the agricultural robot, and the operation point with the shortest path to the current position in the updated operation list is found. When the remaining oil and the remaining medicine of the agricultural robot cannot support the spraying operation of the operation point with the shortest path, directly find the supply point with the shortest path to the current position of the agricultural robot for replenishment. When the remaining oil and the remaining medicine of the agricultural robot support the spraying operation of the operation point with the shortest path, perform the spraying operation. After completing the spraying operation, return to the supply point with the shortest path to the current position of the agricultural robot before the remaining oil and the remaining medicine are exhausted. After the replenishment is completed, the operation list is updated, and the operation point with the shortest path is found with the supply point as the current position of the agricultural robot, and the next step of spraying is performed.

[0037] The function expression of the shortest path for equipment replenishment is as follows: ; In the formula, represents the shortest path length for equipment replenishment, Indicates the minimum value of the path for equipment replenishment, Represents the total number of supply points, Indicates the current position of the slave device To Supply Points Path cost.

[0038] According to the formula of the shortest path for equipment replenishment mentioned above, find the replenishment point with the shortest distance to the agricultural robot path for replenishment.

[0039] Path planning strategies also include: Generate an equipment list based on the equipment number, and execute the job list in sequence according to the equipment list. If the equipment executes the job, the remaining equipment in the equipment list will be on standby; if the equipment executes the replenishment, the remaining equipment in the equipment list will execute the job based on the equipment balance and equipment position.

[0040] A list of agricultural robots for spraying pesticides is generated according to the numbers of the agricultural robots. When the first agricultural robot is performing the spraying operation, the other agricultural robots in the agricultural robot list are on standby. When the first agricultural robot is performing replenishment, the other agricultural robots in the agricultural robot list execute the operation point with the shortest path to the agricultural robot based on their current positions, remaining oil and remaining pesticide amounts. The number of agricultural robots is determined according to the actual farm area and the number of operation points.

[0041] The abnormality judgment module is used to monitor equipment data in real time, and judge the abnormal status of the equipment according to the equipment data through the abnormality judgment strategy, and judge the authenticity of the abnormal status through the real judgment strategy, so as to timely monitor and identify various abnormal status of the equipment and reduce the downtime and cost loss caused by equipment abnormality.

[0042] The abnormal state of the device is comprehensively judged based on the device heartbeat, device location, and device margin in the device data through the abnormal judgment strategy. The abnormal judgment strategy includes: The abnormal state of the device is determined according to the abnormality judgment formula. The calculation formula of the abnormality judgment formula is as follows: ; In the formula, Indicates the abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates the abnormal status of the device location. Indicates abnormal status of device reserve. Represents a logical AND operation.

[0043] It should be explained that the abnormality judgment formula means that when the device heartbeat, device position and device margin are normal, the device is normal. , indicating that the device is not abnormal. , indicating that the device is abnormal; when the device heartbeat is abnormal , indicating that the device heartbeat is normal. When the device heartbeat is abnormal , indicating that the device heartbeat is abnormal; when the device location is abnormal , indicating that there is no abnormality in the device location. , indicating that the equipment position is abnormal; when the equipment surplus is abnormal , indicating that the equipment margin is normal. When the equipment margin is abnormal , indicating that the device margin is abnormal.

[0044] like , then the device has no abnormality; like , then the device is abnormal, and the authenticity of the abnormal state is determined by the real judgment strategy.

[0045] Real judgment strategy Figure 3 As shown, specifically including: When a device is abnormal, the online status of the device is determined. If the device is not online, the abnormality of the device is false. If the device is online, the abnormal status of the device heartbeat, device location and device balance is determined. If the device heartbeat is abnormal, and the device location and device balance are not abnormal, the device abnormality is false; If the device heartbeat is abnormal, the device position or the device margin is abnormal, the device position or the device margin is adjusted. If the device does not become abnormal after the device position or the device margin is adjusted, the device abnormality is false; If the device is abnormal after adjusting the device position or device margin, then the device abnormality is true; If the device heartbeat is normal, but the device position or device margin is abnormal, adjust the device position or device margin. If the device does not become abnormal after adjusting the device position or device margin, the device abnormality is false. If the device fails after adjusting the device position or device margin, then Device Failure is true.

[0046] The abnormal situation of agricultural robots in farms is caused by the mutual influence of the heartbeat of the agricultural robot, the position of the agricultural robot, and the oil and medicine remaining amounts of the agricultural robot. The heartbeat of the agricultural robot refers to the signal sent periodically by the agricultural robot, which is used to confirm whether the agricultural robot is operating normally. The position of the agricultural robot and the abnormalities of the oil and medicine remaining amounts of the agricultural robot will affect the abnormal heartbeat of the agricultural robot. Whether the heartbeat of the agricultural robot is abnormal is compared with the preset heartbeat threshold, whether the position of the agricultural robot is abnormal is compared with the preset position, and whether the oil and medicine remaining amounts of the agricultural robot are abnormal are compared with the preset remaining threshold. When the heartbeat of the agricultural robot is greater than the heartbeat threshold, it means that the heartbeat of the agricultural robot is abnormal. When the position of the agricultural robot is not at the preset position, it means that the heartbeat of the agricultural robot is abnormal. When the oil and medicine remaining amounts of the agricultural robot are less than the remaining threshold, it means that the oil and medicine remaining amounts of the agricultural robot are abnormal.

[0047] After determining whether the agricultural robot has an abnormality according to the above formula, the authenticity of the abnormality of the agricultural robot is determined. First, determine whether the agricultural robot is online. If the agricultural robot is not online, then the abnormality of the agricultural robot is false. If the agricultural robot is online, it is necessary to make a comprehensive judgment based on the heartbeat, position, oil remaining and medicine remaining of the agricultural robot. When the heartbeat of the agricultural robot is abnormal, but the position of the agricultural robot and the oil remaining and medicine remaining of the agricultural robot are not abnormal, it means that the abnormality of the agricultural robot is false; when the heartbeat of the agricultural robot is abnormal, but the position of the agricultural robot or the oil remaining and medicine remaining of the agricultural robot are abnormal, and the agricultural robot does not have an abnormality after adjustment, then the abnormality of the agricultural robot is false, otherwise it is true; when the heartbeat of the agricultural robot is not abnormal, but the position of the agricultural robot or the oil remaining and medicine remaining of the agricultural robot are abnormal, and the agricultural robot does not have an abnormality after adjustment, then the abnormality of the agricultural robot is false, otherwise it is true.

[0048] The instruction issuing module is used to issue instructions through the instruction adjustment strategy if the device has an abnormality and the device abnormality is true, so as to quickly repair the device problem and restore the normal operation status.

[0049] Instruction adjustment strategies include: If the device heartbeat is normal, but the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, a restart command is issued; If the device heartbeat is abnormal, the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, the device operation is stopped and a reconfiguration instruction is issued; If the device becomes abnormal after being adjusted according to the restart command or reconfiguration command, the device operation is stopped, a diagnostic command is issued, and a technical staff is notified to handle the problem.

[0050] When the heartbeat of the agricultural robot is not abnormal, but the position of the agricultural robot or the oil and medicine remaining amounts of the agricultural robot are abnormal and have not returned to normal after adjustment, a restart instruction of the agricultural robot is issued, and an attempt is made to solve the abnormal problem of the agricultural robot by restarting; when the heartbeat of the agricultural robot is abnormal, the position of the agricultural robot or the oil and medicine remaining amounts of the agricultural robot are abnormal and have not returned to normal after adjustment, the operation of the agricultural robot is stopped first, and a reconfiguration instruction is issued to reconfigure the parameters of the agricultural robot, including adjusting the navigation settings to ensure the accurate positioning of the agricultural robot on the farm, adjusting the division parameters of the working area to ensure that the agricultural robot operates in different working areas as planned, adjusting the communication parameters to ensure that the agricultural robot can communicate and exchange data effectively, and adjusting the parameters of the automatic stop of the agricultural robot to improve the response capability of the agricultural robot in an emergency; when the agricultural robot has not returned to normal after adjustment according to the restart instruction or the reconfiguration instruction, the operation of the agricultural robot is stopped first, and a diagnosis instruction is issued to notify the technicians to remotely diagnose the problem of the agricultural robot and solve the problem based on the diagnosis results.

[0051] The response display module is used to receive and respond to the issued instructions, and display the content of the instructions, equipment heartbeat, equipment location, equipment margin and operation path, providing technicians with comprehensive equipment status information to help monitor and manage equipment operation in real time.

[0052] Receive and respond to issued commands, visualize the contents of the commands, monitor simulation data in real time, visualize device heartbeat, device location, device margin, and operation path, and visualize abnormal status of the device.

[0053] After the instruction issuing module issues an instruction, it receives and parses the issued instruction, and displays the content of the instruction visually, so that the technician can make a judgment and respond accordingly according to the content of the instruction to solve the abnormality of the agricultural robot. During the restart, reconfiguration and diagnosis process, it monitors the simulation data in real time and displays the heartbeat of the agricultural robot, the position of the agricultural robot, the remaining oil and medicine amount of the agricultural robot, and the operation path of the agricultural robot visually. When the agricultural robot has an abnormality, it displays the abnormal state of the agricultural robot visually, focusing on the data in which the abnormality occurs, helping technicians to track the status of the agricultural robot in real time, effectively manage the operating status and abnormal handling of the agricultural robot, thereby improving the stability and reliability of the agricultural robot simulation control system.

[0054] Working principle and effect: (1) Easy to operate and configure. It can quickly configure the simulation control environment. The operation is simple and easy to use.

[0055] (2) Support simulation of multiple terminal devices, such as mobile phones, tablet computers, etc.

[0056] (3) Save time and improve simulation control efficiency.

[0057] (4) Support concurrent execution on multiple machines and multiple browsers.

[0058] (5) The overall closed-loop response is timely: The instructions are responded to in a timely manner, and the position, heartbeat, oil level and medicine level of the agricultural robot can be reported in real time. The reporting-receiving-sending-receiving layers are linked to form a closed loop.

[0059] Example 2

[0060] In this embodiment, a method flow chart of a simulation control method based on cloud native SaaS is provided. A simulation control method based on cloud native SaaS is implemented based on a simulation control system based on cloud native SaaS in Example 1, such as Figure 4 As shown, a simulation control method based on cloud native SaaS includes: S1. Obtain simulation data and generate a work path through a path planning strategy.

[0061] S2. Monitor device data in real time, and use an abnormality judgment strategy to judge the abnormal state of the device based on the device data, and use a true judgment strategy to judge the authenticity of the abnormal state.

[0062] S3. If the device is abnormal and the device is abnormal, the command is issued through the command adjustment strategy.

[0063] S4. Receive the issued command and respond, and display the command content, device heartbeat, device location, device margin and operation path.

[0064] For the specific content of the above-mentioned simulation control method based on cloud native SaaS, please refer to A simulation control system based on cloud native SaaS, which will not be repeated here.

[0065] Example 3

[0066] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned simulation control method based on cloud native SaaS.

[0067] Example 4

[0068] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned simulation control method based on cloud-native SaaS are implemented.

[0069] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A simulation control system based on cloud native SaaS, characterized in that: include: Data acquisition module, abnormality judgment module, instruction issuing module and response display module; The data acquisition module is used to acquire simulation data and generate an operation path through a path planning strategy; The abnormality judgment module is used to monitor the device data in real time, and judge the abnormal state of the device through the abnormality judgment strategy, and judge the authenticity of the abnormal state through the authenticity judgment strategy; The abnormality judgment strategy includes: The abnormal state of the device is determined according to the abnormality determination formula, and the calculation formula of the abnormality determination formula is as follows: ； In the formula, Indicates the abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates abnormal status of device location, Indicates abnormal status of equipment reserve; like , then the device has no abnormality; like , then the device is abnormal, and the authenticity of the abnormal state is determined by the real judgment strategy; The truth judgment strategy includes: When a device is abnormal, the online status of the device is determined. If the device is not online, the abnormality of the device is false. If the device is online, the abnormal status of the device heartbeat, device location and device balance is determined. If the device heartbeat is abnormal, and the device location and device balance are not abnormal, the device abnormality is false; If the device heartbeat is abnormal, the device position or the device margin is abnormal, the device position or the device margin is adjusted. If the device does not become abnormal after the device position or the device margin is adjusted, the device abnormality is false; If the device is abnormal after adjusting the device position or device margin, then the device abnormality is true; If the device heartbeat is normal, but the device position or device margin is abnormal, adjust the device position or device margin. If the device does not become abnormal after adjusting the device position or device margin, the device abnormality is false. If the device is abnormal after adjusting the device position or device margin, then the device abnormality is true; The instruction issuing module is used to issue instructions through an instruction adjustment strategy if the device is abnormal and the device is abnormal is true; The response display module is used to receive the issued instruction and respond, and display the content of the instruction, device heartbeat, device location, device margin and operation path.

2. A cloud-native SaaS-based simulation control system as claimed in claim 1, characterized in that: The simulation data includes basic site information, equipment data and operation path; The device data includes device number, device heartbeat, device location and device balance; The equipment location includes an operation point and a supply point; The operation path is generated by a path planning strategy, and the path planning strategy includes: The equipment position is determined according to the basic information of the site, the operation points and the supply points are sorted to form an operation list and a supply list, the shortest path of the equipment operation is calculated, and the equipment operates in sequence according to the shortest path of the equipment operation, the current position of the equipment and the equipment balance are monitored in real time, the consumption rate of the equipment balance is calculated, the exhaustion time of the equipment balance is predicted, and an alarm signal is generated. The operation list is dynamically adjusted according to the alarm signal, and the shortest path of equipment replenishment is calculated, and the equipment performs replenishment according to the shortest path of equipment supply.

3. A cloud-native SaaS-based simulation control system as described in claim 2, characterized in that: The function expression of the shortest path of the equipment operation is as follows: ； In the formula, Indicates the shortest path length of the equipment operation, The minimum value of the path representing the device operation, Indicates the total number of operating points, Indicates the current position of the slave device To Work points Path cost; The functional expression of the consumption rate of the equipment margin is as follows: ； In the formula, Indicates the rate at which the equipment reserve is consumed. Indicates the time interval Equipment surplus consumed within the facility; The function expression for predicting the exhaustion time of the equipment margin is as follows: ； In the formula, Indicates the time when the device's remaining power is exhausted. Indicates the device margin.

4. A cloud-native SaaS-based simulation control system as claimed in claim 3, characterized in that: The adjustment strategy of the job list includes: Configure a time threshold. If the device margin exhaustion time is greater than or equal to the time threshold, it means that the device margin is sufficient and no alarm signal is generated. If the equipment balance exhaustion time is less than the time threshold, an alarm signal is generated, the job list and supply list are updated, the shortest path between the equipment's current position and the job list is recalculated, the job point in the job list is selected according to the equipment balance, and the supply point in the supply list is returned to perform replenishment based on the equipment balance exhaustion time and the shortest path for equipment replenishment. After replenishment, the job list is updated and the shortest path between the equipment's current position and the job list is recalculated.

5. A cloud-native SaaS-based simulation control system as claimed in claim 4, characterized in that: The path planning strategy also includes: Generate an equipment list based on the equipment number, and execute the job list in sequence according to the equipment list. If the equipment executes the job, the remaining equipment in the equipment list will be on standby; if the equipment executes the replenishment, the remaining equipment in the equipment list will execute the job based on the equipment balance and equipment position.

6. A cloud-native SaaS-based simulation control system as claimed in claim 5, characterized in that: The instruction adjustment strategy includes: If the device heartbeat is normal, but the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, a restart command is issued; If the device heartbeat is abnormal, the device position or device margin is abnormal, and the device becomes abnormal after adjusting the device position or device margin, the device operation is stopped and a reconfiguration instruction is issued; If the device becomes abnormal after being adjusted according to the restart command or reconfiguration command, the device operation is stopped, a diagnostic command is issued, and a technical staff is notified to handle the problem.

7. A cloud-native SaaS-based simulation control system as claimed in claim 6, characterized in that: The response display module is used to: receive the issued instructions and respond, and visually display the content of the instructions, monitor the simulation data in real time, and visually display the device heartbeat, the device location, the device margin and the operation path, and visually display the abnormal status of the device.

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