A simulation control system based on cloud-native SaaS

Abstract

The present invention relates to the field of automatic control technology, and discloses a simulation control system based on cloud native SaaS. The simulation data is acquired through a data acquisition module, and an operation path is generated through a path planning strategy, providing real-time operation path planning, ensuring that the equipment can perform tasks according to the optimal path, and improving operation efficiency and accuracy; an abnormality judgment module is set to monitor equipment data in real time, and the abnormal state of the equipment is judged through an abnormality judgment strategy, and the authenticity of the abnormal state is judged through a true judgment strategy, so as to timely monitor and identify various abnormal states of the equipment; an instruction issuing module is set, and if an abnormality occurs in the equipment and the abnormality is true, an instruction is issued through an instruction adjustment strategy, so as to quickly repair the equipment problem and restore the normal operation state; a response display module receives and responds to the issued instruction, and displays the content of the instruction, the heartbeat of the equipment, the position of the equipment, the equipment margin 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] Leveraging the elasticity and flexibility provided by cloud computing platforms, and employing a microservices architecture for modularity and scalability, computing resources can be dynamically adjusted based on demand. Incorporating machine learning and data mining techniques enables pattern recognition, predictive analysis, and optimization recommendations. This allows for the application of analog control systems in industrial control and automation, as well as smart city management. Here, the analog control system is applied to agricultural robots to achieve intelligent farm management. However, most existing technologies fail to address the problem of how to identify equipment abnormalities by acquiring simulation data and equipment monitoring data, issuing relevant instructions, responding to instructions promptly, and performing visual processing.

[0003] For example, a Chinese patent with authorization announcement number CN102354167B discloses a building equipment simulation control system, which includes 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 connected to the Internet and a local wireless communication module; 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 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, the Chinese patent with the 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 connected 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. The comparison effect is used to determine whether there is any 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 provide timely warnings of possible fault phenomena of 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 on-site equipment through the touch-sensitive operation control panel and the detection parts corresponding to the on-site 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 any abnormality in the working status of the multiple currently paired terminal equipment. The above two patents 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. 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 purpose 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;

[0009] The data acquisition module is used to acquire simulation data and generate an operation path through a path planning strategy;

[0010] The abnormality judgment module is used to monitor 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;

[0011] The abnormality judgment strategy includes:

[0012] 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:

[0013] ;

[0014] Where, Indicates abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates abnormal status of the device location, Indicates abnormal status of equipment reserve;

[0015] like , the device has no abnormality;

[0016] like , then the device is abnormal, and the authenticity of the abnormal state is determined by the real judgment strategy;

[0017] The truth judgment strategy includes:

[0018] When a device is abnormal, the online status of the device is determined. If the device is not online, the abnormality is false.

[0019] If the device is online, the abnormal status of the device heartbeat, device location, and device remaining capacity is determined. If the device heartbeat is abnormal, and the device location and device remaining capacity are normal, the device abnormality is false.

[0020] If the device heartbeat, device position, or device margin is abnormal, adjust the device position or device margin. If the device does not show any abnormality after adjusting the device position or device margin, the device abnormality is false.

[0021] If the device fails after adjusting the device position or device margin, then the device fails is true;

[0022] 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 exhibit abnormalities after adjusting the device position or device margin, the device abnormality is false.

[0023] If the device fails after adjusting the device position or device margin, then the device fails is true;

[0024] The instruction issuing module is used to issue an instruction through an instruction adjustment strategy if an abnormality occurs in the device and the abnormality occurs in the device is true;

[0025] 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 remaining capacity and operation path.

[0026] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the simulation data includes basic site information, equipment data, and operation paths;

[0027] The device data includes device number, device heartbeat, device location and device remaining capacity;

[0028] The equipment location includes an operation point and a supply point;

[0029] The operation path is generated by a path planning strategy, and the path planning strategy includes:

[0030] The equipment location is determined based on 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 for the equipment operation is calculated, and the equipment operates in sequence according to the shortest path for 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, an alarm signal is generated, the operation list is dynamically adjusted according to the alarm signal, and the shortest path for equipment supply is calculated, and the equipment performs supply according to the shortest path for equipment supply.

[0031] 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 device operation is as follows:

[0032] ;

[0033] Where, Indicates the shortest path length of the equipment operation, Indicates the minimum value of the path for device operation, Indicates the total number of operating points, Indicates the current position of the slave device To work points Path cost;

[0034] The functional expression of the consumption rate of the device margin is as follows:

[0035] ;

[0036] Where, Indicates the rate at which the device's reserve is consumed. Indicates the time interval Equipment surplus for internal consumption;

[0037] The function expression for predicting the exhaustion time of the device margin is as follows:

[0038] ;

[0039] Where, Indicates the time when the device's remaining battery is exhausted. Indicates the device reserve.

[0040] 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:

[0041] Configure a time threshold. If the device's remaining battery power is depleted for a time greater than or equal to the time threshold, the device's remaining battery power is sufficient and no alarm signal is generated.

[0042] If the equipment's remaining energy is depleted for 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 based on the equipment's remaining energy, and the supply point in the supply list is returned to perform supply based on the equipment's remaining energy depletion time and the shortest path for equipment supply. After supply is completed, the job list is updated, and the shortest path between the equipment's current position and the job list is recalculated.

[0043] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the path planning strategy also includes:

[0044] Generate an equipment list based on the equipment number, and execute the operation list in sequence according to the equipment list. If the equipment performs the operation, the remaining equipment in the equipment list will be on standby; if the equipment performs the supply, the remaining equipment in the equipment list will perform the operation based on the equipment balance and equipment position.

[0045] As a preferred solution of the cloud-native SaaS-based simulation control system described in the present invention, the instruction adjustment strategy includes:

[0046] If the device heartbeat is normal, but the device position or device margin is abnormal, and the device returns to abnormal state after adjusting the device position or device margin, a restart command is issued;

[0047] If the device heartbeat is abnormal, the device position or device margin is abnormal, and the device returns to abnormal state after adjusting the device position or device margin, the device will be stopped and a reconfiguration command will be issued;

[0048] If the device malfunctions after being adjusted according to the restart command or reconfiguration command, the device will be stopped, a diagnostic command will be issued, and technical personnel will be notified to handle the problem.

[0049] 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 position, the device margin and the operation path, and visually display the abnormal status of the device.

[0050] The beneficial effects of the present invention are as follows: the present invention obtains simulation data through the data acquisition module, generates an operation path through the 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 abnormality judgment module monitors the equipment data in real time, and judges the abnormal state of the equipment through the abnormality judgment strategy, and judges the authenticity of the abnormal state through the real judgment strategy, timely monitors and identifies various abnormal states of the equipment, and reduces the downtime and cost loss caused by equipment abnormalities; an instruction issuing module is set, if the equipment has an abnormality and the equipment abnormality is true, an instruction is issued through the instruction adjustment strategy, the equipment problem is quickly repaired, and the normal operation state is restored; the issued instruction is received and responded to by the response display module, and the content of the instruction, equipment heartbeat, equipment location, equipment margin and operation path are displayed, providing technical personnel with comprehensive equipment status information, helping to monitor and manage the operation of the equipment in real time.

[0051] 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 operations; the instruction issuance 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, allowing technicians to understand the equipment status and execution status in a timely manner, which helps to make quick decisions and adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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. Those skilled in the art can derive other drawings based on these drawings without inventive work. Among them:

[0053] Figure 1 This is a system structure diagram of a simulation control system based on cloud native SaaS in the present invention;

[0054] 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;

[0055] 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;

[0056] Figure 4 This is a method flow chart of a simulation control method based on cloud native SaaS in the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. 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.

[0059] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0060] Example 1

[0061] 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.

[0062] The agricultural robot in this embodiment refers to equipment.

[0063] 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.

[0064] The simulation data includes basic site information, equipment data and operation paths.

[0065] Device data includes device number, device heartbeat, device location, and device reserve.

[0066] Equipment locations include operation points and supply points.

[0067] The operation path is generated through the path planning strategy, which includes:

[0068] 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 according to the shortest path for the equipment operation. Monitor the current position and equipment balance of the equipment in real time, calculate the consumption rate of the equipment balance, predict the time when the equipment balance is exhausted, generate an alarm signal, dynamically adjust the operation list according to the alarm signal, and calculate the shortest path for equipment supply. The equipment will perform supply according to the shortest path for equipment supply.

[0069] The function expression of the shortest path of equipment operation is as follows:

[0070] ;

[0071] Where, Indicates the shortest path length of the equipment operation, Indicates the minimum value of the path for device operation, Indicates the total number of operating points, Indicates the current position of the slave device To work points Path cost.

[0072] It should be explained that the shortest path for the above-mentioned equipment operation is to find the shortest path that minimizes the path cost between the operation points.

[0073] Agricultural robots spray pesticides on the farm. The work points on the farm are the places where pesticides need to be sprayed, and the supply points on the farm are the places where the agricultural robots' power and pesticides are replenished. The initial position, work points, and supply points of the agricultural robots are determined based on 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. The initial work list and supply list are generated by analogy, and the agricultural robots are controlled to work in sequence according to the work list.

[0074] The function expression of the consumption rate of device reserve is as follows:

[0075] ;

[0076] Where, Indicates the rate at which the device's reserve is consumed. Indicates the time interval The equipment surplus consumed within the device.

[0077] The function expression for predicting the exhaustion time of the device margin is as follows:

[0078] ;

[0079] Where, Indicates the time when the device's remaining battery is exhausted. Indicates the device reserve.

[0080] The current position, oil level, and pesticide level of the agricultural robot are monitored in real time. The consumption rates of the oil level and the pesticide level are calculated respectively, and the exhaustion time of the oil level and the pesticide level is predicted.

[0081] The adjustment strategy of the job list is as follows Figure 2As shown, specifically including:

[0082] Configure a time threshold. If the device's remaining battery power is depleted for a time greater than or equal to the time threshold, the device's remaining battery power is sufficient and no alarm signal is generated.

[0083] If the equipment's remaining energy is depleted for 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 based on the equipment's remaining energy, and the supply point in the supply list is returned to perform supply based on the equipment's remaining energy depletion time and the shortest path for equipment supply. After supply is completed, the job list is updated, and the shortest path between the equipment's current position and the job list is recalculated.

[0084] 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 spraying operation is performed.

[0085] The function expression of the shortest path for equipment replenishment is as follows:

[0086] ;

[0087] Where, Indicates the shortest path length for equipment replenishment, Indicates the minimum value of the equipment supply path, Indicates the total number of supply points, Indicates the current position of the slave device To Supply points Path cost.

[0088] According to the formula of the shortest path for equipment replenishment mentioned above, the replenishment point with the shortest distance to the agricultural robot path is found for replenishment.

[0089] Path planning strategies also include:

[0090] Generate an equipment list based on the equipment number, and execute the operation list in sequence according to the equipment list. If the equipment performs the operation, the remaining equipment in the equipment list will be on standby; if the equipment performs the supply, the remaining equipment in the equipment list will perform the operation based on the equipment balance and equipment position.

[0091] A list of agricultural robots for spraying is generated based on 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 the supply operation, 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 position, remaining oil amount, and remaining medicine amount. The number of agricultural robots is determined by the actual farm area and the number of operation points.

[0092] 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 authenticity judgment strategy, so as to timely monitor and identify various abnormal status of the equipment and reduce the downtime and cost losses caused by equipment abnormalities.

[0093] The abnormality judgment strategy comprehensively judges the abnormal status of the device based on the device heartbeat, device location, and device reserve in the device data. The abnormality judgment strategy includes:

[0094] 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:

[0095] ;

[0096] Where, Indicates abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates abnormal status of the device location, Indicates abnormal status of equipment reserve. Represents a logical AND operation.

[0097] 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 not abnormal. , indicating that the device is not abnormal. When the device is 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 margin is abnormal , indicating that the equipment margin is normal. When the equipment margin is abnormal , indicating that the device reserve is abnormal.

[0098] like , the device has no abnormality;

[0099] like , then the device is abnormal, and the authenticity of the abnormal state is determined by the real judgment strategy.

[0100] True judgment strategy Figure 3 As shown, specifically including:

[0101] When a device is abnormal, the online status of the device is determined. If the device is not online, the abnormality is false.

[0102] If the device is online, the abnormal status of the device heartbeat, device location, and device remaining capacity is determined. If the device heartbeat is abnormal, and the device location and device remaining capacity are normal, the device abnormality is false.

[0103] If the device heartbeat, device position, or device margin is abnormal, adjust the device position or device margin. If the device does not show any abnormality after adjusting the device position or device margin, the device abnormality is false.

[0104] If the device fails after adjusting the device position or device margin, then the device fails is true;

[0105] 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 exhibit abnormalities after adjusting the device position or device margin, the device abnormality is false.

[0106] If the device fails after adjusting the device position or device margin, then Device Failed is true.

[0107] Abnormal situations of agricultural robots on the farm are 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 to confirm whether the agricultural robot is operating normally, and abnormalities in the position of the agricultural robot and 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.

[0108] 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, then 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.

[0109] 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.

[0110] Instruction adjustment strategies include:

[0111] If the device heartbeat is normal, but the device position or device margin is abnormal, and the device returns to abnormal state after adjusting the device position or device margin, a restart command is issued;

[0112] If the device heartbeat is abnormal, the device position or device margin is abnormal, and the device returns to abnormal state after adjusting the device position or device margin, the device will be stopped and a reconfiguration command will be issued;

[0113] If the device malfunctions after being adjusted according to the restart command or reconfiguration command, the device will be stopped, a diagnostic command will be issued, and technical personnel will be notified to handle the problem.

[0114] When the heartbeat of the agricultural robot is normal, but the position of the agricultural robot or the oil and medicine remaining amounts are abnormal and have not returned to normal after adjustment, a restart command 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 are abnormal and have not returned to normal after adjustment, the operation of the agricultural robot is stopped first, and a reconfiguration command 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 operating area to ensure that the agricultural robot operates in different operating areas as planned, adjusting the communication parameters to ensure that the agricultural robot can carry out effective communication and data exchange, 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 command or the reconfiguration command, the operation of the agricultural robot is stopped first, and a diagnostic command is issued to notify the technicians to remotely diagnose the problem of the agricultural robot and solve the problem based on the diagnosis results.

[0115] The response display module is used to receive and respond to issued commands, and display the command content, device heartbeat, device location, device reserve and operation path, providing technicians with comprehensive device status information to help monitor and manage device operation in real time.

[0116] Receive and respond to issued commands, and visually display the content of the commands, monitor simulation data in real time, and visually display device heartbeat, device location, device margin and operation path, as well as visually display abnormal status of the device.

[0117] After the instruction issuing module issues an instruction, it receives and parses the issued instruction, and visually displays the content of the instruction to facilitate the judgment of technicians. According to the content of the instruction, the abnormality of the agricultural robot is resolved. During the restart, reconfiguration and diagnosis process, the simulation data is monitored in real time, and 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 are visually displayed. When the agricultural robot has an abnormality, the abnormal state of the agricultural robot is visually displayed, and the data in which the abnormality occurs is emphasized, which helps technicians track the status of the agricultural robot in real time, effectively manage the operating status and abnormality handling of the agricultural robot, thereby improving the stability and reliability of the agricultural robot simulation control system.

[0118] Working principle and its effect:

[0119] (1) Easy to operate and configure, it can quickly configure the simulation control environment, and the operation is simple and easy to use.

[0120] (2) Support simulation of multiple terminal devices, such as mobile phones, tablet computers, etc.

[0121] (3) Save time and improve simulation control efficiency.

[0122] (4) Supports concurrent execution on multiple machines and multiple browsers.

[0123] (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-issuing-receiving layers are linked together to form a closed loop.

[0124] Example 2

[0125] 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:

[0126] S1. Obtain simulation data and generate a work path through path planning strategy.

[0127] 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.

[0128] S3. If the device is abnormal and the device is abnormal, the command is issued through the command adjustment strategy.

[0129] S4. Receive the issued command and respond, and display the command content, device heartbeat, device location, device reserve and operation path.

[0130] 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.

[0131] Example 3

[0132] 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 performs the steps of implementing the above-mentioned cloud-native SaaS-based simulation control method.

[0133] Example 4

[0134] 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 cloud-native SaaS-based simulation control method are implemented.

[0135] The computer-readable storage medium includes various media for storing program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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, and all of these 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 simulation data includes basic site information, equipment data and operation path; The device data includes device number, device heartbeat, device location and device remaining capacity; 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: Determine the equipment location based on basic site information, sort the operation points and the supply points to form an operation list and a supply list, calculate the shortest path for equipment operation, and have the equipment operate in sequence along the shortest path for equipment operation, monitor the current location and equipment remaining in real time, calculate the consumption rate of the equipment remaining, predict the time when the equipment remaining will be exhausted, generate an alarm signal, dynamically adjust the operation list based on the alarm signal, and calculate the shortest path for equipment supply, and have the equipment perform supply along the shortest path for equipment supply; The adjustment strategies of the job list include: Configure a time threshold. If the device's remaining battery power is depleted for a time greater than or equal to the time threshold, the device's remaining battery power is sufficient and no alarm signal is generated. If the device's remaining energy is depleted less than the time threshold, an alarm signal is generated, the job list and supply list are updated, the shortest path between the device's current position and the job list is recalculated, a job point in the job list is selected based on the device's remaining energy, and the device returns to the supply point in the supply list for replenishment based on the device's remaining energy and the shortest path for equipment replenishment. After replenishment is complete, the job list is updated and the shortest path between the device's current position and the job list is recalculated. The abnormality judgment module is used to monitor 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 judgment formula. The calculation formula of the abnormality judgment formula is as follows: ； Where, Indicates abnormal status of the device. Indicates the abnormal status of the device heartbeat. Indicates abnormal status of the device location, Indicates abnormal status of equipment reserve; like , 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 is false. If the device is online, the abnormal status of the device heartbeat, device location, and device remaining capacity is determined. If the device heartbeat is abnormal, and the device location and device remaining capacity are normal, the device abnormality is false. If the device heartbeat, device position, or device margin is abnormal, adjust the device position or device margin. If the device does not show any abnormality 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 the device fails 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 exhibit abnormalities 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 the device fails is true; The instruction issuing module is used to issue an instruction through an instruction adjustment strategy if an abnormality occurs in the device and the abnormality occurs in the device 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 remaining capacity and operation path; The response display module is used to: receive the instructions issued 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.

2. A cloud-native SaaS-based simulation control system according to claim 1, characterized in that: The function expression of the shortest path of the equipment operation is as follows: ； Where, Indicates the shortest path length of the equipment operation, Indicates the minimum value of the path for 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 device margin is as follows: ； Where, Indicates the rate at which the device's reserve is consumed. Indicates the time interval Equipment surplus for internal consumption; The function expression for predicting the exhaustion time of the device margin is as follows: ； Where, Indicates the time when the device's remaining battery is exhausted. Indicates the device reserve.

3. A cloud-native SaaS-based simulation control system according to claim 2, characterized in that: The path planning strategy also includes: Generate an equipment list based on the equipment number, and execute the operation list in sequence according to the equipment list. If the equipment performs the operation, the remaining equipment in the equipment list will be on standby; if the equipment performs the supply, the remaining equipment in the equipment list will perform the operation based on the equipment balance and equipment position.

4. A cloud-native SaaS-based simulation control system according to claim 3, 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 returns to abnormal state 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 returns to abnormal state after adjusting the device position or device margin, the device will be stopped and a reconfiguration command will be issued; If the device malfunctions after being adjusted according to the restart command or reconfiguration command, the device will be stopped, a diagnostic command will be issued, and technical personnel will be notified to handle the problem.

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