Micro-robot form man-machine collaborative decision-making method, system and device based on image and physiological information fusion perception, medium and product

Abstract

The invention discloses a micro-robot form man-machine collaborative decision-making method, system and device based on image and physiological information fusion perception, a medium and a product, and relates to the technical field of micro-robot form decision-making, and the method comprises the steps: obtaining the physiological information of a designated area where a micro-robot is located; based on the actual pose state information and the physiological information, the micro-robot generates spontaneous deformation, and pose state information after deformation is obtained; performing optimal state estimation on the deformed pose state information by using a Kalman filtering model to obtain optimal state estimation of the pose state information; the control center determines a correct and wrong result of spontaneous deformation of the micro robot; if the deformation is correct, determining the pose state information after deformation as final pose state information; otherwise, sending an error correction instruction; and determining final pose state information by using a CART classification tree model based on the error correction instruction and the deformation quantity for generating spontaneous deformation. According to the invention, the form decision-making precision of the micro-robot is improved.

Description

Technical Field

[0001] The present application relates to the technical field of micro-robot morphology decision-making, and in particular to a method, system, device, medium and product for human-machine collaborative decision-making of micro-robot morphology based on the fusion perception of image and physiological information. Background Art

[0002] Microrobots refer to small robots with a scale of micro-nano (a few nanometers to hundreds of microns). Such robots can perform tasks at the micro-nano scale and have excellent flexibility and adaptability. Microrobot technology has rapidly developed into an emerging frontier research field. It has extremely important application value in clinical medicine, bioengineering and other fields, such as minimally invasive surgery, targeted therapy, cell manipulation, heavy metal detection, pollutant degradation, etc. The external drive and motion control system of the microrobot is an important guarantee for the microrobot to achieve precise movement and execute multi-task sequences.

[0003] Limited by the sensing technology of closed living environments, traditional imaging technologies based on ultrasound, fluorescence, and magnetic fields all have problems such as weak signals, low sensitivity, and poor real-time performance, which brings huge challenges to the real-time adjustment of the microrobot's position under human decision-making during living operations, such as movement, hovering, rotation, contraction, and stretching.

[0004] The targeted operation of the microrobot requires that the robot body can track and move, and dynamically adjust its shape during the movement to curl, shrink, etc. to ensure that the robot does not cause damage to the surrounding tissues. At the same time, when encountering specific strong alkali and strong acid environments, it can deform in real time to protect the drug from physical contact with environmental substances and inactivation. After reaching the target area, it is also necessary to adjust the posture and shape so that the robot can contact the lesion in the optimal state. This puts higher requirements on the real-time and accuracy of dynamic decision-making in non-structural environments. Summary of the invention

[0005] The purpose of this application is to provide a method, system, device, medium and product for human-machine collaborative decision-making of micro-robot morphology based on the fusion perception of image and physiological information, so as to solve the problem of low accuracy of micro-robot morphology decision-making.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception, comprising:

[0008] Acquiring physiological information of a designated area where the microrobot is located;

[0009] Based on the physiological information, the microrobot generates spontaneous deformation and obtains posture state information after deformation;

[0010] Using the Kalman filter model, the optimal state estimation of the deformed posture state information is performed to obtain the optimal state estimation of the posture state information;

[0011] The control center determines whether the spontaneous deformation of the microrobot is correct or incorrect; the correct or incorrect result is whether the deformation is correct or incorrect;

[0012] If the correct result is that the deformation is correct, the posture state information after the deformation is determined as the final posture state information of the micro robot;

[0013] If the correct or incorrect result is a deformation error, the control center sends a correction instruction to the microrobot, and the microrobot uses a CART classification tree model to determine the final posture state information of the microrobot based on the correction instruction and the deformation amount generated by the spontaneous deformation.

[0014] Optionally, before obtaining the physiological information of the designated area where the micro-robot is located, the method further includes:

[0015] The microrobot obtains the position information of the designated area, and moves to the designated area based on the position information.

[0016] Optionally, a Kalman filter model is used to perform an optimal state estimation on the deformed posture state information to obtain an optimal state estimation of the posture state information, specifically including:

[0017] The deformed posture state information is used as the measured value of the posture state information at the initial optimization time, and the optimal state estimation is performed on the initial posture state information according to the set optimization time step to obtain the optimal state estimation of the posture state information.

[0018] Optionally, the Kalman filter model includes:

[0019]

[0020] in, is the optimal state estimation of the posture state information at the kth optimization moment; is the predicted state estimate of the posture state information at the kth optimization moment determined based on the measured value of the posture state information at the k-1th optimization moment; K(k) is the Kalman gain at the kth optimization moment; Z(k) is the measured value of the posture state information at the kth optimization moment; H(k) is the observation matrix at the kth optimization moment; A(k) is the state transfer matrix at the kth optimization moment; It is the optimal state estimate of the posture state information at the k-1th optimization moment.

[0021] Optionally, determining whether the spontaneous deformation of the microrobot is correct or not comprises:

[0022] Based on the posture state information after deformation and the optimal state estimation of the posture state information, the correctness or incorrectness result of the spontaneous deformation of the micro robot is determined.

[0023] Optionally, determining the correctness of the spontaneous deformation of the microrobot based on the posture state information after deformation and the optimal state estimation of the posture state information includes:

[0024] Determine whether the deformed posture state information is consistent with the optimal state estimation of the posture state information;

[0025] If so, the correct answer is that the deformation is correct;

[0026] If not, the correct answer is a deformation error.

[0027] In the second aspect, the present application provides a micro-robot human-machine collaborative decision-making system based on image and physiological information fusion perception, including:

[0028] A physiological information acquisition module, used to acquire physiological information of a designated area where the microrobot is located;

[0029] A spontaneous deformation module, used for causing the microrobot to spontaneously deform based on the physiological information and obtaining the posture state information after the deformation;

[0030] A Kalman filter module is used to use a Kalman filter model to perform optimal state estimation on the deformed posture state information to obtain the optimal state estimation of the posture state information;

[0031] A judgment module, used for the control center to determine whether the spontaneous deformation of the micro-robot is correct or incorrect; the correct or incorrect result is whether the deformation is correct or incorrect;

[0032] A first final posture determination module is used to determine the posture state information after deformation as the final posture state information of the micro robot if the correctness result is that the deformation is correct;

[0033] The second final posture determination module is used for, if the correctness result is a deformation error, the control center sends an error correction instruction to the microrobot, and the microrobot uses the CART classification tree model to determine the final posture state information of the microrobot based on the error correction instruction and the deformation amount generated by the spontaneous deformation.

[0034] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described micro-robot morphology human-computer collaborative decision-making methods based on image and physiological information fusion perception.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned micro-robot morphology human-machine collaborative decision-making methods based on image and physiological information fusion perception.

[0036] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned micro-robot morphology human-machine collaborative decision-making methods based on image and physiological information fusion perception.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0038] The present application discloses a method, system, device, medium and product for human-machine collaborative decision-making of microrobot morphology based on image and physiological information fusion perception. First, the physiological information of the designated area where the microrobot is located is obtained; based on the physiological information, the microrobot produces spontaneous deformation and obtains the posture state information after deformation; secondly, the Kalman filter model is used to perform optimal state estimation on the posture state information after deformation to obtain the optimal state estimation of the posture state information; then, the control center determines the correct or incorrect result of the spontaneous deformation of the microrobot; the correct or incorrect result is that the deformation is correct or the deformation is wrong; if the correct or incorrect result is that the deformation is correct, the posture state information after deformation is determined as the final posture state information of the microrobot; if the correct or incorrect result is that the deformation is correct, the posture state information after deformation is determined as the final posture state information of the microrobot; if the correct or incorrect result is that the deformation is wrong, the control center sends an error correction instruction to the microrobot, and the microrobot uses the CART classification tree model to determine the final posture state information of the microrobot based on the error correction instruction and the deformation amount generated by the spontaneous deformation. The present application combines the autonomous deformation and error correction instructions of the microrobot to realize the human-machine collaborative decision-making of the microrobot morphology and improve the accuracy of the microrobot morphology decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a process flow of a micro-robot human-machine collaborative decision-making method based on image and physiological information fusion perception provided in an embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of the architecture of the micro-robot morphology human-machine collaborative decision-making method based on the fusion perception of image and physiological information;

[0042] Figure 3A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] The purpose of this application is to provide a method, system, device, medium and product for micro-robot morphology human-machine collaborative decision-making based on the fusion perception of image and physiological information, aiming to improve the accuracy of micro-robot morphology decision-making.

[0045] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0046] In an exemplary embodiment, Figure 1 and Figure 2 As shown, the micro-robot human-machine collaborative decision-making method based on image and physiological information fusion perception in this embodiment includes:

[0047] Step 1: Obtain the physiological information of the specified area where the microrobot is located.

[0048] Specifically, the microrobot is a microrobot working in a human body or an animal body. The designated area is a designated part in the body, so the designated area includes physiological information of hydrogen ions, sodium ions and hydroxide ions.

[0049] As an optional implementation manner, before step 1, the method further includes:

[0050] The micro robot obtains the location information of the designated area and moves to the designated area based on the location information.

[0051] Step 2: Based on physiological information, the microrobot undergoes spontaneous deformation and obtains the posture state information after deformation.

[0052] Specifically, spontaneous deformation becomes expansion or contraction.

[0053] Step 3: Use the Kalman filter model to perform optimal state estimation on the deformed posture state information to obtain the optimal state estimation of the posture state information.

[0054] As an optional implementation, step 3 specifically includes:

[0055] The deformed posture state information is used as the measured value of the posture state information at the initial optimization time. According to the set optimization time step, the optimal state estimation of the initial posture state information is performed to obtain the optimal state estimation of the posture state information.

[0056] As an optional implementation, the Kalman filter model includes:

[0057]

[0058] in, is the optimal state estimation of the posture state information at the kth optimization moment; is the predicted state estimate of the posture state information at the kth optimization moment determined based on the measured value of the posture state information at the k-1th optimization moment; K(k) is the Kalman gain at the kth optimization moment; Z(k) is the measured value of the posture state information at the kth optimization moment; H(k) is the observation matrix at the kth optimization moment; A(k) is the state transfer matrix at the kth optimization moment; It is the optimal state estimate of the posture state information at the k-1th optimization moment.

[0059] Step 4: The control center determines the correctness or incorrectness of the spontaneous deformation of the micro-robot. The correctness or incorrectness result is that the deformation is correct or incorrect.

[0060] As an optional implementation, determining the correctness of the spontaneous deformation of the microrobot includes:

[0061] Based on the posture state information after deformation and the optimal state estimation of the posture state information, the correctness or incorrectness result of the spontaneous deformation of the micro robot is determined.

[0062] As an optional implementation, based on the posture state information after deformation and the optimal state estimation of the posture state information, determining the correctness or incorrectness of the spontaneous deformation of the micro robot includes:

[0063] Determine whether the deformed posture state information is consistent with the optimal state estimation of the posture state information.

[0064] If so, the true or false result is that the deformation is correct.

[0065] If not, the correct answer is a deformation error.

[0066] Step 5: If the error result is that the deformation is correct, the posture state information after deformation is determined as the final posture state information of the micro robot.

[0067] Step 6: If the error result is that the deformation is correct, the control center sends a correction instruction to the microrobot. The microrobot uses the CART classification tree model to determine the final posture state information of the microrobot based on the correction instruction and the deformation amount generated by the spontaneous deformation.

[0068] Further, after determining the final posture state information of the microrobot, the microrobot performs corresponding deformation according to the final posture state information.

[0069] Specifically, the determination process of the CART classification tree model includes:

[0070] 1. Data preparation:

[0071] Collect the decision signal data set D, which includes elements such as autonomous posture perception output, functional perception output, image feedback status and posture correction output.

[0072] Each element in the decision signal dataset D represents a specific decision signal, such as the probability of the microrobot being in a certain autonomous posture perception output, the probability of a certain functional perception output occurring, the probability of a certain state occurring based on image feedback, and the probability of the posture correction output being a specific posture.

[0073] The interpretation of each element and probability is:

[0074] (1) Autonomous posture perception output: represents the posture changes (such as stretching or contraction) of the microrobot in the autonomous state according to environmental conditions. For example, the microrobot may automatically adjust its own shape under certain pH conditions to cope with changes in the external environment. The decision signal dataset D contains the probability information of the autonomous posture state, which is used to evaluate the posture taken by the microrobot under different stimulation conditions.

[0075] (2) Functional perception output: This involves the functional performance of the microrobot in a specific scenario, such as the deformation that occurs when it comes into contact with tumor tissue or other specific physiological environments. Functional perception output refers to the microrobot's response behavior to the environment in this special scenario and its probability of occurrence. For example, a microrobot may undergo a specific autonomous deformation when it detects tumor tissue in order to better perform its tasks.

[0076] (3) Image feedback status: Visual feedback information obtained through an external image acquisition device is used to monitor the position status information (stretching or contracting) of the microrobot. The image feedback status is reflected in the decision signal data set D as the state evaluation result based on the visual recognition algorithm and its occurrence probability. These data can help the operator monitor the motion status of the microrobot in real time and make necessary adjustments.

[0077] (4) Posture correction output: When the operator finds through the monitoring equipment that the deformation behavior of the microrobot is inconsistent with expectations, he may issue a posture correction instruction. The posture correction output is the control signal applied by the operator through manual intervention, which is used to adjust the posture state of the microrobot. In the decision signal dataset D, these posture correction information of human intervention will also be quantified into the probability of occurrence of different states, so as to be fused with the autonomous decision information.

[0078] 2. Calculate the Gini coefficient:

[0079] The Gini coefficient of the decision signal dataset D is calculated using formula (3) to evaluate the purity or uncertainty of the decision signal dataset D.

[0080]

[0081] Where Gini(D) is the Gini coefficient of the decision signal data set D; p(x i ) is the i-th element x in the decision signal data set D i The probability of occurrence reflects the possibility that the microrobot performs a certain behavior (such as stretching or contraction) under specific conditions; n is the total number of elements in the decision signal data set D.

[0082] When x i is the probability p(x i ): For example, when the microrobot is in a specific pH environment, it may automatically expand or contract. If we define x i is the category of the microrobot's stretched state in this environment, then p(x i ) is the probability that the microrobot is in the extended state under this pH environment. For example, if the microrobot has a 70% probability of being in the extended state when the pH value is 7, then p(x i )=0.7.

[0083] When x i is the probability p(x i ): When the microrobot detects a specific physiological environment (such as tumor tissue), it may undergo a preset deformation to perform a specific function. In this case, x i represents the autonomous deformation behavior of the microrobot when detecting tumor tissue, and p(x i ) is the probability of such deformation. For example, if the microrobot has a 90% probability of deforming when it contacts a tumor, then p(x i )=0.9.

[0084] When x i The probability p(x i): When the operator finds that the microrobot does not work as expected, he can issue a posture correction command (i.e., error correction command) through the control system. In this case, x i may represent the operator's instruction to adjust the microrobot to a specific posture (e.g., contraction), and p(x i ) is the probability of successful posture adjustment. For example, if the microrobot has a 95% probability of successfully adjusting to the retracted state after the operator's instruction, then p(x i )=0.95.

[0085] For each possible element A, the Gini coefficient is calculated using formula (4) when its value is a specific value a. This helps determine which element and its specific value can best reduce the uncertainty of the decision signal dataset D.

[0086]

[0087] Where GiniIndex(D|A=a) is the Gini coefficient of D when the value of element A is a specific value a; D 1 and D 2 The two data sets obtained by dividing the data set D according to the value of element A, D 1 is the data set consisting of all data points in the stretched state, D 2 is a data set consisting of all data points in a contracted state; |·| is the number of samples in the data set.

[0088] 3. Use the decision signal data set to construct a CART classification tree:

[0089] In each step, the element A with the lowest Gini coefficient and its value are selected as the splitting criterion for the current node.

[0090] Divide the dataset D into two subsets D according to the value of attribute A 1 and D 2 , and serve as the left and right child nodes of the current node respectively.

[0091] Repeat the above process for each child node until the stopping condition is met. The stopping condition is that the number of samples in the child node is less than a predetermined threshold or the Gini coefficient is lower than a certain threshold.

[0092] Advantages of the method of this application:

[0093] 1. Significantly improve operation accuracy and real-time performance: By integrating the non-real-time micro-robot's posture state information (in the form of images) with dynamic physiological information, the micro-robot's operation scene can be more accurately identified, thereby making more accurate decisions. At the same time, the application of the Kalman filter model can determine the optimal state estimation of the micro-robot's posture state information in real time, effectively compensate for the image processing delay, and improve the system's response speed and operation accuracy.

[0094] 2. Enhance the environmental adaptability and flexibility of microrobots: enable microrobots to spontaneously adjust their morphology according to the physiological information and error correction instructions of the environment to adapt to different operating requirements and environmental conditions. This flexibility not only improves the operating efficiency of microrobots, but also enhances their survivability and operating safety in complex environments.

[0095] 3. Optimize the stability and reliability of the decision-making system: Through the CART classification tree algorithm, it is possible to efficiently process multiple modal perception information with low computational complexity and understandable decision rules, thereby optimizing the decision-making performance and stability of the system. The application of this algorithm reduces the subjectivity and uncertainty of human decision-making and improves reliability and accuracy.

[0096] 4. Improve the robustness and scalability of the overall system: When faced with complex and changing operating environments and task requirements, it can maintain stable performance and good adaptability, while also providing more possibilities for future technology upgrades and functional expansions.

[0097] The CART classification tree model is used to screen the matching degree and priority of nonlinear differentiated command signals, so as to achieve the fusion output of multimodal signals and realize the uncoupled output of decision signals (i.e., the final posture state information of the microrobot determined based on error correction instructions and the deformation amount generated by spontaneous deformation), thereby improving the high dynamics and fault tolerance of the microrobot in the process of global motion and local posture adjustment.

[0098] In an exemplary embodiment, a micro-robot-based human-machine collaborative decision-making system based on image and physiological information fusion perception is provided, comprising:

[0099] The physiological information acquisition module is used to obtain the physiological information of the specified area where the micro robot is located.

[0100] The spontaneous deformation module is used to generate spontaneous deformation of the microrobot based on physiological information and obtain the posture state information after deformation.

[0101] The Kalman filter module is used to use the Kalman filter model to perform optimal state estimation on the deformed posture state information to obtain the optimal state estimation of the posture state information.

[0102] The judgment module is used for the control center to determine the correctness or incorrectness of the spontaneous deformation of the micro robot; the correctness or incorrectness result is correct deformation or incorrect deformation.

[0103] The first final posture determination module is used to determine the posture state information after deformation as the final posture state information of the micro robot if the correctness result is that the deformation is correct.

[0104] The second final posture determination module is used to determine the final posture state information of the microrobot based on the error correction instruction and the deformation amount generated by the spontaneous deformation by using the CART classification tree model if the error result is that the deformation is correct.

[0105] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception.

[0106] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception is implemented.

[0107] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception.

[0108] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a micro-robot morphology human-computer collaborative decision-making method based on image and physiological information fusion perception is implemented.

[0109] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0112] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception, characterized in that: The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception includes: Acquiring physiological information of a designated area where the microrobot is located; Based on the physiological information, the microrobot generates spontaneous deformation and obtains posture state information after deformation; Using the Kalman filter model, the optimal state estimation of the deformed posture state information is performed to obtain the optimal state estimation of the posture state information; The control center determines whether the spontaneous deformation of the microrobot is correct or incorrect; the correct or incorrect result is whether the deformation is correct or incorrect; If the correct result is that the deformation is correct, the posture state information after the deformation is determined as the final posture state information of the micro robot; If the correct or incorrect result is a deformation error, the control center sends a correction instruction to the microrobot, and the microrobot uses a CART classification tree model to determine the final posture state information of the microrobot based on the correction instruction and the deformation amount generated by the spontaneous deformation.

2. The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception according to claim 1 is characterized in that: Before obtaining the physiological information of the designated area where the micro-robot is located, the method further includes: The microrobot obtains the position information of the designated area, and moves to the designated area based on the position information.

3. The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception according to claim 1 is characterized in that: The Kalman filter model is used to perform optimal state estimation on the deformed posture state information to obtain the optimal state estimation of the posture state information, which specifically includes: The deformed posture state information is used as the measured value of the posture state information at the initial optimization time, and the optimal state estimation is performed on the initial posture state information according to the set optimization time step to obtain the optimal state estimation of the posture state information.

4. The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception according to claim 3 is characterized in that: The Kalman filter model comprises: in, is the optimal state estimation of the posture state information at the kth optimization moment; is the predicted state estimate of the posture state information at the kth optimization moment determined based on the measured value of the posture state information at the k-1th optimization moment; K(k) is the Kalman gain at the kth optimization moment; Z(k) is the measured value of the posture state information at the kth optimization moment; H(k) is the observation matrix at the kth optimization moment; A(k) is the state transfer matrix at the kth optimization moment; It is the optimal state estimate of the posture state information at the k-1th optimization moment.

5. The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception according to claim 1 is characterized in that: Determining whether the spontaneous deformation of the microrobot is correct or not, comprising: Based on the posture state information after deformation and the optimal state estimation of the posture state information, the correctness or incorrectness result of the spontaneous deformation of the micro robot is determined.

6. The micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception according to claim 5 is characterized in that: Based on the deformed posture state information and the optimal state estimation of the posture state information, determining the correctness or incorrectness of the spontaneous deformation of the micro robot comprises: Determine whether the deformed posture state information is consistent with the optimal state estimation of the posture state information; If so, the correct answer is that the deformation is correct; If not, the correct answer is a deformation error.

7. A micro-robot-based human-machine collaborative decision-making system based on image and physiological information fusion perception, characterized in that: The micro-robot morphology human-machine collaborative decision-making system based on image and physiological information fusion perception includes: A physiological information acquisition module, used to acquire physiological information of a designated area where the microrobot is located; A spontaneous deformation module, used for causing the microrobot to spontaneously deform based on the physiological information and obtaining the posture state information after the deformation; A Kalman filter module is used to use a Kalman filter model to perform optimal state estimation on the deformed posture state information to obtain the optimal state estimation of the posture state information; A judgment module, used for the control center to determine whether the spontaneous deformation of the micro-robot is correct or incorrect; the correct or incorrect result is whether the deformation is correct or incorrect; A first final posture determination module is used to determine the posture state information after deformation as the final posture state information of the micro robot if the correctness result is that the deformation is correct; The second final posture determination module is used for, if the correctness result is a deformation error, the control center sends an error correction instruction to the microrobot, and the microrobot uses the CART classification tree model to determine the final posture state information of the microrobot based on the error correction instruction and the deformation amount generated by the spontaneous deformation.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the micro-robot morphology human-machine collaborative decision-making method based on image and physiological information fusion perception as described in any one of claims 1 to 7 is implemented.

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