Mechanical arm control system and method for emergency rescue

Abstract

The invention relates to the technical field of mechanical arm control, and discloses a mechanical arm control system and method for emergency rescue, and the method comprises the steps: obtaining the surrounding environment information of a rescue arm, and building a construction model according to the environment information; and acquiring state information of the to-be-rescued target, and evaluating a crisis score of the to-be-rescued target according to the state information. And acquiring communication data between the rescue arm and the control end, and determining a communication score according to the communication data. And according to the crisis score and the communication score, determining an adjustment coefficient, and according to the adjustment coefficient, adjusting a preset receiving frequency of a control instruction sent by the rescue arm receiving control end. And the real-time receiving frequency of the control instruction sent by the rescue arm receiving control end is obtained, and whether the rescue arm conducts self rescue or not is determined according to the relation between the real-time receiving frequency and the adjusted preset receiving frequency. According to the invention, the operation mode of the rescue arm is dynamically adjusted, so that the rescue task can be efficiently and accurately completed under different environments and communication conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of robot arm control, and in particular to a robot arm control system and method for emergency rescue. Background Art

[0002] Emergency rescue missions usually take place in complex, dangerous and unpredictable environments, such as earthquakes, fires, mining disasters and other disaster scenarios. The complexity and harsh conditions of these environments pose huge challenges to traditional manual rescue. First, disaster sites are often full of ruins or unstable structures, and it is difficult for manual rescuers to quickly enter the disaster area in a safe manner, which greatly prolongs the rescue time. Secondly, in extreme environments, the safety of rescuers is always the most important consideration, and excessive reliance on manual rescue will increase the risk of casualties. To this end, the use of automated equipment, especially robotic arms, has become an effective means to solve these problems.

[0003] However, the control of traditional robotic arms currently faces many technical challenges. Especially in post-disaster environments, communication facilities are often destroyed or severely restricted, and real-time and efficient command transmission cannot be guaranteed, which may cause the robotic arm to be unable to accurately perform rescue tasks. Since the time from receiving commands to executing tasks is usually very short, the instability of communication will significantly reduce its work efficiency and may cause mission failure. Secondly, in earthquakes, fires or other disasters, the environment in the disaster area will change dramatically, buildings may collapse, roads may be blocked, and the position and status of targets (such as trapped people, objects or equipment) may also change. This requires the robotic arm to be able to adapt to the changing environment and respond quickly based on real-time information when performing tasks. The traditional robotic arm control method relies on high-frequency command transmission and manual intervention, which cannot achieve flexible response and immediate response in complex environments.

[0004] Therefore, there is an urgent need to invent a control technology for a robotic arm for emergency rescue to solve the challenges faced by traditional robotic arm control in post-disaster environments, such as unstable communications and drastic environmental changes, which make it unable to flexibly respond to complex situations and efficiently complete rescue missions. Summary of the invention

[0005] In view of this, the present invention proposes a robotic arm control system and method for emergency rescue, aiming to solve the problem that the robotic arm control in the current technology faces challenges of unstable communication and drastic environmental changes in post-disaster environments, resulting in its inability to flexibly respond to complex situations and efficiently complete rescue missions.

[0006] The present invention proposes a method for controlling a mechanical arm for emergency rescue, which is applicable to a grabbing type rescue mechanical arm, comprising:

[0007] Acquire the surrounding environment information of the rescue arm, and establish a structural model according to the environmental information;

[0008] Acquire status information of the target to be rescued, and evaluate the crisis score of the target to be rescued based on the status information;

[0009] Acquire communication data between the rescue arm and the control end, and determine a communication score based on the communication data;

[0010] Determine an adjustment coefficient according to the crisis score and the communication score, and adjust a preset receiving frequency at which the rescue arm receives the control command sent by the control end according to the adjustment coefficient;

[0011] The real-time receiving frequency of the rescue arm receiving the control command sent by the control end is obtained, and according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, it is determined whether the rescue arm performs rescue by itself, wherein:

[0012] If the real-time receiving frequency is lower than or equal to the preset receiving frequency, it is determined that the rescue arm performs rescue according to the received control instruction;

[0013] If the real-time receiving frequency is higher than the preset receiving frequency, the rescue path is determined according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and the rescue arm is controlled to perform autonomous rescue according to the rescue path until the real-time receiving frequency is lower than or equal to the preset receiving frequency, and the rescue arm is controlled to perform rescue according to the control instructions.

[0014] Furthermore, obtaining the status information of the target to be rescued and evaluating the crisis score of the target to be rescued according to the status information includes:

[0015] Acquire the ambient temperature, ambient humidity, oxygen concentration, dangerous gas concentration and ambient structural vibration amplitude of the target to be rescued in the status information;

[0016] Obtaining the respiratory rate, real-time body temperature, and facial image of the target to be rescued in the status information;

[0017] Substituting the facial image into a pre-established age model to obtain the age of the target to be rescued;

[0018] Substitute the ambient temperature, ambient humidity, oxygen concentration, dangerous gas concentration and ambient structural vibration amplitude into Formula I to obtain the environmental score of the target to be rescued, wherein Formula I is as follows:

[0019] ;

[0020] Wherein, E is the environmental score, T is the ambient temperature, and Tmin is the safety temperature starting degree, T max is the safety temperature termination degree, H is the ambient humidity, △H is the preset safety humidity, H min is the starting value of safe humidity, H max is the safe humidity termination value, O is the dangerous gas concentration, △O is the safe oxygen concentration, G is the dangerous gas concentration, △G is the safe dangerous gas concentration, V is the environmental structure vibration amplitude, V max is the maximum risk value of the vibration amplitude, w 1 -w 5 is the first weight coefficient, and w 1 -w 5 All are not zero;

[0021] Substitute the respiratory rate, real-time body temperature and age of the target to be rescued into Formula II to obtain the status score of the target to be rescued, where Formula II is as follows:

[0022] ;

[0023] Wherein, R is the respiratory frequency, r is the center value of the preset safe respiratory frequency range, △R is the preset safe respiratory frequency range, P is the real-time body temperature, p is the center value of the preset safe body temperature range, △P is the preset safe body temperature range, Q is the age of the target to be rescued, q is the center range of the preset healthy age range, △Q is the preset healthy age range, z 1 -z 3 is the second weight coefficient, and z 1 -z 3 All are not zero;

[0024] Substitute the environmental score and the state score of the target to be rescued into Formula III to determine the crisis score of the target to be rescued, where Formula III is as follows:

[0025] Y = c1·E+c2·S;

[0026] Wherein, Y is the crisis score, S is the status score of the target to be rescued, E is the environment score of the target to be rescued, c1 and c2 are third weight coefficients, and both c1 and c2 are not zero.

[0027] Furthermore, when establishing the age model in advance, it includes:

[0028] Acquire image information of each age group, extract facial features in each image, and establish an age feature relationship according to the facial features, the age group and the image information;

[0029] Obtaining a distance metric for each age characteristic relational expression in the age group, and establishing a distance matrix according to the distance metric;

[0030] The age-feature relationship formulas are recursively merged according to the distance matrix, and the age-feature relationship formulas of the age groups after the recursive merging are obtained to establish the age model.

[0031] Further, determining the communication score according to the communication data includes:

[0032] The data transmission delay, transmission rate and data packet loss rate in the communication data are obtained, and the data transmission delay, transmission rate and data packet loss rate are substituted into formula IV to determine the communication score, wherein the formula IV is as follows:

[0033] ;

[0034] Wherein, M is the communication score, is the data transmission delay, F is the transmission rate, and F max is the preset transmission rate, J is the data packet loss rate, J max is the preset data packet loss rate, a 1 -a 3 is the fourth weight coefficient, and a 1 -a 3 All are not zero.

[0035] Furthermore, when determining the adjustment coefficient according to the crisis score and the communication score, it includes:

[0036] Determining the adjustment coefficient according to a relationship between the crisis score and a pre-configured preset crisis score;

[0037] When the crisis score is less than the preset crisis score, the adjustment coefficient is determined to be △L0;

[0038] When the crisis score is greater than or equal to the preset crisis score, the adjustment coefficient is determined according to a crisis score difference between the crisis score and the preset crisis score.

[0039] Furthermore, when determining the adjustment coefficient according to the crisis score difference between the crisis score and the preset crisis score, it includes:

[0040] Determine the adjustment coefficient according to the relationship between the crisis score difference and a pre-configured first preset crisis score difference and a second preset crisis score difference;

[0041] When the crisis score difference is less than the first preset crisis score difference, the adjustment coefficient is determined to be △L1;

[0042] When the crisis score difference is greater than or equal to the first preset crisis score difference and less than the second preset crisis score difference, determine that the adjustment coefficient is △L2;

[0043] When the crisis score difference is greater than or equal to the second preset crisis score difference, determine that the adjustment coefficient is △L3;

[0044] Wherein, the first preset crisis score difference is less than the second preset crisis score difference, and △L0 < 1 < △L1 < △L2 < △L3.

[0045] Further, when determining that the adjustment coefficient is △Li, i = 0, 1, 2, 3, it includes:

[0046] Determine whether to correct the adjustment coefficient △Li according to the relationship between the communication score and the pre-configured preset communication score:

[0047] When the communication score is greater than or equal to the preset communication score, determine not to correct the adjustment coefficient △Li;

[0048] When the communication score is less than the preset communication score, determine the correction coefficient according to the communication score difference between the communication score and the preset communication score, and correct the adjustment coefficient △Li according to the correction coefficient.

[0049] Further, when determining the correction coefficient according to the communication score difference between the communication score and the preset communication score, it includes:

[0050] Determine the correction coefficient according to the relationship between the communication score difference and the pre-configured first preset communication score difference and second preset communication score difference;

[0051] When the communication score difference is less than or equal to the communication score difference, determine that the correction coefficient is △K3;

[0052] When the communication score difference is greater than the first preset communication score difference and less than or equal to the second preset communication score difference, determine that the correction coefficient is △K2;

[0053] When the communication score difference is greater than the second preset communication score difference, determine that the correction coefficient is △K1;

[0054] Wherein, the first preset communication score difference is less than the second preset communication score difference, and 1 < △K1 < △K2 < △K3.

[0055] Furthermore, when determining the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, it includes:

[0056] Obtaining the density of obstacles in the current path of the rescue arm and the radius of the rescue arm according to the constructed model, and determining the complexity of the current path of the rescue arm according to the density of obstacles and the radius of the rescue arm;

[0057] Acquire the crisis score of each of the targets to be rescued in the current path of the rescue arm, and determine the rescue cost of each of the targets to be rescued according to the crisis score of the targets to be rescued and the complexity of the current path;

[0058] The rescue costs of the targets to be rescued are sorted in positive order, and the rescue cost ranked first is determined as the rescue path.

[0059] Compared with the prior art, the beneficial effect of the present invention is that by dynamically adjusting the frequency of receiving control instructions of the rescue manipulator, the problem of unstable communication in the post-disaster environment can be effectively dealt with. When communication is restricted, the manipulator can independently decide whether to continue the mission based on the real-time receiving frequency and the target's crisis score, and flexibly adjust the rescue path, thereby improving the rescue efficiency and the autonomous adaptability of the manipulator. This method not only optimizes the operational flexibility of the manipulator, but also ensures that the rescue mission can proceed smoothly even in complex environments, reducing the reliance on real-time communication.

[0060] On the other hand, the present application also provides a manipulator control system for emergency rescue, which is configured on a grabbing rescue manipulator, and which adopts a manipulator control method for emergency rescue as described above, including:

[0061] An information collection module, which is equipped with a sound wave detection unit, and is used to obtain the surrounding environment information of the rescue arm;

[0062] A model building module, electrically connected to the information acquisition module, and configured to build a construction model according to the environmental information;

[0063] A first evaluation module is configured with an infrared detection unit and an image acquisition unit, and is used to obtain status information of a target to be rescued and evaluate a crisis score of the target to be rescued according to the status information;

[0064] A second evaluation module is electrically connected to the control end of the rescue arm, and is used to obtain communication data between the rescue arm and the control end, and determine a communication score according to the communication data;

[0065] The central control module is electrically connected to the control end of the rescue arm, the first evaluation module, and the second evaluation module respectively. The central control module is used to determine an adjustment coefficient according to the crisis score and the communication score, and adjust a preset reception frequency at which the rescue arm receives a control instruction sent by the control end according to the adjustment coefficient. The central control module is further used to obtain a real-time reception frequency at which the rescue arm receives a control instruction sent by the control end, and determine whether the rescue arm performs self-rescue according to the relationship between the real-time reception frequency and the adjusted preset reception frequency, where:

[0066] If the real-time reception frequency is lower than or equal to the preset reception frequency, the central control module determines that the rescue arm performs rescue according to the received control instruction;

[0067] If the real-time reception frequency is higher than the preset reception frequency, the central control module determines a rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and controls the rescue arm to perform autonomous rescue according to the rescue path until the real-time reception frequency is lower than or equal to the preset reception frequency, and then controls the rescue arm to perform rescue according to the control instruction.

[0068] It can be understood that the mechanical arm control system and method for emergency rescue in the above embodiments of the present invention have the same beneficial effects, which will not be elaborated herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0070] Figure 1 is a flowchart of a mechanical arm control method for emergency rescue provided by an embodiment of the present invention;

[0071] Figure 2 is a functional block diagram of a mechanical arm control system for emergency rescue provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0073] As Figure 1 shown, in some embodiments of the present application, this embodiment provides a robotic arm control method for emergency rescue, including:

[0074] Step S100: Obtain the circumferential environment information of the rescue arm and establish a structural model according to the environment information.

[0075] It can be understood that by obtaining the information of the environment around the rescue arm and using this information to construct a structural model, it helps the robotic arm understand the environmental characteristics around it. These environmental information includes factors such as obstacles, spatial layout, terrain changes, etc. By establishing a structural model, the robotic arm can accurately perceive and evaluate the environmental situation, thereby providing a basis for subsequent decision-making and path planning.

[0076] Step S200: Obtain the status information of the target to be rescued and evaluate the crisis score of the target to be rescued according to the status information.

[0077] Specifically, when obtaining the status information of the target to be rescued and evaluating the crisis score of the target to be rescued according to the status information, it includes: obtaining the environmental temperature, environmental humidity, oxygen concentration, dangerous gas concentration, and environmental structure vibration amplitude of the target to be rescued in the status information. Obtaining the breathing frequency, real-time body temperature, and facial image of the target to be rescued in the status information. Substituting the facial image into the pre-established age model to obtain the age of the target to be rescued. Substituting the environmental temperature, environmental humidity, oxygen concentration, dangerous gas concentration, and environmental structure vibration amplitude into Formula I to obtain the environmental score of the target to be rescued, where Formula I is as follows:

[0078] .

[0079] where, E is the environmental score, T is the environmental temperature, T min is the starting degree of safe temperature, T max is the ending degree of safe temperature, H is the environmental humidity, △H is the preset safe humidity, H min is the starting value of safe humidity, H maxis the safe humidity termination value, O is the dangerous gas concentration, △O is the safe oxygen concentration, G is the dangerous gas concentration, △G is the safe dangerous gas concentration, V is the environmental structural vibration amplitude, V max is the maximum risk value of the vibration amplitude, w 1 -w 5 is the first weight coefficient, and w 1 -w 5 The respiratory rate, real-time body temperature and age of the target to be rescued are substituted into Formula II to obtain the status score of the target to be rescued, where Formula II is as follows:

[0080] .

[0081] Where R is the respiratory rate, r is the center value of the preset safe respiratory rate range, △R is the preset safe respiratory rate range, P is the real-time body temperature, p is the center value of the preset safe body temperature range, △P is the preset safe body temperature range, Q is the age of the target to be rescued, q is the center range of the preset healthy age range, △Q is the preset healthy age range, z 1 -z 3 is the second weight coefficient, and z 1 -z 3 The environmental score and the state score of the target to be rescued are substituted into Formula III to determine the crisis score of the target to be rescued, where Formula III is as follows:

[0082] Y=c1·E+c2·S.

[0083] Among them, Y is the crisis score, S is the status score of the target to be rescued, E is the environment score of the target to be rescued, c1 and c2 are the third weight coefficients, and both c1 and c2 are not zero.

[0084] It can be seen that by obtaining factors such as ambient temperature, humidity, oxygen concentration, concentration of dangerous gases, and vibration amplitude, and substituting them into formula I, the environmental score of the target to be rescued is calculated. These environmental factors reflect the degree of danger of the target's environment and can reflect the impact of the environment on the target's survival status. Secondly, by obtaining the target's physiological status information such as breathing rate, body temperature, and facial image, combined with the preset safety range, the target's status score is calculated by formula II, and the score is further optimized according to the target's age information. Finally, by substituting the environmental score and status score into formula III, combined with the weight coefficient, the target's crisis score is calculated. This score comprehensively considers the target's environment and the target's own health status, and can then accurately assess the target's survival risk in real time, thereby providing a decision-making basis for the robot arm to formulate subsequent rescue strategies.

[0085] It is understandable that the calculation of the environmental score evaluates the danger of the target's environment through multiple key parameters, including temperature, humidity, oxygen concentration, concentration of hazardous gases, and vibration amplitude of environmental structures. These factors directly affect the survival status of the target. For example, under extreme temperature and humidity conditions, the target may face higher physiological stress; while the concentration of hazardous gases and environmental vibrations will pose a more direct threat to the survival of the target. Through the weighted calculation of these environmental data, the degree of harm of the target's environment can be quantified, and then the risk assessment of environmental conditions can be provided to rescuers. Secondly, the calculation of the state score takes into account the target's physiological status information such as respiratory rate, body temperature and age. Respiratory rate and body temperature are key physiological indicators for assessing the health status of the target. Values ​​above or below the normal range usually mean that the target is in physiological crisis. By real-time monitoring of these parameters and combining them with the preset health range, the current health status of the target can be accurately judged. In addition, the target's age information is also included in the evaluation model, because individuals of different age groups have different tolerance to environmental and physiological changes, and elderly or weak individuals are more vulnerable. Therefore, considering the target's age as a weighted factor can improve the accuracy of the crisis score. Finally, the final crisis score is obtained by combining the environmental score with the status score using a weighted formula. This score combines the dangerousness of the target's environment and the health status of the target itself, providing a more comprehensive way to assess the crisis. The setting of the weight coefficient allows the importance of different scoring factors to be adjusted according to actual conditions. For example, in some extreme disaster scenarios, environmental factors may be more critical than physiological status, so the weight coefficient can be adjusted to make the environmental score account for a larger proportion of the crisis score. Ultimately, this comprehensive assessment method ensures that the robot arm can make decisions based on the most accurate crisis assessment, give priority to high-risk targets, and improve rescue efficiency and success rate.

[0086] Specifically, when establishing an age model in advance, it includes: obtaining image information of each age group, extracting facial features in each image, and establishing an age feature relationship according to the facial features, the age group and the image information. Obtaining the distance measurement of each age feature relationship in the age group, and establishing a distance matrix according to the distance measurement. Recursively merging each age feature relationship according to the distance matrix, and obtaining each age feature relationship of each age group after the recursive merging, and establishing an age model.

[0087] It can be seen that an accurate age model is established through facial image analysis and feature extraction. First, facial images of different age groups are obtained, facial features are extracted, and age feature relationships are established based on these features and the information of the age group to which they belong. Then, the distance measurement method is used to evaluate each age feature relationship and construct a distance matrix to quantify the similarity of facial features between different age groups. By recursively merging these feature relationships, a unified age model covering all age groups is finally obtained, which can effectively express and match the relationship between facial features and age groups.

[0088] It is understandable that facial features of different age groups are extracted through the collection and processing of image data. These facial features change at different ages, especially in terms of skin sagging, wrinkles, facial contours, etc. Therefore, facial image information provides an important basis for judging age. Through deep learning and image processing technology, the subtle differences between the features of different age groups can be accurately extracted. Then, the age feature relationship formula is constructed by combining the features of each age group with the age information. These relationship formulas are used to describe the changing laws of facial features between different age groups. Secondly, the similarity of facial features between different age groups is quantified by the distance measurement method. In this process, the construction of the distance matrix plays a key role. The facial feature relationship formula of each age group can be evaluated by calculating the distance between the relationship formulas of other age groups, thereby reflecting the similarity of facial features between different age groups. In this way, the facial differences between different age groups can be accurately analyzed and a more refined feature relationship model can be constructed. Finally, the recursive merging technology is used to integrate the age feature relationship formulas to form a unified model covering all age groups. The recursive merging process can further optimize the matching degree between feature relations and continuously adjust the weight of the relations according to the similarity between each age group, so that the final age model is more in line with the actual situation. This merging process helps to improve the accuracy of age prediction, allowing the model to be dynamically adjusted in different scenarios to adapt to different individual and environmental needs. Through this multi-level, multi-step facial feature analysis method, the final age model can not only accurately assess the age of the target to be rescued, but also provide effective data support for the target's health status and crisis score.

[0089] Step S300: Acquire the communication data between the rescue arm and the control end, and determine the communication score according to the communication data.

[0090] Specifically, when determining the communication score based on the communication data, it includes: obtaining the data transmission delay, transmission rate and data packet loss rate in the communication data, and substituting the data transmission delay, transmission rate and data packet loss rate into Formula IV to determine the communication score, wherein Formula IV is as follows:

[0091] .

[0092] Among them, M is the communication score, is the data transmission delay, F is the transmission rate, and F max is the preset transmission rate, J is the data packet loss rate, J max is the preset data packet loss rate, a 1 -a 3 is the fourth weight coefficient, and a 1 -a 3 All are not zero.

[0093] It is understandable that data transmission delay is an important indicator for evaluating the responsiveness of the communication link, which measures the time required for the robot to receive the command from the control end. In complex rescue scenarios, increased communication delays may cause the robot to react more slowly to environmental changes. Therefore, giving appropriate weights to data transmission delays in the formula can accurately reflect its impact on task execution efficiency. By monitoring the delay in real time, the control method of the robot arm can be dynamically adjusted. Secondly, the transmission rate, as another key indicator, directly affects the transmission efficiency of communication data. A high transmission rate means that the robot arm can quickly obtain and process task instructions from the control end, which helps to respond to changes in real time in a dynamic environment. F and F in the formula max Represent the current transmission rate and the preset maximum transmission rate respectively. By comparing them, the relative efficiency of communication can be evaluated. The introduction of transmission rate in the communication score makes it possible to flexibly adjust the task execution method according to the network bandwidth situation to avoid interrupting the rescue mission due to the decrease in transmission rate. Thirdly, the data packet loss rate reflects the integrity of information transmission during the communication process. When the data packet loss rate is high, the robot arm may not receive complete instructions, resulting in the inability to accurately execute the task. In the formula, the data packet loss rate J is combined with the preset maximum packet loss rate J max By comparing and assigning corresponding weights, the impact of data loss on communication quality can be quantified. By monitoring and evaluating the packet loss rate, communication problems can be quickly identified, and compensatory measures can be taken to reduce the negative impact of packet loss on task execution. Finally, by combining these three indicators and combining the fourth weight coefficient, Formula IV realizes a comprehensive quantitative evaluation of communication quality. Each weight coefficient can be adjusted according to the actual scenario requirements, so as to flexibly adapt to different rescue environments. For example, in cases where communication is extremely limited, the weight of transmission delay can be increased, while in high-speed and stable networks, its proportion can be reduced, and more attention can be paid to packet loss rate and transmission rate. This dynamic scoring mechanism not only improves the accuracy of communication quality assessment, but also provides more adaptive support for the autonomous rescue decision-making of the robotic arm.

[0094] Step S400: determining an adjustment coefficient according to the crisis score and the communication score, and adjusting a preset receiving frequency of the control command sent by the rescue arm receiving control terminal according to the adjustment coefficient.

[0095] Specifically, when determining the adjustment coefficient according to the crisis score and the communication score, the adjustment coefficient is determined according to the relationship between the crisis score and the pre-configured preset crisis score. When the crisis score is less than the preset crisis score, the adjustment coefficient is determined to be △L0. When the crisis score is greater than or equal to the preset crisis score, the adjustment coefficient is determined according to the crisis score difference between the crisis score and the preset crisis score.

[0096] Specifically, when determining the adjustment coefficient based on the crisis score difference between the crisis score and the preset crisis score, it includes: determining the adjustment coefficient based on the relationship between the crisis score difference and the pre-configured first preset crisis score difference and the second preset crisis score difference. When the crisis score difference is less than the first preset crisis score difference, the adjustment coefficient is determined to be △L1. When the crisis score difference is greater than or equal to the first preset crisis score difference, and the crisis score difference is less than the second preset crisis score difference, the adjustment coefficient is determined to be △L2. When the crisis score difference is greater than or equal to the second preset crisis score difference, the adjustment coefficient is determined to be △L3. Among them, the first preset crisis score difference is less than the second preset crisis score difference, and △L0<1<△L1<△L2<△L3.

[0097] Specifically, when the adjustment coefficient is determined to be △Li, i=0,1,2,3, it includes: determining whether to modify the adjustment coefficient △Li according to the relationship between the communication score and the pre-configured preset communication score: when the communication score is greater than or equal to the preset communication score, determining not to modify the adjustment coefficient △Li. When the communication score is less than the preset communication score, determining the correction coefficient according to the communication score difference between the communication score and the preset communication score, and modifying the adjustment coefficient △Li according to the correction coefficient.

[0098] Specifically, when determining the correction coefficient according to the communication score difference between the communication score and the preset communication score, it includes: determining the correction coefficient according to the relationship between the communication score difference and the pre-configured first preset communication score difference and the second preset communication score difference. When the communication score difference is less than or equal to the communication score difference, the correction coefficient is determined to be △K3. When the communication score difference is greater than the first preset communication score difference, and the communication score difference is less than or equal to the second preset communication score difference, the correction coefficient is determined to be △K2. When the communication score difference is greater than the second preset communication score difference, the correction coefficient is determined to be △K1. Among them, the first preset communication score difference is less than the second preset communication score difference, and 1<△K1<△K2<△K3.

[0099] It can be seen that the adaptive optimization of the frequency of receiving control instructions by the rescue arm is achieved through multi-level logical relationships and difference threshold judgment. First, by comparing the crisis score with the preset crisis score, the adjustment coefficient △Li is determined, and multiple score difference thresholds are set according to the crisis score difference, so as to refine the hierarchical response mechanism of the adjustment coefficient. The larger the crisis score difference, the higher the weight of the adjustment coefficient, indicating that the more urgent the rescue mission is, the more the receiving frequency needs to be appropriately increased to ensure timely response. Secondly, by comparing the communication score with the preset communication score, the adjustment coefficient △Li is further corrected. The determination of the correction coefficient is also based on the multi-level threshold division of the communication score difference, reflecting the dynamic influence of communication quality on the adjustment coefficient. When the communication quality is poor, the adjustment coefficient is further improved by increasing the weight of the correction coefficient △Ki, thereby increasing the frequency of autonomous operation under unstable communication conditions and enhancing the robustness and adaptability of the rescue mission.

[0100] It is understandable that the crisis score can be used to quantitatively evaluate the urgency of the rescue mission. When facing high-risk missions, the frequency can be adjusted to make the rescue arm respond more promptly, thereby providing stronger protection for saving lives and reducing losses. Compared with the traditional manipulator's reliance on fixed command frequencies, this mechanism of dynamic adjustment based on task urgency enables the rescue arm to better allocate resources and give priority to key tasks. Secondly, the application of communication scores provides effective support for solving the fluctuation of communication quality in emergency environments. The communication score ensures the efficient execution of the control end commands when the communication quality is good by real-time evaluation of indicators such as communication delay, data packet loss rate and transmission rate, and can dynamically adjust the autonomy level of the rescue arm when the communication quality is poor. This linkage mechanism of communication and operation reduces the possibility of interruption or failure of rescue missions due to unstable communication, thereby improving the stability of the overall rescue process. In addition, the adjustment coefficient mechanism realizes the refined adjustment of the control frequency by comprehensively analyzing the difference between the crisis score and the communication score. When the mission is critical but the communication is good, the rescue arm can execute instructions at a higher frequency; when the mission is critical and the communication is poor, the rescue arm can autonomously plan the mission path and perform operations independently by correcting the adjustment coefficient. This intelligent control method not only improves the flexibility of the rescue arm, but also reduces the operation delay caused by over-reliance on remote control, bringing a higher success rate for rescue missions in complex environments.

[0101] Step S500, obtaining the real-time receiving frequency of the rescue arm receiving the control command sent by the control end, and determining whether the rescue arm performs rescue by itself according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency.

[0102] Specifically, when determining whether the rescue arm performs rescue by itself according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, the method includes: if the real-time receiving frequency is lower than or equal to the preset receiving frequency, determining that the rescue arm performs rescue according to the received control instruction. If the real-time receiving frequency is higher than the preset receiving frequency, determining the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and controlling the rescue arm to perform autonomous rescue according to the rescue path, until the real-time receiving frequency is lower than or equal to the preset receiving frequency, and controlling the rescue arm to perform rescue according to the control instruction.

[0103] Specifically, when determining the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, it includes: according to the construction model, obtaining the obstacle density and the radius of the rescue arm in the current path of the rescue arm, and determining the complexity of the current path of the rescue arm according to the obstacle density and the radius of the rescue arm. Obtain the crisis score of each target to be rescued in the current path of the rescue arm, and determine the rescue cost of each target to be rescued according to the crisis score of the target to be rescued and the complexity of the current path. Sort the rescue costs of each target to be rescued in positive order, and determine the rescue path with the rescue cost ranked first.

[0104] It can be seen that the operation mode of the rescue arm (control command drive or autonomous rescue) is determined by the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, and intelligent path planning is performed based on the size information, crisis score and construction model of the rescue arm when necessary. This process first analyzes the density of obstacles in the rescue path and the radius of the rescue arm in combination with the construction model to quantify the path complexity. Subsequently, the rescue cost of each target is calculated by combining the path complexity and the crisis score of the target to be rescued to evaluate the priority. Finally, the rescue costs are sorted, and the optimal rescue path is selected from them to guide the autonomous operation of the rescue arm. By utilizing multi-dimensional information (real-time communication status, path complexity and target urgency), the dynamic decision-making and task optimization of the rescue arm are realized, so that it can independently perform the most urgent rescue tasks when communication is limited or the environment is complex, thereby improving efficiency and reliability.

[0105] It is understandable that by judging whether the real-time receiving frequency is lower than or equal to the preset receiving frequency, it is determined whether the rescue arm needs to perform operations according to the control instructions. When the real-time receiving frequency meets the preset conditions, the rescue arm gives priority to receiving the instructions from the control end, thereby realizing real-time coordination with the command end. However, when the real-time receiving frequency is higher than the preset receiving frequency, it means that the communication quality may be limited. At this time, the rescue arm enters the autonomous rescue mode and performs task processing based on its built-in decision-making mechanism. In the autonomous rescue mode, the rescue arm dynamically generates a rescue path based on its own size information, the target's crisis score and the construction model. Specifically, the rescue arm analyzes the density of obstacles in the current path through the construction model, and evaluates the path complexity in combination with its own radius. This method fully considers the diversity of the rescue environment and the distribution of obstacles, can effectively avoid obstacles, and ensure the feasibility and safety of path planning. In addition, the technical solution also makes full use of the crisis score of the target to be rescued, and calculates the rescue cost of each target in combination with the path complexity. The calculation of the rescue cost not only takes into account the urgency of the target, but also balances the complexity of the path execution to ensure the rationality of resource allocation and the maximization of rescue efficiency. By sorting the rescue costs, the path with the lowest cost can be prioritized and the optimal rescue strategy can be determined. This priority-based path planning mechanism enables the rescue arm to quickly locate key targets, reduce rescue time, and improve mission success rate. Finally, during the autonomous rescue process, the rescue arm continuously monitors the real-time receiving frequency. When the receiving frequency is lower than or equal to the preset receiving frequency again, the rescue arm switches to the control command mode in time and resumes collaborative operation with the control end. This type of mode switching mechanism ensures the continuity and flexibility of the rescue mission. Even under poor communication conditions or complex environments, the rescue arm can still complete the mission in the best way.

[0106] In the above embodiment, by dynamically adjusting the frequency of receiving control instructions of the rescue manipulator, the problem of unstable communication in the post-disaster environment can be effectively dealt with. When communication is restricted, the manipulator can independently decide whether to continue the mission based on the real-time receiving frequency and the target's crisis score, and flexibly adjust the rescue path, thereby improving the rescue efficiency and the manipulator's autonomous adaptability. This method not only optimizes the operational flexibility of the manipulator, but also ensures that the rescue mission can proceed smoothly even in complex environments, reducing dependence on real-time communication.

[0107] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a robotic arm control system for emergency rescue, which is configured on a grasping rescue robotic arm and includes: an information collection module, a model building module, a first evaluation module, a second evaluation module and a central control module.

[0108] Specifically, the information acquisition module is configured with a sound wave detection unit, and the information acquisition module is used to obtain the surrounding environmental information of the rescue arm. The model building module is electrically connected to the information acquisition module, and the model building module is used to establish a structural model according to the environmental information. The first evaluation module is configured with an infrared detection unit and an image acquisition unit, and the first evaluation module is used to obtain the state information of the target to be rescued, and evaluate the crisis score of the target to be rescued according to the state information. The second evaluation module is electrically connected to the control end of the rescue arm, and the second evaluation module is used to obtain the communication data between the rescue arm and the control end, and determine the communication score according to the communication data. The central control module is electrically connected to the control end of the rescue arm, the first evaluation module and the second evaluation module respectively, and the central control module is used to determine the adjustment coefficient according to the crisis score and the communication score, and adjust the preset receiving frequency of the rescue arm receiving the control end to send the control instruction according to the adjustment coefficient. The central control module is also used to obtain the real-time receiving frequency of the rescue arm receiving the control end to send the control instruction, and determine whether the rescue arm rescues itself according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, wherein: if the real-time receiving frequency is lower than or equal to the preset receiving frequency, the central control module determines that the rescue arm performs rescue according to the received control instruction. If the real-time receiving frequency is higher than the preset receiving frequency, the central control module determines the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and controls the rescue arm to perform autonomous rescue according to the rescue path until the real-time receiving frequency is lower than or equal to the preset receiving frequency, and then controls the rescue arm to perform rescue according to the control instructions.

[0109] It can be seen that the information acquisition module accurately captures the environmental information around the rescue arm, including obstacle distribution, spatial structure and dynamic changes, by configuring the acoustic wave detection unit. This information provides comprehensive data support for subsequent model building and path planning. Based on the collected environmental information, the model building module generates a detailed construction model to ensure the rationality of the path and the accuracy of the planning during the rescue process. Even in a complex or dynamically changing environment, the rescue arm can find the optimal path through the model. Secondly, the first evaluation module assumes the important responsibility of evaluating the crisis state of the target to be rescued. Its infrared detection unit and image acquisition unit can obtain the target's vital signs data and image information in real time, and generate the target's crisis score by combining this information. The crisis score quantifies the urgency of the target to be rescued in a data-driven manner, enabling the system to give priority to high-risk targets for rescue, reflecting the sensitivity and response speed to the value of life. In addition, the second evaluation module generates a communication score by analyzing the communication data. This score reflects the communication quality between the control end and the rescue arm. Especially in harsh or severely interfered environments, the communication score provides a quantitative basis for judging the stability of system operation. At the same time, the central control module integrates the crisis score and the communication score, and also dynamically adjusts the working mode of the rescue arm by calculating the adjustment coefficient. The adjusted receiving frequency reflects environmental changes and mission requirements, ensuring that the command receiving frequency of the rescue arm and the control end always adapts to the actual situation. When the real-time receiving frequency is lower than or equal to the adjusted preset receiving frequency, the central control module gives priority to instructing the rescue arm to perform rescue according to the received control instructions. This collaborative mode guarantees the planning and controllability of the rescue to the greatest extent. On the other hand, when the real-time receiving frequency is higher than the adjusted preset receiving frequency, the central control module will switch the rescue arm to the autonomous rescue mode. At this time, the central control module intelligently decides the optimal rescue path by analyzing the size information of the rescue arm, the crisis score of the target, and the path planning generated based on the environmental construction model. The rescue path not only takes into account the distribution and density of obstacles, but also calculates the rescue cost according to the crisis score and path complexity of each target, and gives priority to the target with the lowest cost. In this way, even if the communication quality is poor, the rescue arm can still complete the rescue operation through autonomous path planning and task execution.

[0110] It can be understood that the robotic arm control system and method for emergency rescue in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0111] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for controlling a manipulator arm for emergency rescue, applicable to a grabbing rescue manipulator arm, characterized in that: include: Acquire the surrounding environment information of the rescue arm, and establish a structural model according to the environmental information; Acquire status information of the target to be rescued, and evaluate the crisis score of the target to be rescued based on the status information; Acquire communication data between the rescue arm and the control end, and determine a communication score based on the communication data; Determine an adjustment coefficient according to the crisis score and the communication score, and adjust a preset receiving frequency at which the rescue arm receives the control command sent by the control end according to the adjustment coefficient; The real-time receiving frequency of the rescue arm receiving the control command sent by the control end is obtained, and according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, it is determined whether the rescue arm performs rescue by itself, wherein: If the real-time receiving frequency is lower than or equal to the preset receiving frequency, it is determined that the rescue arm performs rescue according to the received control instruction; If the real-time receiving frequency is higher than the preset receiving frequency, the rescue path is determined according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and the rescue arm is controlled to perform autonomous rescue according to the rescue path until the real-time receiving frequency is lower than or equal to the preset receiving frequency, and the rescue arm is controlled to perform rescue according to the control instructions.

2. The method for controlling a manipulator arm for emergency rescue according to claim 1, characterized in that: Acquiring the status information of the target to be rescued and evaluating the crisis score of the target to be rescued according to the status information includes: Acquire the ambient temperature, ambient humidity, oxygen concentration, dangerous gas concentration and ambient structural vibration amplitude of the target to be rescued in the status information; Obtaining the respiratory rate, real-time body temperature, and facial image of the target to be rescued in the status information; Substituting the facial image into a pre-established age model to obtain the age of the target to be rescued; Substitute the ambient temperature, ambient humidity, oxygen concentration, dangerous gas concentration and ambient structural vibration amplitude into Formula I to obtain the environmental score of the target to be rescued, wherein Formula I is as follows: ; Wherein, E is the environmental score, T is the ambient temperature, and T min is the safety temperature starting point, T max is the safety temperature termination degree, H is the ambient humidity, △H is the preset safety humidity, H min is the starting value of safe humidity, H max is the safe humidity termination value, O is the dangerous gas concentration, △O is the safe oxygen concentration, G is the dangerous gas concentration, △G is the safe dangerous gas concentration, V is the environmental structure vibration amplitude, V max is the maximum risk value of the vibration amplitude, w1-w5 are the first weight coefficients, and w1-w5 are not zero; Substitute the respiratory rate, real-time body temperature and age of the target to be rescued into Formula II to obtain the status score of the target to be rescued, where Formula II is as follows: ; Wherein, S is the status score of the target to be rescued, R is the breathing frequency, r is the center value of the preset safe breathing frequency range, △R is the preset safe breathing frequency range, P is the real-time body temperature, p is the center value of the preset safe body temperature range, △P is the preset safe body temperature range, Q is the age of the target to be rescued, q is the center range of the preset healthy age range, △Q is the preset healthy age range, z1-z3 are second weight coefficients, and z1-z3 are not zero; Substitute the environmental score and the state score of the target to be rescued into formula III to determine the crisis score of the target to be rescued, wherein formula III is as follows: Y=c1·E+c2·S; Wherein, Y is the crisis score, S is the status score of the target to be rescued, E is the environment score of the target to be rescued, c1 and c2 are third weight coefficients, and both c1 and c2 are not zero.

3. The method for controlling a manipulator arm for emergency rescue according to claim 2, characterized in that: When modeling age upfront, include: Acquire image information of each age group, extract facial features in each image, and establish an age feature relationship according to the facial features, the age group and the image information; Obtaining a distance metric for each age characteristic relational expression in the age group, and establishing a distance matrix according to the distance metric; The age-feature relationship formulas are recursively merged according to the distance matrix, and the age-feature relationship formulas of the age groups after the recursive merging are obtained to establish the age model.

4. The method for controlling a manipulator arm for emergency rescue according to claim 3, characterized in that: Determining a communication score based on the communication data includes: The data transmission delay, transmission rate and data packet loss rate in the communication data are obtained, and the data transmission delay, transmission rate and data packet loss rate are substituted into formula IV to determine the communication score, wherein the formula IV is as follows: ; Wherein, M is the communication score, is the data transmission delay, F is the transmission rate, and F max is the preset transmission rate, J is the data packet loss rate, J max is the preset data packet loss rate, a1-a3 are the fourth weight coefficients, and a1-a3 are not zero.

5. The method for controlling a manipulator arm for emergency rescue according to claim 4, characterized in that: When determining the adjustment factor based on the crisis score and communication score, it includes: Determining the adjustment coefficient according to a relationship between the crisis score and a pre-configured preset crisis score; When the crisis score is less than the preset crisis score, the adjustment coefficient is determined to be △L0; When the crisis score is greater than or equal to the preset crisis score, the adjustment coefficient is determined according to a crisis score difference between the crisis score and the preset crisis score.

6. The method for controlling a manipulator arm for emergency rescue according to claim 5, characterized in that: When determining the adjustment coefficient according to the crisis score difference between the crisis score and the preset crisis score, it includes: Determine the adjustment coefficient according to the relationship between the crisis score difference and a pre-configured first preset crisis score difference and a second preset crisis score difference; When the crisis score difference is less than the first preset crisis score difference, the adjustment coefficient is determined to be △L1; When the crisis score difference is greater than or equal to the first preset crisis score difference, and the crisis score difference is less than the second preset crisis score difference, the adjustment coefficient is determined to be △L2; When the crisis score difference is greater than or equal to the second preset crisis score difference, the adjustment coefficient is determined to be △L3; The first preset crisis score difference is smaller than the second preset crisis score difference, and ΔL0<1<ΔL1<ΔL2<ΔL3.

7. The method for controlling a manipulator arm for emergency rescue according to claim 6, characterized in that: When the adjustment coefficient is determined to be △Li, i=0, 1, 2, 3, it includes: According to the relationship between the communication score and the pre-configured preset communication score, it is determined whether to modify the adjustment coefficient △Li: When the communication score is greater than or equal to the preset communication score, it is determined that the adjustment coefficient △Li is not to be corrected; When the communication score is less than the preset communication score, a correction coefficient is determined according to a communication score difference between the communication score and the preset communication score, and the adjustment coefficient ΔLi is corrected according to the correction coefficient.

8. The method for controlling a manipulator arm for emergency rescue according to claim 7, characterized in that: Determining the correction coefficient according to the communication score difference between the communication score and the preset communication score includes: Determine the correction coefficient according to the relationship between the communication score difference and a preconfigured first preset communication score difference and a second preset communication score difference; When the communication score difference is less than or equal to the communication score difference, the correction coefficient is determined to be △K3; When the communication score difference is greater than the first preset communication score difference, and the communication score difference is less than or equal to the second preset communication score difference, determining the correction coefficient to be △K2; When the communication score difference is greater than the second preset communication score difference, the correction coefficient is determined to be △K1; The first preset communication score difference is smaller than the second preset communication score difference, and 1<△K1<△K2<△K3.

9. The method for controlling a manipulator arm for emergency rescue according to claim 4, characterized in that: When determining the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, it includes: Obtaining the density of obstacles in the current path of the rescue arm and the radius of the rescue arm according to the constructed model, and determining the complexity of the current path of the rescue arm according to the density of obstacles and the radius of the rescue arm; Acquire the crisis score of each of the targets to be rescued in the current path of the rescue arm, and determine the rescue cost of each of the targets to be rescued according to the crisis score of the targets to be rescued and the complexity of the current path; The rescue costs of the targets to be rescued are sorted in positive order, and the rescue cost ranked first is determined as the rescue path.

10. A mechanical arm control system for emergency rescue, configured on a grabbing rescue mechanical arm, which adopts a mechanical arm control method for emergency rescue as claimed in claims 1-9, characterized in that: include: An information collection module, which is equipped with a sound wave detection unit, and is used to obtain the surrounding environment information of the rescue arm; A model building module, electrically connected to the information acquisition module, and configured to build a construction model according to the environmental information; A first evaluation module is configured with an infrared detection unit and an image acquisition unit, and is used to obtain status information of a target to be rescued and evaluate a crisis score of the target to be rescued according to the status information; A second evaluation module is electrically connected to the control end of the rescue arm, and the second evaluation module is used to obtain communication data between the rescue arm and the control end, and determine a communication score according to the communication data; The central control module is electrically connected to the control end of the rescue arm, the first evaluation module and the second evaluation module respectively. The central control module is used to determine the adjustment coefficient according to the crisis score and the communication score, and adjust the preset receiving frequency of the rescue arm receiving the control command sent by the control end according to the adjustment coefficient; the central control module is also used to obtain the real-time receiving frequency of the rescue arm receiving the control command sent by the control end, and determine whether the rescue arm rescues itself according to the relationship between the real-time receiving frequency and the adjusted preset receiving frequency, wherein: If the real-time receiving frequency is lower than or equal to the preset receiving frequency, the central control module determines that the rescue arm performs rescue according to the received control instruction; If the real-time receiving frequency is higher than the preset receiving frequency, the central control module determines the rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the construction model, and controls the rescue arm to perform autonomous rescue according to the rescue path until the real-time receiving frequency is lower than or equal to the preset receiving frequency, and then controls the rescue arm to perform rescue according to the control instructions.

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