A manipulator control system and method for emergency rescue

Abstract

The present invention relates to the technical field of robotic arm control, and discloses a robotic arm control system and method for emergency rescue. The method includes: obtaining the circumferential environment information of the rescue arm and establishing a structural model based on the environment information. 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. Obtaining the communication data between the rescue arm and the control end, and determining the communication score according to the communication data. Determining an adjustment coefficient according to the crisis score and the communication score, and adjusting the preset reception frequency at which the rescue arm receives the control instructions sent by the control end according to the adjustment coefficient. Obtaining the real-time reception frequency at which the rescue arm receives the control instructions sent by the control end, and determining whether the rescue arm conducts self-rescue according to the relationship between the real-time reception frequency and the adjusted preset reception frequency. The present invention dynamically adjusts the operation mode of the rescue arm to ensure that the rescue task can be completed efficiently and accurately under different environmental and communication conditions.

Description

Technical Field

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

[0002] Emergency rescue tasks usually occur in complex, dangerous, and unpredictable environments, such as disaster scenarios like earthquakes, fires, and mine disasters. The complexity and harsh conditions of these environments pose great challenges to traditional manual rescue. Firstly, the disaster site is often filled with ruins or unstable structures, making it very difficult for manual rescue workers to quickly enter the disaster area safely, resulting in a significant extension of the rescue time. Secondly, in extreme environments, the safety of rescue workers is always the most important consideration, and excessive reliance on manual rescue increases the risk of casualties. Therefore, the use of automated equipment, especially robotic arms, has become an effective means to solve these problems.

[0003] However, the control of current traditional robotic arms faces many technical challenges. Especially in the post-disaster environment, communication facilities are often damaged or severely restricted, unable to ensure real-time and efficient instruction transmission, which may cause the robotic arm to be unable to accurately execute rescue tasks. Since the robotic arm usually requires a very short time from receiving an instruction to executing a task, the instability of communication will significantly reduce its working efficiency and may cause task failure. Secondly, in earthquakes, fires, or other disasters, the environment in the disaster area will change violently, buildings may collapse, roads may be blocked, and the position and state of targets (such as trapped people, items, or equipment) may also change. This requires the robotic arm to be able to adapt to the constantly changing environment and make quick responses based on real-time information when performing tasks. The traditional robotic arm control method relies on high-frequency instruction transmission and manual intervention, which cannot achieve flexible adaptation and instant response in complex environments.

[0004] Therefore, there is an urgent need to invent a control technology for robotic arms used in emergency rescue to solve the problems that traditional robotic arm control faces challenges such as unstable communication and violent environmental changes in the post-disaster environment, resulting in its inability to flexibly respond to complex situations and efficiently complete rescue tasks. 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 problems that in the current technology, robotic arm control faces challenges such as unstable communication and violent environmental changes in the post-disaster environment, resulting in its inability to flexibly respond to complex situations and efficiently complete rescue tasks.

[0006] The present invention proposes a robotic arm control method applicable to grasping rescue robotic arms, including:

[0007] Obtain the circumferential environment information of the rescue arm and establish a structural model based on the environmental information;

[0008] 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;

[0009] Obtain the communication data between the rescue arm and the control end, and determine the communication score according to the communication data;

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

[0011] Obtain the real-time reception frequency at which the rescue arm receives the control instruction sent by the control end, and determine whether the rescue arm rescues itself according to the relationship between the real-time reception frequency and the adjusted preset reception frequency, where:

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

[0013] If the real-time reception frequency is higher than the preset reception frequency, determine a rescue path according to the size information of the rescue arm, the crisis score of the target to be rescued, and the structural model, and control 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 control the rescue arm to rescue according to the control instruction.

[0014] Further, 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:

[0015] Obtain 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;

[0016] Obtain the breathing frequency, real-time body temperature, and face image of the target to be rescued in the status information;

[0017] Substitute the face image into the pre-established age model to obtain the age of the target to be rescued;

[0018] Substitute 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:

[0019] ;

[0020] where, E is the environmental score, T is the environmental temperature, Tmin is the starting degree of the safe temperature, T max is the ending degree of the safe temperature, H is the environmental humidity, △H is the preset safe humidity, H min is the starting value of the safe humidity, H max is the ending value of the safe humidity, O is the concentration of the hazardous gas, △O is the safe oxygen concentration, G is the concentration of the hazardous gas, △G is the safe hazardous gas concentration, V is the vibration amplitude of the environmental structure, V max is the maximum risk value of the vibration amplitude, w1 - w5 are the first weight coefficients, and w1 - w5 are all non - 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] Among them, R is the respiratory rate, r is the central 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 central 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 central range of the preset healthy age range, △Q is the preset healthy age range, z1 - z3 are the second weight coefficients, and z1 - z3 are all non - zero;

[0024] Substitute the environmental score and status 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] Among them, Y is the crisis score, S is the status score of the target to be rescued, E is the environmental score of the target to be rescued, c1 and c2 are the third weight coefficients, and c1 and c2 are all non - zero.

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

[0028] Obtain the image information of each age group, extract the facial features in each image, and establish an age feature relationship formula according to the facial features, the age group to which they belong, and the image information;

[0029] Obtain the distance metric of each age feature relationship formula in the age group, and establish a distance matrix according to the distance metric;

[0030] Recursively merge the age feature relationships based on the distance matrix, and obtain the age feature relationships of each age group after recursive merging to establish the age model.

[0031] Further, when determining the communication score based on the communication data, it includes:

[0032] Obtain the data transmission delay, transmission rate, and data packet loss rate in the communication data, and substitute the data transmission delay, transmission rate, and data packet loss rate into Formula Ⅳ to determine the communication score, where Formula Ⅳ is as follows:

[0033] ;

[0034] where M is the communication score, is the data transmission delay, F is the transmission rate, 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 all non - zero.

[0035] Further, when determining the adjustment coefficient based on the crisis score and the communication score, it includes:

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

[0037] When the crisis score is less than the preset crisis score, then determine the adjustment coefficient as △L0;

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

[0039] Further, 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 the pre - configured first preset crisis score difference and second preset crisis score difference;

[0041] When the crisis score difference is less than the first preset crisis score difference, then determine the adjustment coefficient as △L1;

[0042] 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, then determine the adjustment coefficient as △L2;

[0043] 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;

[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] According to the relationship between the communication score and the pre-configured preset communication score, determine whether to correct the adjustment coefficient △Li:

[0047] When the communication score is greater than or equal to the preset communication score, it is determined that the adjustment coefficient △Li is not corrected;

[0048] When the communication score is less than the preset communication score, according to the communication score difference between the communication score and the preset communication score, determine the correction coefficient, 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] According to the relationship between the communication score difference and the pre-configured first preset communication score difference and second preset communication score difference, determine the correction coefficient;

[0051] When the communication score difference is less than or equal to the communication score difference, the correction coefficient is determined to be △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, the correction coefficient is determined to be △K2;

[0053] When the communication score difference is greater than the second preset communication score difference, the correction coefficient is determined to be △K1;

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

[0055] Further, when determining the rescue path according to the dimension 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 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;

[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 conducts 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 conducts 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 conduct 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 conduct 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 manipulator control method for emergency rescue, including:

[0074] Step S100: Obtain the circumferential environment information of the rescue arm and establish a structure 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 structure model, it helps the manipulator understand the environmental characteristics around it. These environmental information includes factors such as obstacles, spatial layout, terrain changes, etc. By establishing a structure model, the manipulator 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, concentration of dangerous gases, and vibration amplitude of the environmental structure 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, concentration of dangerous gases, and vibration amplitude of the environmental structure into Formula I to obtain the environmental score of the target to be rescued, where Formula I is as follows:

[0078] .

[0079] Wherein, 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 safety humidity termination value, O is the concentration of dangerous gas, △O is the safe oxygen concentration, G is the concentration of dangerous gas, △G is the safe dangerous gas concentration, V is the vibration amplitude of the environmental structure, V max is the maximum risk value of the vibration amplitude, w1 - w5 are the first weight coefficients, and none of w1 - w5 is zero. Substitute the breathing frequency, 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. The Formula Ⅱ is as follows:

[0080] .

[0081] Among them, R is the breathing frequency, r is the central 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 central 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 central range of the preset healthy age range, △Q is the preset healthy age range, z1 - z3 are the second weight coefficients, and none of z1 - z3 is zero. Substitute the environmental score and the status score of the target to be rescued into Formula Ⅲ to determine the crisis score of the target to be rescued. The 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 environmental score of the target to be rescued, c1 and c2 are the third weight coefficients, and none of c1 and c2 is zero.

[0084] It can be seen that by obtaining factors such as environmental temperature, humidity, oxygen concentration, dangerous gas concentration, 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 environment where the target is located and can reflect the impact of the environment on the survival status of the target. Secondly, by obtaining physiological state information such as the breathing frequency, body temperature, and facial image of the target, combined with the preset safe range, the status score of the target is calculated through Formula Ⅱ, and the score is further optimized according to the age information of the target. Finally, by substituting the environmental score and the status score into Formula Ⅲ, combined with the weight coefficients, the crisis score of the target is calculated. This score comprehensively considers the environment where the target is located and the health status of the target itself, and can thus evaluate the survival risk of the target in real - time and accurately, providing a decision - making basis for the manipulator to formulate subsequent rescue strategies.

[0085] It is understandable that the calculation of the environmental score evaluates the danger level of the environment where the target is located through multiple key parameters, including temperature, humidity, oxygen concentration, concentration of hazardous gases, and vibration amplitude of the environmental structure, etc. These factors directly affect the survival state 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 pose a more direct threat to the survival of the target. Through the weighted calculation of these environmental data, the hazard level of the environment where the target is located can be quantified, thereby providing a risk assessment of the environmental conditions for rescue personnel. Secondly, the calculation of the state score takes into account physiological state information such as the target's breathing frequency, body temperature, and age. Breathing frequency and body temperature are key physiological indicators for evaluating the health status of the target. Values higher or lower than the normal range usually indicate that the target is in a physiological crisis. By continuously monitoring these parameters in real time and combining them with the preset healthy range, the current health status of the target can be accurately judged. In addition, the age information of the target is also incorporated into the evaluation model because individuals of different age groups have different tolerances to environmental and physiological changes, and the elderly or physically weak individuals are more vulnerable to hazards. Therefore, considering the target's age as a weighted factor can improve the accuracy of the crisis score. Finally, by combining the environmental score with the state score and using a weighted formula, the final crisis score is obtained. This score combines the danger level of the environment where the target is located and the health status of the target itself, thus providing a more comprehensive crisis assessment method. The setting of the weight coefficient allows the importance of different scoring factors to be adjusted according to the actual situation. For example, in some extreme disaster scenarios, environmental factors may be more critical than physiological states, so the weight coefficient can be adjusted to make the environmental score account for a larger proportion in the crisis score. Ultimately, this comprehensive evaluation method ensures that the robotic arm can make decisions based on the most accurate crisis assessment, prioritize the handling of high-risk targets, and improve the rescue efficiency and success rate.

[0086] Specifically, when establishing the age model in advance, it includes: obtaining the image information of each age group, extracting the facial features in each image, and establishing an age feature relationship formula based on the facial features, the age group to which they belong, and the image information. Obtaining the distance metric of each age feature relationship formula in the age group, and establishing a distance matrix based on the distance metric. Recursively merging between each age feature relationship formula according to the distance matrix, and obtaining each age feature relationship formula of each age group after 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 therein are extracted, and based on these features and the information of the age groups to which they belong, a relational expression of age features is established. Then, the distance measurement method is used to evaluate each relational expression of age features, and a distance matrix is constructed to quantify the similarity of facial features between different age groups. By recursively merging these relational expressions of features, a unified age model covering all age groups is finally obtained, and this model can effectively express and match the relationship between facial features and age groups.

[0088] It can be understood that through the collection and processing of image data, facial features of each age group are extracted. These facial features vary at different age stages, especially in aspects such as skin relaxation, wrinkles, and facial contours. Therefore, facial image information provides an important basis for age judgment. Through deep learning and image processing technologies, the subtle differences between features of different age groups can be accurately extracted. Then, by combining the features of each age group with age information, relational expressions of age features are constructed, and these relational expressions are used to describe the variation rules of facial features between different age groups. Secondly, the distance measurement method is used to quantify the similarity of facial features between each age group. In this process, the construction of the distance matrix plays a key role. The relational expression of facial features of each age group can be evaluated by calculating the distance from the relational expressions of other age groups, thereby reflecting the similarity of facial features between each age group. 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, using the recursive merging technology, the relational expressions of each age feature are integrated to form a unified model covering all age groups. The process of recursive merging can further optimize the matching degree between relational expressions of features, and continuously adjust the weights of relational expressions according to the similarity between each age group, making the finally established age model more in line with the actual situation. This merging process helps to improve the accuracy of age prediction, enabling the model to be dynamically adjusted in different scenarios to adapt to the needs of different individuals and environments. And through this multi-level and multi-step facial feature analysis method, the finally established age model can not only accurately evaluate the age of the target to be rescued, but also provide effective data support for the health status and crisis score of the target.

[0089] Step S300: Obtain 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 according to 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, where 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, and J max is the preset data packet loss rate, and a1 - a3 are the fourth weight coefficients, and a1 - a3 are all non - zero.

[0093] It can be understood that the data transmission delay is an important indicator for evaluating the response ability of the communication link, which measures the time required from the control end to send an instruction to the robotic arm to receive the instruction. In complex rescue scenarios, an increase in communication delay may cause the robotic arm to respond more slowly to environmental changes. Therefore, assigning an appropriate weight to the data transmission delay in the formula can accurately reflect its impact on the task execution efficiency. By monitoring the delay in real - time, the control method for the robotic arm can be dynamically adjusted. Secondly, as another key indicator, the transmission rate directly affects the transmission efficiency of communication data. A high transmission rate means that the robotic 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 max in the formula 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 the transmission rate into the communication score enables flexible adjustment of the task execution method according to the network bandwidth situation, avoiding interruption of the rescue task due to a decrease in the transmission rate. Thirdly, the data packet loss rate reflects the integrity of information transmission during communication. When the data packet loss rate is high, the robotic arm may not receive complete instructions, resulting in inaccurate task execution. In the formula, by comparing the data packet loss rate J with the preset maximum packet loss rate J max 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 compensation measures can be taken to reduce the negative impact of packet loss on task execution. Finally, by comprehensively considering 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 the case of extremely limited communication, the weight of the transmission delay can be increased, while in a high - speed and stable network, its proportion can be reduced, and more attention can be paid to the packet loss rate and the transmission rate. This dynamic scoring mechanism not only improves the accuracy of communication quality evaluation but also provides more adaptable support for the autonomous rescue decision - making of the robotic arm.

[0094] Step S400: Determine an adjustment coefficient based on the crisis score and the communication score, and adjust the preset reception frequency at which the rescue arm receives control instructions sent by the control terminal according to the adjustment coefficient.

[0095] Specifically, when determining the adjustment coefficient based on the crisis score and the communication score, it includes: determining the adjustment coefficient according to the 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 the crisis score difference between the crisis score and the preset crisis score.

[0096] Specifically, when determining the adjustment coefficient according to the crisis score difference between the crisis score and the preset crisis score, it includes: determining the adjustment coefficient according to the relationship between the crisis score difference and pre-configured first and second preset crisis score differences. 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 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 correct the adjustment coefficient △Li according to the relationship between the communication score and a pre-configured preset communication score: when the communication score is greater than or equal to the preset communication score, it is determined that the adjustment coefficient △Li is not corrected. When the communication score is less than the preset communication score, the correction coefficient is determined according to the communication score difference between the communication score and the preset communication score, and the adjustment coefficient △Li is corrected 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 pre-configured first and second preset communication score differences. 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 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 through multi-level logical relationships and difference threshold judgments, the adaptive optimization of the frequency of the rescue arm receiving control instructions is achieved. 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, thereby refining the hierarchical response mechanism of the adjustment coefficient. The greater the crisis score difference, the higher the weight of the adjustment coefficient, indicating a higher urgency of the rescue task, and 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, by increasing the weight of the correction coefficient △Ki, the adjustment coefficient is further increased, so as to increase the frequency of autonomous operation under unstable communication conditions and enhance the robustness and adaptability of the rescue task.

[0100] It can be understood that the urgency of the rescue task can be quantitatively evaluated through the crisis score. In the face of high-risk tasks, the response of the rescue arm can be made more timely by adjusting the frequency, thus providing stronger guarantee for saving lives and reducing losses. Compared with the traditional robotic arm's dependence on a fixed instruction frequency, this mechanism of dynamically adjusting based on the task urgency enables the rescue arm to better allocate resources and prioritize key tasks. Secondly, the application of the communication score provides effective support for solving the communication quality fluctuations in the emergency environment. The communication score ensures the efficient execution of control terminal instructions when the communication quality is good by real-time evaluating 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 between communication and operation reduces the possibility of rescue task interruption or failure caused by 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 relationship between the crisis score and the communication score. In the case of a critical task but good communication conditions, it can ensure that the rescue arm executes instructions at a higher frequency; while in the case of a critical task and poor communication conditions, by correcting the adjustment coefficient, the rescue arm can autonomously plan the task path and independently execute operations. This intelligent control method not only improves the flexibility of the rescue arm but also reduces the operation delay caused by excessive dependence on remote control, bringing a higher success rate for rescue tasks in complex environments.

[0101] Step S500: Obtain the real-time receiving frequency of the rescue arm receiving the control instructions sent by the control terminal, and determine whether the rescue arm conducts self-rescue 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 self-rescue according to the relationship between the real-time reception frequency and the adjusted preset reception frequency, it includes: if the real-time reception frequency is lower than or equal to the preset reception frequency, it is determined that the rescue arm performs rescue according to the received control instruction. If the real-time reception frequency is higher than the preset reception 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 reception frequency is lower than or equal to the preset reception frequency, and then the rescue arm is controlled 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 in the current path of the rescue arm and the radius of the rescue arm, and determining the complexity in the current path of the rescue arm according to the obstacle density and the radius of the rescue arm. Obtaining the crisis scores of each target to be rescued in the current path of the rescue arm, and determining the rescue cost of each target to be rescued according to the crisis score of the target to be rescued and the complexity in the current path. Sorting the rescue costs of each target to be rescued in ascending order, and determining the rescue cost ranked first as the rescue path.

[0104] It can be seen that the operation mode of the rescue arm (control instruction driven or autonomous rescue) is judged by the relationship between the real-time reception frequency and the adjusted preset reception frequency, and intelligent path planning is carried out based on the size information, crisis score and construction model of the rescue arm when necessary. This process first analyzes the obstacle density in the rescue path and the radius of the rescue arm in combination with the construction model to quantify the path complexity. Subsequently, by comprehensively considering the path complexity and the crisis score of the target to be rescued, the rescue cost of each target is calculated to evaluate the priority. Finally, the rescue costs are sorted, the optimal rescue path is selected from them and used to guide the autonomous operation of the rescue arm. By utilizing multi-dimensional information (real-time communication status, path complexity and target urgency), dynamic decision-making and task optimization of the rescue arm are realized, enabling it to independently execute 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 an acoustic wave detection unit, and the information acquisition module is used to obtain the circumferential environment information of the rescue arm. The model establishment module is electrically connected to the information acquisition module, and the model establishment module is used to establish a structural model based on 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 status information of the target to be rescued and evaluate the crisis score of the target to be rescued according to the status 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. The central control module is used to determine an adjustment coefficient according to the crisis score and the communication score, and adjust the preset reception frequency at which the rescue arm receives the control instruction sent by the control end according to the adjustment coefficient. The central control module is also used to obtain the real-time reception frequency at which the rescue arm receives the control instruction sent by the control end, and determine whether the rescue arm conducts self-rescue according to the relationship between the real-time reception frequency and the adjusted preset reception frequency, where: 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 conducts rescue according to the received control instruction. 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 structural model, and controls the rescue arm to conduct 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 conduct rescue according to the control instruction.

[0109] It can be seen that the information acquisition module accurately captures the environmental information around the rescue arm by configuring the acoustic wave detection unit, including obstacle distribution, spatial structure, and dynamic changes, etc. This information provides comprehensive data support for subsequent model establishment and path planning. The model establishment module generates a detailed structural model based on the collected environmental information to ensure the rationality of the path and the accuracy of the planning during the rescue process. Even in complex or dynamically changing environments, the rescue arm can find the optimal path through the model. Secondly, the first evaluation module undertakes 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 vital sign data and image information of the target in real time, and generate a crisis score for the target 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 preferentially select 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 operation stability of the system. 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 the environmental changes and task requirements, ensuring that the instruction receiving frequency between 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 preferentially instructs the rescue arm to carry out the rescue according to the received control instructions. This collaborative mode maximally ensures the planning and controllability of the rescue. 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 structure model. The rescue path not only considers the distribution and density of obstacles, but also calculates the rescue cost according to the crisis scores and path complexities of each target, and preferentially processes the target with the minimum 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 mechanical arm control system and method for emergency rescue in the above embodiments of the present invention have the same beneficial effects and will not be elaborated here.

[0111] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.) that contain computer-usable program code.

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

[0113] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still, the specific implementation manners of the present invention can be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope 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, a 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, the rescue arm is controlled to perform rescue according to the control instruction; 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, 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, 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, where 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.

2. The method for controlling a manipulator arm for emergency rescue according to claim 1, 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.

3. The method for controlling a manipulator arm for emergency rescue according to claim 2, 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.

4. The method for controlling a manipulator arm for emergency rescue according to claim 3, 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.

5. The method for controlling a manipulator arm for emergency rescue according to claim 4, 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.

6. The method for controlling a manipulator arm for emergency rescue according to claim 5, 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.

7. The method for controlling a manipulator arm for emergency rescue according to claim 6, 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.

8. The method for controlling a manipulator arm for emergency rescue according to claim 3, 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.

9. A robot arm control system for emergency rescue, configured on a grabbing rescue robot arm, which adopts a robot arm control method for emergency rescue as claimed in any one of claims 1 to 8, 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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