Robot remote cooperative control method and system based on Internet of Things

Abstract

The invention discloses a robot remote cooperative control method and system based on the Internet of Things, and the method comprises the steps: collecting the cooperative medical work data and environment data of an Internet of Things robot, and carrying out the preprocessing of the medical work data and the environment data; performing consistency analysis on the medical work data to obtain difference data, and performing anomaly evaluation on the difference data according to a work effect to obtain an anomaly proportion; performing influence analysis on the environment data to obtain an influence factor, and constructing a robot remote cooperative control model according to the abnormal proportion and the inhibition factor; and inputting a control instruction into the robot remote cooperative control model, and outputting a control result. The method not only can improve the precision of robot remote cooperative control based on the Internet of Things, but also has good interpretability, and can be directly applied to a robot remote cooperative control system.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control, and in particular to a robot remote collaborative control method and system based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, IoT robots have been widely used in many fields such as industry, agriculture, medical treatment, and military. IoT robots collect data from the work site in real time through sensors and other equipment, and transmit it to the remote control center through the network, realizing remote monitoring and control of the robots. However, in the scenario of collaborative work of robots, due to factors such as the large number of robots, complex tasks, and changing environments, how to achieve effective collaborative control between robots has become an urgent problem to be solved.

[0003] Traditional robot remote collaborative control methods mainly rely on manually preset control strategies. This method often fails to ensure control accuracy and real-time performance in complex and changing task environments. In addition, due to obstacles in data interaction and information sharing between robots, the effect of collaborative control is greatly reduced. Therefore, how to make full use of the Internet of Things technology to realize the intelligent and adaptive remote collaborative control of robots has become a hot topic and difficulty in current research.

[0004] In order to overcome the above technical problems, the present invention proposes a robot remote collaborative control method based on the Internet of Things. This method collects data and environmental data of the collaborative work of the Internet of Things robots, uses advanced data preprocessing, consistency analysis, anomaly assessment, impact analysis and other technical means, constructs a robot remote collaborative control model, and optimizes it, and finally realizes accurate remote collaborative control of the robots. Summary of the invention

[0005] The purpose of the present invention is to provide a robot remote collaborative control method based on the Internet of Things.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data;

[0009] Performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data according to the work results to obtain an abnormality ratio;

[0010] Performing an impact analysis on the environmental data to obtain an impact factor, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor;

[0011] The robot remote collaborative control model is optimized, control instructions are input into the robot remote collaborative control model, and control results are output.

[0012] Furthermore, the method of performing consistency analysis on the medical work data to obtain difference data includes:

[0013] Cross-checking is used to compare different medical work data, and the medical work data with different comparison results are output as difference data.

[0014] Furthermore, the method of performing abnormality evaluation on the difference data according to the work effect to obtain the abnormality ratio includes:

[0015] According to the similarity, the decision tree is used to initially classify the difference data to obtain the classification group;

[0016] Calculate the information content of the difference data according to classification groups:

[0017]

[0018] The amount of information of the a-th difference data of the j-th classification group is The number of difference data for the jth classification group is The a-th difference data of the j-th classification group is w j,a , difference data w j,a The probability of occurrence is P(w j,a ), the error parameter is ε;

[0019] Calculate the importance of difference data:

[0020]

[0021] The importance of the a-th difference data in the j-th classification group is Difference data w j,a The reference value is w o , the initial working effect is C o , difference data w j,a The working effect is C j,a , the synergistic energy efficiency of the jth classification group is Difference data w j,a and synergistic energy efficiency The correlation degree is The weight of relevance is ω1, and the weight of information is ω2;

[0022] Calculate the anomaly proportion of the differenced data:

[0023]

[0024] The abnormal proportion of the xth test is The abnormal error coefficient is γ.

[0025] Furthermore, the method of performing impact analysis on the environmental data to obtain the impact factor includes:

[0026] Input environmental data into the impact analysis model to calculate the collaborative energy efficiency of robot remote collaborative control:

[0027]

[0028] The first weight is τ1, the second weight is τ2, the third weight is τ3, the fourth weight is τ4, and the task completion time of the i-th robot in the x-th test is s i,x , the reference time for completing the task of the ith robot in the xth test is s i,o , the task completed by the ith robot in the xth test is R i,x , the standard task content of the i-th robot in the x-th test is ZR i,x , the workload of the ith robot in the xth test is G i , the job satisfaction of the i-th robot in the x-th test is B i , the synergistic energy efficiency of the xth test is

[0029] The correlation of environmental data is calculated based on the synergistic energy efficiency:

[0030]

[0031] The synergistic energy efficiency of the x-1th test is The correlation degree of the xth test environment data is θ x , the bth environment data of the xth test is v x,b , the initial value of the bth environmental data is v b,st , the number of environmental data for the xth test is n1;

[0032] Build an entity association network based on the association degree, and calculate the contribution of environmental data based on the single momentum principle:

[0033]

[0034] The initial value of synergistic energy efficiency is The tuning constant is δ and the number of single tests is n x , the contribution of the bth environmental data is ζ b ;

[0035] The relevant data with contribution greater than 0.309 are taken as key data, and the influence coefficient is calculated based on the key data:

[0036]

[0037] The impact factor of the xth test is The control parameter is υ.

[0038] Furthermore, a method for constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor includes:

[0039] Construct the objective function based on the anomaly ratio and suppression factor:

[0040]

[0041] The objective function of the first test is The task completion time of the xth test is s x , the starting time of the task request for the xth test is s o , the number of requests for the xth test task is m x , the loss function is The predicted synergistic energy efficiency of the xth test is

[0042] The robot remote collaborative control model includes scheme selection algorithm, collaborative control algorithm, and deep learning algorithm;

[0043] The solution selection algorithm plans the database retrieval path according to the input requirements and obtains the retrieval solution;

[0044] The cooperative control algorithm allocates wireless resources according to the call scheme and obtains transmission data according to the data transmission mode;

[0045] The deep learning matching algorithm predicts the collaborative energy efficiency and obtains the abnormal proportion and inhibition factor according to the characteristics of the transmitted data, selects the best call-up plan according to the objective function value, and outputs the best call-up plan as a control method.

[0046] Furthermore, the method for optimizing the robot remote collaborative control model includes:

[0047] Introduce particle population and use Sobol sequence to generate particle population. The expression is:

[0048]

[0049] The control parameter is ψ, and the position of the zth particle is The upper limit of the search space is V max , the lower limit of the search space is V min , the random number generated by the Sobol sequence is

[0050] Calculate the fitness of the particle, take the position of the particle with the largest fitness as the target, and update the position of the particle. The expression is:

[0051]

[0052] The position of the zth particle at the t+1th iteration is The position of the zth particle at the tth iteration is The goal of the tth iteration is The random number is p, the random numbers from 0 to 1 are r1, r2, r3, r4, r5, and the maximum number of iterations is t max , the current number of iterations is t, and the search step is ρ;

[0053] Update the search step size, the expression is:

[0054]

[0055] The updated search step length is The step-size adjustment factor is σ, the gamma function is Γ(·), and the normally distributed random variables are α and β respectively;

[0056] Update the particle position according to the nonlinear convergence factor to obtain the converged position. The expression is:

[0057]

[0058] The convergence position of the zth particle in the t+1th iteration is The random number between 0 and 1 is r6, and the nonlinear convergence factor is μ;

[0059] Continue iterating until the maximum number of iterations is reached, then stop iterating, otherwise update the search step size.

[0060] The second aspect is the robot remote collaborative control system based on the Internet of Things, including:

[0061] Data collection module: used to collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data;

[0062] Analysis and evaluation module: used for performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data to obtain abnormality ratio according to the work effect;

[0063] Impact construction module: used for performing impact analysis on the environmental data to obtain impact factors, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor;

[0064] Construct an output module: used to input control instructions into the robot remote collaborative control model and output control results.

[0065] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0066] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0067] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0068] The beneficial effects of the present invention are:

[0069] The present invention is a robot remote collaborative control method and system based on the Internet of Things. Compared with the prior art, the present invention has the following technical effects:

[0070] The present invention can improve the accuracy of robot remote collaborative control based on the Internet of Things through preprocessing, consistency analysis, anomaly assessment, impact analysis, construction of control functions, model building and model optimization steps, thereby improving the precision of robot remote collaborative control based on the Internet of Things, and optimizing robot remote collaborative control based on the Internet of Things, which can greatly save resources and improve work efficiency, and can realize automatic control of robot remote collaboration, and perform anomaly assessment and impact analysis on robot remote collaborative control based on the Internet of Things in real time, which is of great significance to robot remote collaborative control based on the Internet of Things, can adapt to different standards of robot remote collaborative control based on the Internet of Things and different requirements of robot remote collaborative control based on the Internet of Things, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flowchart of the steps of the robot remote collaborative control method based on the Internet of Things of the present invention;

[0072] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0073] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0074] The present invention provides a robot remote collaborative control method and system based on the Internet of Things, comprising the following steps:

[0075] like Figure 1 As shown, in this embodiment, the following steps are included:

[0076] Collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data;

[0077] In actual evaluation, environmental data include temperature, humidity, external vibration, and electromagnetic interference;

[0078] Medical work data includes request instructions, status data, network transmission data, fault diagnosis, alarm data, robot function and information database, target coordinates, target rotation angle, specified motor rotation angle, pulse number, control task number, control task instruction, network transmission delay, data packet loss rate, data misalignment degree, sensor data;

[0079] The xxx robot was used as the research object to study the synergistic effect in simulated tumor resection surgery;

[0080] Performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data according to the work results to obtain an abnormality ratio;

[0081] In the actual evaluation, status data, network transmission data, target coordinates, target rotation angle, specified motor rotation angle, number of pulses, control task instructions, network transmission delay, packet loss rate, and data misalignment degree;

[0082] The abnormal proportion is 0.279;

[0083] Performing an impact analysis on the environmental data to obtain an impact factor, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor;

[0084] In the actual evaluation, the impact factor is 0.2014;

[0085] The robot remote collaborative control model is optimized, control instructions are input into the robot remote collaborative control model, and control results are output.

[0086] In this embodiment, the method for performing consistency analysis on the medical work data to obtain difference data includes:

[0087] Cross-checking is used to compare different medical work data, and the medical work data with different comparison results are output as difference data.

[0088] In this embodiment, the method for performing an abnormality evaluation on the difference data according to the work effect to obtain an abnormality ratio includes:

[0089] According to the similarity, the decision tree is used to initially classify the difference data to obtain the classification group;

[0090] Calculate the information content of the difference data according to classification groups:

[0091]

[0092] The amount of information of the a-th difference data of the j-th classification group is The number of difference data for the jth classification group is The a-th difference data of the j-th classification group is w j,a , difference data w j,a The probability of occurrence is P(w j,a ), the error parameter is ε;

[0093] Calculate the importance of difference data:

[0094]

[0095] The importance of the a-th difference data in the j-th classification group is Difference data w j,a The reference value is w o , the initial working effect is C o , difference data w j,a The working effect is C j,a , the synergistic energy efficiency of the jth classification group is Difference data w j,a and synergistic energy efficiency The correlation degree is The weight of relevance is ω1, and the weight of information is ω2;

[0096] Calculate the anomaly proportion of the differenced data:

[0097]

[0098] The abnormal proportion of the xth test is The abnormal error coefficient is γ.

[0099] In this embodiment, the method of performing impact analysis on the environmental data to obtain the impact factor includes:

[0100] Input environmental data into the impact analysis model to calculate the collaborative energy efficiency of robot remote collaborative control:

[0101]

[0102] The first weight is τ1, the second weight is τ2, the third weight is τ3, the fourth weight is τ4, and the task completion time of the i-th robot in the x-th test is s i,x, the reference time for completing the task of the ith robot in the xth test is s i,o , the task completed by the ith robot in the xth test is R i,x , the standard task content of the i-th robot in the x-th test is ZR i,x , the workload of the ith robot in the xth test is G i , the job satisfaction of the i-th robot in the x-th test is B i , the synergistic energy efficiency of the xth test is

[0103] The correlation of environmental data is calculated based on the synergistic energy efficiency:

[0104]

[0105] The synergistic energy efficiency of the x-1th test is The correlation degree of the xth test environment data is θ x , the bth environment data of the xth test is v x,b , the initial value of the bth environmental data is v b,st , the number of environmental data for the xth test is n1;

[0106] Build an entity association network based on the association degree, and calculate the contribution of environmental data based on the single momentum principle:

[0107]

[0108] The initial value of synergistic energy efficiency is The tuning constant is δ and the number of single tests is n x , the contribution of the bth environmental data is ζ b ;

[0109] The relevant data with contribution greater than 0.309 are taken as key data, and the influence coefficient is calculated based on the key data:

[0110]

[0111] The impact factor of the xth test is The control parameter is υ.

[0112] In this embodiment, the method for constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor includes:

[0113] Construct the objective function based on the anomaly ratio and suppression factor:

[0114]

[0115] The objective function of the first test is The task completion time of the xth test is s x , the starting time of the task request for the xth test is s o , the number of requests for the xth test task is m x , the loss function is The predicted synergistic energy efficiency of the xth test is

[0116] The robot remote collaborative control model includes scheme selection algorithm, collaborative control algorithm, and deep learning algorithm;

[0117] The solution selection algorithm plans the database retrieval path according to the input requirements and obtains the retrieval solution;

[0118] The cooperative control algorithm allocates wireless resources according to the call scheme and obtains transmission data according to the data transmission mode;

[0119] The deep learning matching algorithm predicts the collaborative energy efficiency and obtains the abnormal proportion and inhibition factor according to the characteristics of the transmitted data, selects the best call-up plan according to the objective function value, and outputs the best call-up plan as a control method.

[0120] In this embodiment, the method for optimizing the robot remote collaborative control model includes:

[0121] Introduce particle population and use Sobol sequence to generate particle population. The expression is:

[0122]

[0123] The control parameter is ψ, and the position of the zth particle is The upper limit of the search space is V max , the lower limit of the search space is V min , the random number generated by the Sobol sequence is

[0124] Calculate the fitness of the particle, take the position of the particle with the largest fitness as the target, and update the position of the particle. The expression is:

[0125]

[0126] The position of the zth particle at the t+1th iteration is The position of the zth particle at the tth iteration is The goal of the tth iteration is The random number is p, the random numbers from 0 to 1 are r1, r2, r3, r4, r5, and the maximum number of iterations is t max , the current number of iterations is t, and the search step is ρ;

[0127] Update the search step size, the expression is:

[0128]

[0129] The updated search step length is The step-size adjustment factor is σ, the gamma function is Γ(·), and the normally distributed random variables are α and β respectively;

[0130] Update the particle position according to the nonlinear convergence factor to obtain the converged position. The expression is:

[0131]

[0132] The convergence position of the zth particle in the t+1th iteration is The random number between 0 and 1 is r6, and the nonlinear convergence factor is μ;

[0133] Continue iterating until the maximum number of iterations is reached, then stop iterating, otherwise update the search step size.

[0134] The second aspect is the robot remote collaborative control system based on the Internet of Things, including:

[0135] Data collection module: used to collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data;

[0136] Analysis and evaluation module: used for performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data to obtain abnormality ratio according to the work effect;

[0137] Impact construction module: used for performing impact analysis on the environmental data to obtain impact factors, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor;

[0138] Construct an output module: used to input control instructions into the robot remote collaborative control model and output control results.

[0139] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0140] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0141] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0142] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a robot remote collaborative control device based on the Internet of Things at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned robot remote collaborative control methods based on the Internet of Things.

[0143] The above application Figure 1The robot remote collaborative control method based on the Internet of Things disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0144] The electronic device may also perform Figure 1 A remote collaborative control method of robots based on the Internet of Things is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0145] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute any of the aforementioned IoT-based robot remote collaborative control methods.

[0146] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0151] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0152] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0153] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0154] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.

[0155] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robot remote collaborative control method based on the Internet of Things, characterized in that: The following steps are involved: Collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data; Performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data according to the work results to obtain an abnormality ratio; Performing an impact analysis on the environmental data to obtain an impact factor, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor; The robot remote collaborative control model is optimized, control instructions are input into the robot remote collaborative control model, and control results are output.

2. The method for remote collaborative control of robots based on the Internet of Things according to claim 1, characterized in that: The method of performing consistency analysis on the medical work data to obtain difference data comprises: Cross-checking is used to compare different medical work data, and the medical work data with different comparison results are output as difference data.

3. The method for remote collaborative control of robots based on the Internet of Things according to claim 1 is characterized in that: The method of performing abnormal evaluation on the difference data according to the work effect to obtain the abnormal proportion includes: According to the similarity, the decision tree is used to initially classify the difference data to obtain the classification group; Calculate the information content of the difference data according to classification groups: The amount of information of the a-th difference data of the j-th classification group is The number of difference data for the jth classification group is The a-th difference data of the j-th classification group is w j,a , difference data w j,a The probability of occurrence is P(w j,a ), the error parameter is ε; Calculate the importance of difference data: The importance of the a-th difference data in the j-th classification group is Difference data w j,a The reference value is w o , the initial working effect is C o , difference data w j,a The working effect is C j,a , the synergistic energy efficiency of the jth classification group is Difference data w j,a and synergistic energy efficiency The correlation degree is The weight of relevance is ω1, and the weight of information is ω2; Calculate the anomaly proportion of the differenced data: The abnormal proportion of the xth test is The abnormal error coefficient is γ.

4. The method for remote collaborative control of robots based on the Internet of Things according to claim 1, characterized in that: The method of performing impact analysis on the environmental data to obtain impact factors includes: Input environmental data into the impact analysis model to calculate the collaborative energy efficiency of robot remote collaborative control: The first weight is τ1, the second weight is τ2, the third weight is τ3, the fourth weight is τ4, and the task completion time of the i-th robot in the x-th test is s i,x , the reference time for completing the task of the ith robot in the xth test is s i,o , the task completed by the ith robot in the xth test is R i,x , the standard task content of the i-th robot in the x-th test is ZR i,x , the workload of the ith robot in the xth test is G i , the job satisfaction of the i-th robot in the x-th test is B i , the synergistic energy efficiency of the xth test is The correlation of environmental data is calculated based on the synergistic energy efficiency: The synergistic energy efficiency of the x-1th test is The correlation degree of the xth test environment data is θ x , the bth environment data of the xth test is v x,b , the initial value of the bth environmental data is v b,st , the number of environmental data for the xth test is n1; Build an entity association network based on the association degree, and calculate the contribution of environmental data based on the single momentum principle: The initial value of synergistic energy efficiency is The tuning constant is δ and the number of single tests is n x , the contribution of the bth environmental data is ζ b ; The relevant data with contribution greater than 0.309 are taken as key data, and the influence coefficient is calculated based on the key data: The impact factor of the xth test is The control parameter is υ.

5. The method for remote collaborative control of robots based on the Internet of Things according to claim 1, characterized in that: The method for constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor comprises: Construct the objective function based on the anomaly ratio and suppression factor: The objective function of the first test is The task completion time of the xth test is s x , the starting time of the task request for the xth test is s o , the number of requests for the xth test task is m x , the loss function is The predicted synergistic energy efficiency of the xth test is The robot remote collaborative control model includes scheme selection algorithm, collaborative control algorithm, and deep learning algorithm; The solution selection algorithm plans the database retrieval path according to the input requirements and obtains the retrieval solution; The cooperative control algorithm allocates wireless resources according to the call scheme and obtains transmission data according to the data transmission mode; The deep learning matching algorithm predicts the collaborative energy efficiency and obtains the abnormal proportion and inhibition factor according to the characteristics of the transmitted data, selects the best call-up plan according to the objective function value, and outputs the best call-up plan as a control method.

6. The method for remote collaborative control of robots based on the Internet of Things according to claim 1, characterized in that: The method for optimizing the robot remote collaborative control model comprises: Introduce particle population and use Sobol sequence to generate particle population. The expression is: The control parameter is ψ, and the position of the zth particle is The upper limit of the search space is V max , the lower limit of the search space is V min , the random number generated by the Sobol sequence is x; Calculate the fitness of the particle, take the position of the particle with the largest fitness as the target, and update the position of the particle. The expression is: The position of the zth particle at the t+1th iteration is The position of the zth particle at the tth iteration is The goal of the tth iteration is The random number is p, the random numbers from 0 to 1 are r1, r2, r3, r4, r5, and the maximum number of iterations is t max , the current number of iterations is t, and the search step is ρ; Update the search step size, the expression is: The updated search step length is The step-size adjustment factor is σ, the gamma function is Γ(·), and the normally distributed random variables are α and β respectively; Update the particle position according to the nonlinear convergence factor to obtain the converged position. The expression is: The convergence position of the zth particle in the t+1th iteration is The random number between 0 and 1 is r6, and the nonlinear convergence factor is μ; Continue iterating until the maximum number of iterations is reached, then stop iterating, otherwise update the search step size.

7. A robot remote collaborative control system based on the Internet of Things, used to execute the method described in any one of claims 1 to 7, characterized in that: include: Data collection module: used to collect medical work data and environmental data coordinated by the IoT robot, and pre-process the medical work data and the environmental data; Analysis and evaluation module: used for performing consistency analysis on the medical work data to obtain difference data, and performing abnormality evaluation on the difference data to obtain abnormality ratio according to the work effect; Impact construction module: used for performing impact analysis on the environmental data to obtain impact factors, and constructing a robot remote collaborative control model according to the abnormal proportion and the inhibition factor; Construct an output module: used to input control instructions into the robot remote collaborative control model and output control results.

8. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the method according to any one of claims 1 to 6.

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