Fire-fighting host remote control method and system based on mechanical arm

Abstract

The invention discloses a fire-fighting host remote control method and system based on a mechanical arm, and relates to the technical field of fire-fighting host control. By acquiring operation interface state information of a fire-fighting host and spatial positioning data of the tail end of a mechanical arm and a host panel, real-time calibration of the position of a target operation part is achieved, and accurate operation coordinate information can be output; by loading formula files corresponding to different fire-fighting hosts, operation part coordinates, operation sequences and action parameters are managed in a structured mode, and a configurable basis is provided for analysis of action logic of the mechanical arm. And after the system receives an operation instruction, a corresponding servo control instruction is generated according to the formula content and the calibration coordinates, so that the mechanical arm can accurately execute pressing, rotating or resetting actions. The system has the flexible adaptation capability to different hosts, and can reliably replace manual work to complete the operation process of the fire-fighting host in an unattended or remote control scene.

Description

Technical Field

[0001] This invention relates to the field of fire alarm control technology, specifically to a remote control method and system for a fire alarm control panel based on a robotic arm. Background Technology

[0002] With the widespread adoption of building fire protection systems, fire control rooms are generally equipped with fire alarm control panels for displaying alarm information, controlling linkages, and resetting systems. Traditional fire alarm control panel operation relies primarily on manual operation, where staff identify the panel's interface status and manually execute actions such as button confirmations, starting / stopping linked equipment, and resetting the system. Due to differences in fire alarm control panel models, interface layouts, and operating methods, manual operation is not only highly subjective but also demands a high level of skill and consistency from the operators.

[0003] While existing technologies utilize robotic arms to replace human-machine interface (HMI) operations, most focus on repetitive actions in fixed positions, lacking the ability to recognize the status of the fire alarm control panel interface and struggling to execute accurate operations in response to changes in the interface. Furthermore, significant differences exist in button positions, operating procedures, and motion parameters among different brands of fire alarm control panels, making it difficult to achieve unified control logic. In addition, the lack of real-time spatial calibration when performing actions such as pressing or rotating can easily lead to motion deviations, accidental or missed touches, affecting the reliability of automated operations. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for remotely controlling a fire alarm control panel based on a robotic arm.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention discloses a remote control method for a fire alarm control panel based on a robotic arm, comprising the following steps:

[0007] Acquire the status information of the fire control panel's operating interface and the spatial positioning data used to characterize the relative position of the end effector of the robotic arm and the fire control panel;

[0008] The operation interface status information and the spatial positioning data are subjected to coordinate calibration to obtain the operation coordinate information of the target operation component on the fire control panel.

[0009] Load the pre-stored recipe file corresponding to the target fire control panel, wherein the recipe file includes at least the coordinates of the operating components, the operating sequence, and parameter thresholds;

[0010] The operation instructions are obtained, and the action logic of the robotic arm is parsed based on the operation instructions, the recipe file, and the operation coordinate information. The action logic is then converted into servo control instructions executed by the servo motor.

[0011] The servo control command is sent to the robotic arm, causing the end effector of the robotic arm to perform pressing, rotating, or resetting actions according to the servo control command;

[0012] The system acquires the feedback position signal from the robotic arm's servo motor, and generates a correction command and re-executes the corresponding action when the feedback deviation meets the correction conditions.

[0013] Secondly, this invention discloses a remote control system for a fire alarm control panel based on a robotic arm, comprising:

[0014] The sensing and acquisition module is used to acquire the status information of the fire control panel's operating interface and the spatial positioning data used to characterize the relative position of the end effector of the robotic arm and the fire control panel.

[0015] The coordinate calibration module is used to perform coordinate calibration processing on the operation interface status information and the spatial positioning data to obtain the operation coordinate information of the target operation component on the fire control panel;

[0016] The recipe management module is used to load pre-stored recipe files corresponding to the target fire control panel. The recipe files include at least the coordinates of the operating components, the operating sequence, and parameter thresholds.

[0017] The instruction parsing module is used to acquire operation instructions, parse the operation instructions, the recipe file, and the operation coordinate information to obtain the motion logic of the robotic arm, and convert the motion logic into servo control instructions executed by the servo motor.

[0018] The mechanical execution module is used to send the servo control commands to the robotic arm, so that the end effector of the robotic arm performs pressing, rotating or resetting actions according to the servo control commands;

[0019] The feedback correction module is used to acquire the feedback position signal of the robotic arm's servo motor, and generate a correction command and re-execute the corresponding action when the feedback deviation meets the correction conditions.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0021] 1. By simultaneously acquiring the status information of the fire alarm control panel interface and the spatial positioning data between the robotic arm's end effector and the control panel, the robotic arm gains the ability to understand the location of the target operating area and the current operating environment. After obtaining the interface status information, coordinate calibration is completed in conjunction with spatial positioning. Real-time updated and directly usable coordinates of the target operating components can be obtained before each operation, without relying on manual marking or fixed templates. This allows the solution to cope with actual usage conditions such as differences in control panel models, changes in lighting, or robotic arm position offsets.

[0022] 2. By loading recipe files corresponding to specific fire alarm control panels, the operation becomes configurable and scalable. These recipe files use a structured description of "operating component coordinates, operating sequence, and parameter thresholds," allowing the operational differences between different devices to be written into the system as data without modifying the control logic or underlying instruction generation strategy. This achieves highly flexible adaptability and provides possibilities for subsequent maintenance and expansion. Attached Figure Description

[0023] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0024] Figure 1 This is a flowchart of the method of the present invention;

[0025] Figure 2 This is a schematic diagram of the workflow of the present invention;

[0026] Figure 3 This is a system architecture diagram of the present invention. Detailed Implementation

[0027] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0028] Application Overview

[0029] This invention relates to the field of automated operation technology for fire control equipment, specifically disclosing a remote control method for a fire control panel based on a robotic arm. The core of this method lies in combining interface status recognition, spatial positioning calibration, configurable operation formula analysis, and servo motion feedback correction to achieve automated and precise operation of the robotic arm on the buttons, knobs, and reset components of the fire control panel. The precise position of the target operating component is obtained through joint calibration of interface status information and spatial positioning data, and universal adaptation to different fire control panels is achieved using formula files, thereby constructing a highly reliable remotely executable action logic generation and dynamic correction mechanism.

[0030] Currently, fire alarm control panels typically rely on manual operation, which suffers from problems such as interface recognition depending on human experience, error-prone operation procedures, poor cross-model compatibility, and insufficient remote control capabilities. Existing robotic arm-based automation methods often lack the ability to dynamically recognize and calibrate the coordinates of the fire alarm control panel interface, making it difficult to ensure the accuracy of motion execution. They also lack a closed-loop feedback correction mechanism, resulting in insufficient reliability.

[0031] This invention aims to solve the technical problems of lack of reliable positioning basis, unconfigurable action process and inability to self-correct execution deviation when the robotic arm performs the operation of the fire control panel. The goal is to enable the key operation process of the fire control panel to be remotely, automatically and with high stability.

[0032] To achieve the above objectives, this invention obtains the status information of the fire alarm control panel and the spatial positioning data of the robotic arm end effector and the control panel, performs coordinate calibration to determine the precise position of the target operating component; manages the operating coordinates, sequence, and parameter thresholds of different control panels by loading preset formula files; parses the robotic arm's motion logic based on the operation instructions and formula content and generates servo execution instructions; and triggers motion correction when the deviation exceeds the limit by collecting the position signal feedback from the servo motor, thereby achieving complete closed-loop control.

[0033] like Figure 1 , Figure 2 As shown, this application proposes a remote control method for a fire alarm control panel based on a robotic arm, comprising the following steps:

[0034] The system acquires the status information of the fire alarm control panel's operating interface, as well as spatial positioning data characterizing the relative position of the robotic arm's end effector to the fire alarm control panel. The operating interface status information can include screen alarm codes and indicator light statuses captured by a fixed industrial camera, and visual features of physical buttons and knobs identified by a camera on the robotic arm's wrist. Spatial positioning data is preferably provided by high-precision devices such as laser displacement sensors to determine the precise spatial geometric relationship between the robotic arm's end effector and the fire alarm control panel in real time. This provides multimodal, high-real-time environmental information input for subsequent operations.

[0035] The operation interface status information and the spatial positioning data are subjected to coordinate calibration to obtain the operation coordinate information of the target operation component on the fire alarm control panel. A unified coordinate system (usually the robotic arm base coordinate system) is established, and the image coordinates of the visually recognized target component (such as the "alarm confirmation" button) are fused with the spatial geometric data measured by laser. For example, through hand-eye calibration algorithms, the "button" seen by the camera is associated with the "precise position of the button in space" measured by laser, and finally, an absolute "operation coordinate information" in its coordinate system that the robotic arm can directly understand and execute is output. This solves the problems that pure visual positioning is easily affected by lighting and viewing angle, and that pure geometric positioning lacks target recognition capabilities. Data fusion greatly improves the accuracy and robustness of the final operation coordinates.

[0036] The system loads a pre-stored recipe file corresponding to the target fire alarm control panel. This recipe file includes at least the coordinates of the operating components, the operating sequence, and parameter thresholds. The recipe file is a structured data set, including at least the "coordinates of the operating components" (i.e., the logical position reference of the target component on the control panel), the "operating sequence" (e.g., in case of a fire alarm, the "alarm confirmation" button must be pressed first, followed by the "linkage start" button, and finally the "reset" button), and the "parameter thresholds" (e.g., the required rotation angle of a knob, the required duration of button presses). By calling different recipes, the same robotic arm hardware system can adapt to different brands and models of fire alarm control panels without any physical modifications or rewriting of the underlying code, fundamentally solving the industry pain point of poor adaptability and limitation to only a single model in existing technologies.

[0037] The system acquires operation instructions and parses the robotic arm's motion logic based on these instructions, the recipe file, and the operation coordinate information. This motion logic is then converted into servo control instructions executed by the servo motors. Operation instructions are commands that trigger the entire operation, such as alarm signals from a fire alarm control panel or remote commands from a remote monitoring center. Upon receiving the instruction, the system combines the loaded recipe file (providing the operation flow) and calibrated operation coordinate information (providing precise location) to parse a complete, step-by-step motion logic. Subsequently, through hierarchical instruction transmission logic (such as a logic parsing layer and an instruction generation layer), this motion logic is converted into servo control instructions (such as pulse instructions or bus instructions) that the servo motors can directly recognize and execute. These instructions include information such as target position, motion trajectory, speed, and even the force of the end effector. This achieves the translation from task to action, transforming high-level operation commands into control signals for the underlying drive equipment, ensuring consistency between intent and action.

[0038] The servo control commands are sent to the robotic arm, causing its end effector to perform pressing, rotating, or resetting actions according to the commands. The robotic arm's servo system responds to the commands, driving the multi-degree-of-freedom robotic arm body to move, bringing the end effector to the designated operating coordinates, and performing pressing, rotating, and other simulated manual operations with configured force and method according to the parameter thresholds in the formula. This directly realizes the physical control of the fire alarm control panel, replacing repetitive and error-prone manual labor with highly reliable automated operation.

[0039] The system acquires feedback position signals from the robotic arm's servo motors and generates correction commands and re-executes the corresponding actions when the feedback deviation meets correction conditions. The encoders and other devices built into the servo motors provide real-time feedback on the actual position of the robotic arm's end effector. The feedback position signal is compared with the expected position of the command to calculate the deviation. Once the deviation meets the correction conditions (i.e., exceeds a certain dynamic threshold), a correction command is generated and the operation is re-executed, preventing further erroneous operations or simple alarm stops. This forms a negative feedback loop, significantly improving the success rate of single operations and the overall system reliability and intelligence, meeting the stringent requirements for operational success rates in fire-fighting scenarios.

[0040] This proposed solution can significantly improve the automation and accuracy of fire alarm control panel operation, reduce the intensity of manual duty, and enhance cross-device adaptability. At the same time, it ensures the stability of action execution through a feedback correction mechanism, enabling the system to reliably replace manual labor in remote, hazardous, or unattended scenarios to complete the confirmation, reset, and functional operation of the fire alarm control panel, thus having good practical application value.

[0041] This application also discloses a correction instruction generation process, which, through an intelligent, multi-level evaluation and decision-making process, ensures that the system can promptly detect operational deviations and respond effectively. The process is described in detail below.

[0042] The process of generating the correction instruction is as follows:

[0043] Collect multimodal feedback data from the end effector of the robotic arm. The multimodal feedback data includes position feedback signals, motion timing data, and end effector pressure feedback signals.

[0044] Multimodal feedback data comprehensively perceives the state of the robotic arm in a single operation from multiple physical dimensions. Specifically:

[0045] The position feedback signal comes from the encoder built into the servo motor and is used to provide feedback on the actual spatial position of the robotic arm's end effector, which is directly compared with the command target position.

[0046] The motion timing data records the actual time taken from the issuance of the command to the completion of the action, which is used to determine whether the operation rhythm of the robotic arm meets the preset requirements.

[0047] The end effector pressure feedback signal is provided by a miniature pressure sensor installed at the end, which is used to monitor the actual force applied during pressing or rotating operations.

[0048] By constructing a three-dimensional perception network using multimodal feedback data, it is possible to comprehensively evaluate the accuracy, speed, and appropriateness of the robotic arm's operation, providing a sufficient data foundation for subsequent judgments on the success of the operation.

[0049] Based on the operation type of the servo control command, the corresponding deviation weight coefficient is obtained from the recipe file. The multimodal feedback data is then weighted and fused with the preset standard value to obtain a comprehensive deviation score.

[0050] Depending on the specific operation type, such as button pressing or knob adjustment, preset deviation weighting coefficients are retrieved from the recipe file. For example, for button operation, position and timing may be given higher weights; while for knob adjustment, the accuracy of force control may be more important. Subsequently, the collected multimodal feedback data is compared with various preset standard values ​​in the recipe (such as standard position, standard time, and standard force), calculating the deviation value for each dimension. These deviations are then weighted and fused using the aforementioned weights to ultimately output a normalized comprehensive deviation score. By introducing weighting coefficients, the evaluation criteria can adapt to different operational tasks, avoiding a one-size-fits-all evaluation model. The weighted fusion calculation unifies deviations of different dimensions and importance into a quantifiable score, providing a scientific and unified basis for subsequent correction decisions.

[0051] The overall deviation score is compared with a dynamically adjusted correction threshold. When the score exceeds the threshold, the correction condition is deemed met, and a correction instruction is generated. The correction threshold is dynamically adjusted based on the following criteria:

[0052] Cumulative running time of the robotic arm: Considering that the equipment will experience normal wear and tear with use, the threshold can be appropriately relaxed to reflect the tolerance for equipment aging;

[0053] Historical operation success rate: If the recent operation success rate is high, it indicates a good status, and the threshold can be maintained or tightened to maintain a high standard; otherwise, the threshold should be appropriately relaxed to avoid frequent alarms due to oversensitivity.

[0054] The critical level of the current operation: For critical operations such as "starting up linked equipment", a stricter threshold (i.e., a lower fault tolerance range) is adopted to ensure that nothing goes wrong; for general operations, a relatively lenient threshold can be adopted to improve efficiency.

[0055] The correction threshold is no longer a fixed, rigid value, but a variable that can be intelligently adjusted based on equipment status, historical performance, and task importance. This makes fault tolerance judgments more consistent with actual operating conditions, maintaining high requirements when the equipment is in good condition, while avoiding unnecessary intervention when performance is degraded or non-critical tasks are being performed, thus achieving an optimal balance between reliability and efficiency.

[0056] This application further discloses a weighted fusion calculation method in the process of generating correction instructions; through mathematical processing, physical quantities with different properties and dimensions are unified into a comprehensive score with clear meaning, providing a scientific basis for subsequent correction decisions. The calculation process is described in detail below.

[0057] First, an adaptive acquisition step for weighting coefficients is performed, retrieving the corresponding set of deviation weighting coefficients from the recipe file based on the operation type of the servo control command. It identifies whether the current operation is "button pressing" or "knob adjustment" and, based on this, retrieves the most suitable weighting coefficient from the recipe file preset for this brand's main unit. , , These correspond to the weights of position trajectory deviation, timing action deviation, and force control feedback deviation, respectively, and satisfy the following conditions: The normalization conditions.

[0058] For example, for button operations that require rapid and precise triggering, the recipe may set a position trajectory deviation weighting coefficient. Weighting coefficient for timing action deviation High, force control feedback deviation weighting coefficient The force control feedback deviation weighting coefficient can be set for fine-tuning knob operations. Significantly improved. This allows the deviation assessment criteria to be dynamically adjusted according to the inherent requirements of different operations, realizing task-specific assessment strategies. It avoids the shortcomings of a single fixed weight that cannot take into account different operational characteristics (such as precision priority or force priority), thereby improving the scientificity and effectiveness of the assessment results.

[0059] Subsequently, the standardization process for multi-dimensional deviations is initiated, calculating the standardized deviations for position, timing, and force control respectively. This is a crucial preprocessing step that transforms the original physical quantities into dimensionless, comparable values.

[0060] Position trajectory deviation value The calculation formula is: This formula will use the command position vector. The actual position coordinate vector fed back by the servo motor The Euclidean distance between them (characterizing the actual positional error in space) and the maximum permissible positional deviation preset in the formula for this host. Compare and normalize.

[0061] Timing deviation value The calculation formula is: This formula represents the actual time taken for the robotic arm to complete an action from the issuance of a command. Compared with the standard time preset in the formula The absolute difference, relative to the preset maximum allowable time deviation Standardize it.

[0062] Force control feedback deviation value The calculation formula is: It measures the actual pressing force monitored by the end effector pressure sensor. The recommended pressure level is preset in the formula. The absolute difference, relative to the preset maximum permissible force deviation Standardize it.

[0063] It achieves dimensional unification and fair comparison of multi-source heterogeneous data. By dividing by their respective preset "maximum permissible deviations," it uniformly maps the original deviations of different units (such as millimeters, seconds, and Newtons) and orders of magnitude to a unified scale. Dimensionless values ​​near the interval. Eliminating the influence of dimensions allows for the weighted calculation of three distinct physical quantities—position, time, and force—within the same mathematical framework. Furthermore, it introduces… , , These parameters, which are related to the specific characteristics of the host model, enable the standardization process to be adaptable to different devices, and set reasonable deviation measurement benchmarks for host devices with different operating sensitivities.

[0064] Finally, the weighted fusion and comprehensive score generation steps are performed, and the comprehensive deviation score is calculated using the weighted fusion formula. :

[0065] ;

[0066] In this weighted fusion formula, the standardized deviation values ​​of each dimension obtained in the previous steps ( , , According to the weighting coefficients dynamically obtained from the formula and matched with the operation type ( , , A linear weighted sum is performed, and a single comprehensive score is finally output. This method integrates multi-dimensional deviation information reflecting different aspects of the operation into a single, comprehensive, and quantitative indicator that can characterize the overall quality of a single operation. (Comprehensive Deviation Score) This serves as a direct and unified basis for judging whether an operation is qualified and whether it needs to be corrected, making the subsequent decision-making process simple, clear, and reliable.

[0067] This application further discloses an adaptive adjustment method for the correction threshold during the correction instruction generation process. Through a dynamic model coupled with multiple factors, the correction threshold can intelligently change according to equipment status, historical performance, and task importance, thereby achieving an optimal balance between accuracy, fault tolerance, and reliability. The adjustment process is described in detail below.

[0068] First, consider the impact of equipment operating status on accuracy, using the formula... Calculate the runtime correction factor ;in, It is the cumulative operating time of the robotic arm, used to characterize the degree of aging or wear of the equipment; This is a preset time-related factor, with a value between 0 and 0.2, used to control the sensitivity of equipment aging to threshold adjustments; The preset time base value represents a reasonable equipment life cycle node; runtime correction factor. The calculation formula uses a saturation function design, that is, when the cumulative running time of the robotic arm... Not exceeding At that time, running time correction factor With the cumulative running time of the robotic arm Linear growth reflects compensation for the normal decline in equipment performance; as the cumulative operating time of the robotic arm... Exceed Then, the running time correction factor The decision not to increase the threshold acknowledges the objective law that equipment aging will naturally lead to a decline in operational accuracy, thus appropriately relaxing the threshold to avoid excessively frequent erroneous corrections. At the same time, the saturation design prevents the threshold from being infinitely relaxed after ultra-long-term operation, ensuring the basic accuracy baseline for long-term system operation.

[0069] Secondly, a self-learning mechanism based on historical performance is introduced, through formulas. Calculate the success rate correction factor .in, It is the historical operation success rate calculated based on the most recent preset number of operations (such as the most recent 100 times), reflecting the overall stability of the system in the recent period; This is a success rate influencing factor, ranging from 0 to 0.15, controlling the contribution weight of historical success rates to threshold adjustment. When recent operation success rates are high ( When it approaches 1), Approaching 1, maintaining or adopting a stricter threshold reflects confidence in one's own state; when the success rate decreases ( When (decreases), Increasing the threshold will moderately relax the tolerance scale; it can dynamically adjust the fault tolerance scale according to its recent health status, and adopt a more conservative strategy when the status is not good to reduce unnecessary alarms and corrections, thereby improving the overall operating efficiency and adaptability of the system.

[0070] Furthermore, a differentiated strategy based on operational criticality levels is implemented. This involves determining the criticality level of the current operation by querying the recipe file and assigning different criticality level correction coefficients accordingly. :

[0071] When the critical level is normal operation ;

[0072] When the criticality level is important operation (such as alarm confirmation), ;

[0073] When the critical level is critical operation (such as starting linked equipment), ;

[0074] For critical operations related to the effectiveness of the fire protection system, the system significantly reduces... This allows for setting stricter correction thresholds, meaning that the allowable range of deviations for these operations is smaller, and any minor anomaly will trigger a correction, thereby maximizing the absolute reliability of the core fire protection functions.

[0075] Finally, multi-factor coupling and boundary constraints are applied using formulas. Calculate the final correction threshold The formula uses a preset baseline correction threshold. Using a baseline of (values ​​between 0.1 and 0.3), the correction coefficients for the three dimensions of runtime, historical success rate, and criticality level are coupled by multiplication. This multiplicative model can capture the synergistic effects of multiple factors.

[0076] At the same time, in order to finally correct the threshold Hard boundaries were set The technical effect of this step is to comprehensively integrate all dynamic influencing factors, forming a comprehensive and intelligent decision threshold. The boundary protection mechanism ensures that the system's behavior always stays within the preset safe and effective range, preventing the risk of threshold runaway under extreme parameter combinations (too high leading to missed judgments, too low leading to oversensitivity), and guaranteeing the rationality and stability of decision-making under any operating condition.

[0077] In summary, by intelligently coupling and calculating three key factors—equipment uptime, historical operation success rate, and operation criticality level—a corrected threshold that best suits the current system state and task requirements is dynamically generated. This frees the system from relying on a rigid, fixed threshold, instead providing a self-learning and adjusting threshold, significantly improving its robustness and practicality in complex, long-term real-world fire protection applications.

[0078] This application further discloses information regarding the acquisition of user interface status information and spatial positioning data;

[0079] By deploying sensors in different locations to work together, the system comprehensively and in real-time captures the status information of the fire control panel and the spatial relationship with the robotic arm, laying a solid data foundation for subsequent decision-making and control. The following is a detailed description of this acquisition process.

[0080] In terms of acquiring status information from the user interface, a collaborative sensing architecture using dual industrial cameras was adopted.

[0081] The first sensing operation is performed by an industrial camera fixed to the robotic arm base. Its core task is to collect the display status information of the fire alarm control panel screen. The industrial camera, fixed to the robotic arm base, remains stationary and continuously monitors the dynamic changes of the fire alarm control panel screen. The acquired display status information includes, but is not limited to: alarm codes representing specific fire alarm types, indicator light statuses of display equipment (such as normal, fault, fire alarm, etc.), and text prompts for auxiliary operations. This achieves non-contact, high real-time reading of the fire alarm control panel's electronic information, providing the most direct decision-making basis for determining whether intervention is needed and what kind of operation to perform, essentially simulating the process of on-duty personnel observing the screen and acquiring information.

[0082] The second sensing method is handled by an auxiliary industrial camera mounted on the robotic arm's wrist. Its core task is to identify the physical operating component features on the fire alarm control panel. Because the auxiliary industrial camera moves with the robotic arm, it can observe the control panel up close from the optimal viewing angle. The identified physical operating component features include: the shape of buttons (e.g., round or square, for initial differentiation), the physical location of knobs, and key identification symbols (e.g., text or graphics such as "Silence," "Reset," and "Start"). This gives the robotic arm the ability to navigate, that is, to visually identify the specific physical target that needs to be operated. This complements the fixed camera, together forming a complete visual coverage of the fire alarm control panel's 'screen status' and 'physical panel,' ensuring that the system can both understand the screen information and locate the operating entity.

[0083] For acquiring spatial positioning data, a laser displacement sensor was introduced to provide precise geometric measurements. This sensor measures the relative distance and angle between the robotic arm's end effector and the target operating component on the fire alarm control panel in real time. Unlike visual information, laser measurement provides highly accurate spatial geometric information unaffected by ambient light, directly and quickly determining the spatial relative relationship between the end effector and the target point. This solves the problem of insufficient accuracy in depth information and absolute distance measurement in pure visual positioning. The relative distance and angle data provided by the laser sensor provides a high-precision spatial reference for the robotic arm's motion control, ensuring that the robotic arm can "go the right way and find the right point," which is indispensable key data for achieving precise physical operations (such as pressing and rotating).

[0084] By organically combining a fixed camera, a wrist camera, and a laser displacement sensor, a redundant, complementary, and high-precision sensing system is constructed. The fixed camera macroscopically monitors the screen status, the wrist camera microscopically identifies the target being operated on, and the laser sensor precisely measures the spatial position. Through multi-sensor information fusion, the comprehensiveness, accuracy, and robustness of the system's perception are greatly improved. It utilizes the richness of visual information for identification and judgment, and leverages the precision of laser measurement for positioning and navigation, jointly providing reliable perception support for the core objective of "automatically controlling the fire alarm control panel by a robotic arm instead of manual labor," which is a crucial prerequisite for the accurate and stable operation of the entire solution.

[0085] This application further discloses the process for determining the operational coordinate information; through coordinate system transformation and data fusion algorithms, raw data from different sensors and located in different coordinate systems are unified and optimized into a unique and precise operational command target that the robotic arm can directly execute. The following provides a detailed description of this determination process.

[0086] First, the first step of establishing the basic coordinate system is performed, which involves establishing the coordinate mapping relationship between the robotic arm's base coordinate system and the fire alarm control panel's coordinate system. The robotic arm's base coordinate system is a reference frame describing the robotic arm's own motion, while the fire alarm control panel's coordinate system is a reference frame defined on the control panel. Through pre-calibration of the system, the transformation relationship between these two coordinate systems (usually including rotation and translation) is established. This constructs a unified "spatial language," enabling all subsequent sensing data, regardless of its source, to be represented and processed within the same reference frame (usually the robotic arm's base coordinate system). This provides a prerequisite for the fusion of multi-source data and solves the fundamental problem that data from different coordinate systems cannot be directly compared and used.

[0087] Based on a unified coordinate system, two types of key data are processed in parallel.

[0088] On one hand, the spatial positioning data collected by the laser displacement sensor is converted into the base coordinate system of the robotic arm to obtain the first coordinate data of the robotic arm's end effector. The laser sensor directly measures the relative distance and angle in its sensor coordinate system. Using the known installation relationship between the sensor and the robotic arm base, this high-precision geometric measurement data can be converted into the base coordinate system to form the first coordinate data describing the current position of the end effector. This provides high-precision, real-time spatial geometric perception of the robotic arm's end effector position based on laser ranging. This data is renowned for its excellent absolute accuracy and resistance to light interference.

[0089] On the other hand, the image feature coordinates of the operating parts identified by the industrial camera are transformed into the robot arm's base coordinate system using a hand-eye calibration matrix to obtain the second coordinate data of the target operating part. After the industrial camera (especially a wrist camera) identifies the target button or knob, it obtains the pixel coordinates in its image coordinate system. By using a pre-calibrated hand-eye calibration matrix (which describes the relative pose relationship between the camera and the robot arm's end effector) and combining it with the robot arm's own kinematic model, the target position identified in the image can be transformed into the robot arm's base coordinate system, forming the second coordinate data describing the position of the operating part. This achieves a precise correlation between the visually recognized "what the target is" and its physical "where the target is," providing the target point location of the operational intent.

[0090] Subsequently, the intelligent data fusion and decision-making step is taken, which involves fusing the first coordinate data and the second coordinate data, and calculating the final operation coordinate information through a weighted average algorithm.

[0091] The first coordinate data based on laser positioning and the second coordinate data based on visual recognition are fused using a weighted average algorithm. The key parameters are set as follows: the weighting coefficient for the laser positioning data is... The weighting coefficient of visual recognition data is And satisfy , The value range is 0.6–0.8. This means that in the final result, the more accurate and stable laser positioning data dominates (60%–80%), while visual data, which provides target recognition information, serves as an important supplement (20%–40%). This combines the advantages of both sensing modes while suppressing their respective disadvantages. The high-weighted laser data ensures the absolute accuracy and stability of the final coordinates, avoiding the vulnerability of pure visual positioning to lighting and deformation interference; while the introduction of visual data provides target confirmation and redundancy verification, preventing errors caused by abnormal laser ranging or slight coordinate system drift. This fusion strategy significantly improves the robustness and reliability of the final operational coordinate information.

[0092] Through a coherent technical process from system calibration to independent conversion of multi-source data, and then to weighted fusion, an optimized operating coordinate system is generated that is far superior to any single sensor data, ensuring that the robotic arm can accurately operate the fire-fighting main unit.

[0093] This application further discloses a formula file loading mechanism. Through a complete formula library creation, retrieval, and maintenance system, it encapsulates the operational knowledge of different brand host systems into a flexibly callable digital instruction set, thereby endowing the robotic arm system with unprecedented versatility and flexibility. The loading process is described in detail below.

[0094] First, a recipe library management interface is provided, the core of which is a recipe library storing recipe files containing the operating logic of multiple brands of fire alarm control panels. Instead of writing independent control programs for each fire alarm control panel, the operating logic of all brands of control panels is managed uniformly in a single library as standardized digital assets of recipe files. This creates a scalable recipe library, centralizing and digitally encapsulating previously scattered and fixed operating experience, laying the foundation for the system's one-to-many adaptability, and fundamentally changing the traditional solution's one-machine-one-code, incompatible approach.

[0095] At the operational level, the target host is designated through a highly simplified user interaction. Specifically, the user receives the target fire alarm control panel brand command via a brand selection button provided on the host computer interface. Operators do not require professional programming knowledge or modification of any underlying control code; they simply click the corresponding brand button on the graphical interface to switch the target configuration file. This significantly reduces the system's usage threshold and operational complexity, enabling one-click switching. It makes migrating the system between different brand fire alarm control panels quick and easy, effectively solving deployment difficulties caused by insufficient personnel technical capabilities or cumbersome program modifications, and enhancing the system's practicality and accessibility.

[0096] Once the user issues a command, the core formula is intelligently retrieved, meaning the formula file corresponding to the selected brand is called from the formula library. Crucially, the formula file is stored in a structured data format, with core fields including:

[0097] Operating component coordinate field: Stores the "logical coordinates" of each operating component (such as buttons and knobs) on the fire alarm control panel; this is a position reference relative to the coordinate system of the control panel, and serves as the benchmark for subsequent real-time coordinate calibration.

[0098] The operation sequence field stores a logically arranged sequence of operation steps necessary to complete a specific fire protection function (such as "alarm confirmation and activation of the linked fan"). This is equivalent to digitally recording the operation process of skilled operators.

[0099] The parameter threshold field stores various parameters that ensure accurate and safe operation, such as "knob adjustment angle", "button press duration", and "operation timeout".

[0100] Through structured data design, all the knowledge (location, process, parameters) required to operate a brand's main unit is clearly and unambiguously defined in a recipe file. This allows the robotic arm control system to accurately reproduce the standard operating procedures for that brand's main unit, just like reading an instruction manual, achieving a perfect decoupling between the software definition of the control logic and the hardware execution.

[0101] Finally, the recipe library supports adding new branded recipes or modifying existing recipes via a recipe editing interface. This interface is open to authorized users, allowing them to add or modify recipes at any time based on new brand mainframes encountered on-site or updates to the operating logic of existing mainframes. This endows the recipe library with self-evolution and adaptability, ensuring it won't become obsolete due to product iterations or the emergence of new brands. Users don't need to replace expensive robotic arms or control hardware; the system can acquire new capabilities simply through software-level recipe updates, significantly extending the system's technical lifecycle and reducing long-term use and maintenance costs.

[0102] In summary, the aforementioned formula file loading method, through its end-to-end design encompassing knowledge base construction, one-click invocation, structured data parsing, and sustainable expansion, constitutes a complete, efficient, and future-oriented multi-brand adaptation solution. This is key to the solution's broad applicability and is also the core of its ability to establish technological barriers and protect independent intellectual property rights in market competition.

[0103] This application also provides a process for generating servo control commands. Through a multi-stage parsing and synthesis process, it achieves accurate translation from abstract tasks to specific execution, ensuring the intent and reliability of the operation. The following is a detailed description of this generation process.

[0104] First, the system enters the operation command reception and identification phase, which involves receiving operation commands, including alarm confirmation commands, equipment start / stop commands, or system reset commands. These operation commands are trigger signals that initiate specific operation procedures. Their source can be an automatically detected alarm signal from the fire alarm control panel or a remote control command from a remote monitoring center. The type of command clearly indicates the category of fire protection function to be executed. This provides a clear objective and starting point for the entire control process, enabling a switch from standby monitoring mode to specific task execution mode, achieving external controllability and event-driven behavior of the system.

[0105] Upon receiving an instruction, the system executes an intelligent matching and extraction phase of the operational logic. This involves extracting the corresponding operational sequence and parameter thresholds from the formula file based on the type of instruction. Depending on the specific type of instruction, such as an alarm confirmation instruction or system reset instruction, the system locates and extracts the necessary operational sequence to complete the instruction from the currently loaded brand formula file, much like looking up a dictionary. For example, for alarm confirmation, the sequence might be: move to the mute button → press → move to the alarm confirmation button → press; along with relevant parameter thresholds (such as press duration, movement speed, etc.). This dynamic association of standardized operational knowledge for specific brand hosts stored in the formula with the current specific task requirements ensures the logical correctness and brand adaptability of subsequent actions.

[0106] Next, the spatial path planning and analysis phase begins. Based on the operational coordinate information, the sequence of operations is analyzed into the robotic arm's path planning logic, which includes the starting position, target position, and motion trajectory. In this step, the operational sequence extracted from the previous step, based on logical steps, is combined with the precise operational coordinate information obtained through real-time calibration. The analysis engine concretizes the logical step of "pressing the 'alarm confirmation' button" into a series of motion commands for the robotic arm in space, forming a complete path planning logic that includes the starting position (such as the end point of the previous operation or a safety waiting point), the target position (i.e., the real-time operational coordinates of the 'alarm confirmation' button), and the motion trajectory connecting the two points (such as a straight line or circular interpolation path used to avoid collisions). This transforms the abstract operational process into an executable, collision-free, and efficient motion path in physical space, ensuring the smoothness and safety of the robotic arm's movements.

[0107] Finally, the synthesis stage of the execution control commands involves combining the path planning logic with parameter thresholds to generate servo control commands that the servo motor can execute. This is the final step in servo control command generation, deeply integrating the path planning logic containing spatial path information with the parameter thresholds extracted from the formula to synthesize servo control commands that directly drive hardware actions. These commands are a language that the underlying servo motors and end effectors can directly recognize. They not only contain the target position and motion trajectory but also integrate the fine operational requirements specified in the formula, such as: the speed of movement, the amount of force applied when pressing a button, and the duration of the pressing action. This generates optimal control commands that combine spatial accuracy and operational flexibility, ensuring that the robotic arm not only moves to the correct position but also completes the operation with just the right force and manner. This satisfies the differentiated requirements of various operating components of the fire alarm control panel, avoiding equipment damage caused by excessive force or improper timing, and also simulates the skilled techniques of experienced operators, ultimately achieving precise, reliable, and flexible automated control of the fire alarm control panel.

[0108] Through a coherent and sophisticated processing flow from instruction recognition, logic matching, path parsing to instruction synthesis, it is ensured that firefighting operation tasks can be transformed into precise and safe physical movements of the underlying robotic arm.

[0109] Regarding servo control commands, their types are further disclosed, specifically including: position control commands, force control commands, and force control commands.

[0110] Position control commands specify the target position and speed of the servo motor. The target position, derived from precise spatial coordinates obtained after coordinate calibration and path planning, ensures that the end effector of the robotic arm accurately reaches directly above the intended operating component (such as a specific button or knob). The speed controls how quickly the robotic arm moves from its current position to the target position. This solves the problem of precise positioning during operation. Simultaneously, fine-grained control of the speed allows for deceleration and buffering when approaching the target, preventing damage to the fire alarm control panel due to high-speed impact, and maintaining high efficiency during long-distance movements, thus achieving an optimal balance between operational efficiency and equipment safety.

[0111] Force control commands specify the pressing force or rotational torque of the end effector. For button operations, it specifies the amount of force the actuator should apply; for knob operations, it specifies the amount of torque to output for rotation. The required force or torque value is directly derived from the parameter threshold preset for that specific operating component in the formulation file. This solves the problem of "appropriate force" in operation. The components on the fire alarm control panel are diverse; some require gentle touch (such as membrane buttons), while others require greater torsional force (such as metal knobs). Through programmable force control, the technique of an experienced operator can be simulated to complete the operation with the most appropriate force, fundamentally avoiding ineffective operation due to insufficient force or physical damage to equipment due to excessive force, greatly improving the success rate of operation and system safety.

[0112] The timing control instructions specify the execution time and intervals for each action step. They define the duration of each action (such as pressing or rotating) and the time interval between consecutive actions. For example, pressing a button requires 500 milliseconds to ensure the host computer responds, and after each press, a 200-millisecond wait is required for the host screen to refresh before executing the next operation. This solves the sequential problem in the operation process. The fire alarm control panel requires a certain amount of time to process data; operating too quickly may cause it to fail to respond, while operating too slowly will affect the efficiency of the response. The timing control instructions ensure that the robotic arm's operating rhythm perfectly matches the response characteristics of the fire alarm control panel, making the entire operation sequence orderly. This guarantees that each step can be effectively recognized and processed by the host computer, and also ensures the timeliness of the overall operation process in emergency fire scenarios.

[0113] like Figure 3 As shown, this application further discloses:

[0114] A remote control system for fire suppression systems based on robotic arms, through the collaborative operation of six core functional modules, constructs a complete, efficient, and reliable automated control solution. The technical features and effects of each module are described in detail below.

[0115] The sensing and acquisition module is responsible for acquiring two types of key information: first, the status information of the fire alarm control panel's operating interface; and second, spatial positioning data that characterizes the relative position of the robotic arm's end effector and the fire alarm control panel. Specifically, this module uses various sensors, including a fixed camera, a wrist camera, and a laser displacement sensor, to achieve comprehensive perception of the control panel's screen status, physical component characteristics, and spatial geometric relationships.

[0116] The coordinate calibration module fuses and calibrates the operation interface status information acquired by the sensing and acquisition module with spatial positioning data, ultimately outputting precise operation coordinate information of the target operating components on the fire alarm control panel. This module organically combines visual recognition information with laser ranging data by establishing a unified coordinate system and employing data fusion algorithms (such as weighted averaging).

[0117] The recipe management module stores, manages, and loads recipe files corresponding to different target fire alarm control panel brands. Recipe files use a structured data format and include at least the coordinates of operating components, the operating sequence, and parameter thresholds. This module provides a user-friendly management interface and API calls.

[0118] The instruction parsing module is responsible for receiving external operation instructions and performing comprehensive parsing based on these instructions, the recipe file retrieved from the recipe management module, and the operation coordinate information obtained from the coordinate calibration module. This module first matches the operation instruction with the operation sequence in the recipe, then plans a specific motion path using the coordinate information, and finally generates servo control instructions executable by the servo motor.

[0119] The mechanical execution module, as the final execution unit of the instructions, is responsible for sending the servo control instructions generated by the instruction parsing module to the servo drive system of the robotic arm. It drives the end effector of the robotic arm to complete precise physical actions such as pressing, rotating, or resetting according to the instructions.

[0120] The feedback correction module acquires the position feedback signal from the robotic arm's servo motor in real time and compares it with the expected value of the command to calculate the operational deviation. Once the deviation meets the correction conditions, it immediately generates a correction command and controls the mechanical execution module to re-execute the corresponding action.

[0121] The aforementioned remote control system for a fire alarm control panel based on a robotic arm also includes a communication module and a power supply module.

[0122] The communication module includes a wired communication unit and a wireless communication unit.

[0123] The wired communication unit uses an Ethernet switch to connect the remote control system and the fire alarm control panel (adapting the fire alarm control panel's communication interface through a protocol converter), reads internal alarm information and operating parameters from the fire alarm control panel, and ensures stable data transmission.

[0124] The wireless communication unit uses industrial-grade wireless communication to enable communication between the remote control system and the remote monitoring center and firefighters' handheld terminals, supporting remote recipe modification and fault intervention, and improving the remote control system's remote management capabilities.

[0125] Dual-mode information interaction: Information from the fire alarm control panel can be obtained independently through "screen vision" or "direct communication", and dual-mode cross-verification (such as consistency verification between the alarm area on the screen and the alarm area read directly) can also be performed to improve the accuracy of information.

[0126] The power module adopts a dual power supply mode. The main power supply is the emergency circuit (220V AC) in the fire control room, and the backup power supply is an energy storage battery pack. It has the ability to switch quickly and automatically, ensuring that there is no risk of power failure for core components such as the remote control system and robotic arm, and ensuring the continuous operation of the system.

[0127] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for remotely controlling a fire alarm control panel based on a robotic arm, characterized in that: Includes the following steps: Acquire the status information of the fire control panel's operating interface and the spatial positioning data used to characterize the relative position of the end effector of the robotic arm and the fire control panel; The operation interface status information and the spatial positioning data are subjected to coordinate calibration to obtain the operation coordinate information of the target operation component on the fire control panel. Load the pre-stored recipe file corresponding to the target fire control panel, wherein the recipe file includes at least the coordinates of the operating components, the operating sequence, and parameter thresholds; The operation instructions are obtained, and the action logic of the robotic arm is parsed based on the operation instructions, the recipe file, and the operation coordinate information. The action logic is then converted into servo control instructions executed by the servo motor. The servo control command is sent to the robotic arm, causing the end effector of the robotic arm to perform pressing, rotating, or resetting actions according to the servo control command; The system acquires the feedback position signal from the robotic arm's servo motor, and generates a correction command and re-executes the corresponding action when the feedback deviation meets the correction conditions.

2. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 1, characterized in that: The process of generating the correction instruction is as follows: Collect multimodal feedback data from the end effector of the robotic arm, including position feedback signals, motion timing data, and end effector pressure feedback signals; Based on the operation type of the servo control command, the corresponding deviation weight coefficient is obtained from the recipe file. The multimodal feedback data is then weighted and fused with the preset standard value to obtain a comprehensive deviation score. The comprehensive deviation score is compared with a dynamically adjusted correction threshold. When the score exceeds the threshold, the correction condition is determined to be met, and a correction instruction is generated. The correction threshold is adaptively adjusted based on the robotic arm's cumulative running time, historical operation success rate, and the criticality level of the current operation.

3. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 2, characterized in that: The weighted fusion calculation is achieved through the following steps: Based on the operation type of the servo control command, obtain the corresponding set of deviation weight coefficients from the recipe file. ,in: This is the weighting coefficient for position trajectory deviation. This is the weighting coefficient for timing action deviation. Let be the force control feedback deviation weighting coefficient, and satisfy: ; Calculate the standardized deviation values ​​for each dimension separately, where: Position trajectory deviation value : ;in, The command position coordinate vector, This is the actual position coordinate vector fed back by the servo motor. This is the preset maximum allowable position deviation; Timing deviation value : ;in, This refers to the actual time taken for the robotic arm to complete an action from the issuance of a command. The standard time preset in the formula, This is the preset maximum allowable time deviation; Force control feedback deviation value : ;in, The actual pressing force monitored by the pressure sensor of the end effector. The recommended pressure level is preset in the formula. This is the preset maximum allowable force deviation; The comprehensive deviation score is calculated using a weighted fusion formula. : 。 4. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 2, characterized in that: The adaptive adjustment process of the correction threshold includes: Obtain the cumulative running time of the robotic arm And based on the cumulative running time of the robotic arm Calculate the runtime correction factor : ;in, The preset time-related factors, The preset time base value; Obtain the success rate of the most recent preset number of operations and calculate the historical operation success rate. Based on historical operation success rate Calculate the success rate correction factor : ;in, As a factor affecting success rate; Obtain the critical level of the current operation from the recipe file based on the current operation type, and determine the critical level correction factor based on the critical level. Key level correction coefficient The rules for determining the value are as follows: When the critical level is normal operation ; When the critical level is important operation ; When the criticality level is critical operation. ; Based on time correction factor Success rate correction coefficient Key level correction coefficient Update final correction threshold : ； in, The preset base correction threshold has a range of values. ; The range of values ​​is limited to ,in , .

5. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 1, characterized in that: The process of obtaining the operation interface status information is as follows: The display status information of the fire alarm control panel screen is collected by an industrial camera fixed to the base of the robotic arm. The display status information includes alarm codes, indicator light status, and text prompts. The physical operating component features on the fire control panel are identified by an auxiliary industrial camera installed on the wrist of the robotic arm. These physical operating component features include button shapes, knob positions, and symbolic elements. The process of acquiring the spatial positioning data is as follows: The relative distance and angle between the end effector of the robotic arm and the target operating component on the fire alarm control panel are measured in real time using a laser displacement sensor.

6. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 5, characterized in that: The process for determining the operation coordinate information is as follows: Establish the coordinate mapping relationship between the base coordinate system of the robotic arm and the coordinate system of the fire alarm control panel; The spatial positioning data collected by the laser displacement sensor is converted to the base coordinate system of the robotic arm to obtain the first coordinate data of the robotic arm end effector. The coordinates of the image features of the operating parts identified by the industrial camera are transformed into the base coordinate system of the robotic arm through a hand-eye calibration matrix to obtain the second coordinate data of the target operating parts. The first coordinate data and the second coordinate data are fused together, and the final operation coordinate information is calculated by a weighted average algorithm. Wherein, the weighting coefficient of the laser positioning data in the weighted average algorithm is The weighting coefficient of visual recognition data is And satisfy , The value range is 0.6 to 0.

8.

7. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 1, characterized in that: The pre-stored recipe file corresponding to the target fire control panel includes: A formula library management interface is provided, wherein the formula library stores the formula files for the operation logic of fire alarm control panels from multiple brands; The system receives the target fire alarm control panel brand command selected by the user through the brand selection button provided on the host computer interface. The recipe file corresponding to the selected brand is retrieved from the recipe library. The recipe file is stored in a structured data format and includes: Operating component coordinate field: Stores the logical coordinates of each operating component on the fire alarm control panel; Operation sequence field: Stores the sequence of operation steps required to complete a specific function; Parameter threshold field: Stores parameters including knob adjustment angle, button press duration, and operation timeout time; The formula library supports adding new brand formulas or modifying existing formulas through the formula editing interface.

8. The method for remotely controlling a fire alarm control panel based on a robotic arm according to claim 1, characterized in that: The process of generating the servo control commands is as follows: Receive operation instructions, including alarm confirmation instructions, equipment start / stop instructions, or system reset instructions; Based on the type of operation instruction, extract the corresponding operation sequence and parameter threshold from the recipe file; Based on the operation coordinate information, the operation sequence is parsed into the path planning logic of the robotic arm, which includes the starting position, the target position, and the motion trajectory. By combining path planning logic with parameter thresholds, servo control commands that can be executed by the servo motor are generated.

9. A remote control method for a fire alarm control panel based on a robotic arm according to claim 8, characterized in that: The servo control commands include: Position control commands: specify the target position and speed of the servo motor; Force control command: Specifies the pressing force or rotational torque of the end effector; Timing control instructions: specify the execution time and interval of each action step.

10. A remote control system for a fire alarm control panel based on a robotic arm, characterized in that: include: The sensing and acquisition module is used to acquire the status information of the fire control panel's operating interface and the spatial positioning data used to characterize the relative position of the end effector of the robotic arm and the fire control panel. The coordinate calibration module is used to perform coordinate calibration processing on the operation interface status information and the spatial positioning data to obtain the operation coordinate information of the target operation component on the fire control panel; The recipe management module is used to load pre-stored recipe files corresponding to the target fire control panel. The recipe files include at least the coordinates of the operating components, the operating sequence, and parameter thresholds. The instruction parsing module is used to acquire operation instructions, parse the operation instructions, the recipe file, and the operation coordinate information to obtain the motion logic of the robotic arm, and convert the motion logic into servo control instructions executed by the servo motor. The mechanical execution module is used to send the servo control commands to the robotic arm, so that the end effector of the robotic arm performs pressing, rotating or resetting actions according to the servo control commands; The feedback correction module is used to acquire the feedback position signal of the robotic arm's servo motor, and generate a correction command and re-execute the corresponding action when the feedback deviation meets the correction conditions.

Images