Intelligent computing platform based on high-throughput computing technology

Abstract

The invention provides an intelligent computing platform based on a high-throughput computing technology, which integrates multiple functions and tools, and comprises an algorithm warehouse, a chip tool chain, application development middleware, an algorithm and business strategy block pool and a basic system software driver, the platform can provide powerful support for products such as an intelligent industrial control board card, an intelligent computing industrial control computer and an intelligent chip, helps a robot developer to quickly construct an intelligent robot with powerful sensing, reasoning and decision-making capabilities, reduces the complexity and cost of robot development, provides rich robot algorithms and application reference schemes, and improves the development efficiency. The development efficiency of the robot is greatly improved, the performance and stability of the robot can be effectively improved through an internal chip tool chain and a basic system software drive, multiple fields of natural language interaction, video analysis, visual perception and the like are covered, and the robot has more comprehensive capacity.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and robotics technology, and in particular to an intelligent computing platform based on high-throughput computing technology. Background Art

[0002] With the rapid development of artificial intelligence and robotics, the requirements for the intelligence of robots are getting higher and higher. However, the existing robot development platforms often have problems such as single functions, insufficient algorithms and low development efficiency, which cannot meet the complex needs of current robots in natural language interaction, video content analysis, three-dimensional visual perception and other aspects. The existing development model is that robot developers first select suitable intelligent industrial control boards, intelligent industrial computers, intelligent chips and other products according to the needs, and install them on the intelligent computing platform of the present invention. Then, developers can select the required algorithms from the algorithm warehouse and use the chip tool chain to optimize and train the model. Through the application development middleware, refer to the scenario application solutions therein, and quickly build The basic behavioral functions of the robot; at the same time, suitable algorithms and strategies are selected from the algorithm and business strategy block pool for the robot's navigation, modeling, data fusion and other tasks; the basic system software driver provides stable and efficient support for the whole process. The development process is complicated, and there are conflicts between the algorithms and protocols that are connected as needed, which is not conducive to the rapid development and application of the robot. For this reason, the applicant proposes an intelligent computing platform based on high-throughput computing technology according to the existing development needs of artificial intelligence and robots. It integrates a variety of functions and tools, including algorithm warehouse, chip tool chain, application development middleware, algorithm and business strategy block pool and basic system software driver, which can quickly help developers to develop and apply artificial intelligence and robots. Summary of the invention

[0003] To solve the above technical problems, the present invention proposes an intelligent computing platform based on high-throughput computing technology, which integrates multiple functions and tools, including an algorithm warehouse, a chip tool chain, an application development middleware, an algorithm and business strategy block pool, and a basic system software driver; the platform can provide strong support for products such as intelligent industrial control boards, intelligent industrial computers, and intelligent chips, helping robot developers to quickly build intelligent robots with powerful perception, reasoning, and decision-making capabilities, which reduces the complexity and cost of robot development, provides rich robot algorithms and application reference solutions, and greatly improves the development efficiency of robots.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] An intelligent computing platform based on high-throughput computing technology, characterized in that: the intelligent computing platform based on high-throughput computing includes an algorithm warehouse, a chip tool chain, an application development middleware, an algorithm and business strategy block pool and a basic system software driver, specifically:

[0006] Algorithm warehouse: covers algorithms in the fields of intelligent interaction, video structuring, and 2.5 / 3D vision, supporting robots to perform natural language interaction, video content analysis, and 3D visual perception tasks;

[0007] Chip tool chain: Provides model training, conversion, compilation, and prediction library tools to optimize the performance and efficiency of machine learning models;

[0008] Application development middleware: A development framework for robot behavior, including reference solutions for automatic recharging, behavior recognition, and environmental perception scenarios, helping developers quickly build basic robot functions;

[0009] Algorithm and business strategy block pool: It integrates a variety of algorithms and business strategies, including SLAM intelligent mapping, parallel computing and multi-mode fusion algorithms, which can be used for robot navigation path planning, environmental modeling and multi-sensor data fusion tasks;

[0010] Basic system software driver: provides high-performance driver, video processing framework, memory management and kernel driver functions to ensure the stability and efficiency of robot operation.

[0011] Furthermore, the algorithm of the intelligent computing platform based on high-throughput computing and the SLAM intelligent mapping algorithm in the business strategy block pool are specifically:

[0012] When the robot’s position in the environment is x t , the observed feature point is z t , the map feature point is m, then the algorithm formula for predicting the robot's posture through the Bayesian filtering method is:

[0013] p(x t |z 1：t-1 ,u 1：t )=∫p(x t |x t-1 ,u t )p(x t-1 |z 1：t-1 ,u 1：t-1 )dx t-1

[0014] Where: p(x t |x t-1 ,u t ) represents the state transition probability, which describes the control input u t Under the action of t-1 Transfer to pose x t The probability of

[0015] p(x t-1 |z 1：t-1 ,u1：t-1 ) is the posterior probability of the pose at the previous moment.

[0016] Furthermore, the parallel computing in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is specifically as follows:

[0017] A large-scale parallel computing architecture is adopted to process multiple computing tasks at the same time, and the above integral operation is decomposed into multiple subtasks for parallel execution. Efficient memory management and data caching technology are used to reduce data access delay and further improve computing efficiency. The specific formula for updating the robot's posture estimation is:

[0018] p(x t |z 1：t ,u 1：t )∝p(z t |x t ,m)p(x t |z 1：t-1 ,u 1：t )

[0019] Where: p(z t |x t , m) is the observation model describing the robot at a given position x t and map feature point m, the feature point z is observed t probability.

[0020] Furthermore, the multi-fusion algorithm in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is specifically:

[0021] When there are two sensor modes, the measurement value of mode one is y1, and its measurement is p(y1|x), and the measurement value of mode two is y2, and its measurement model is p(y2|x); then the goal is to fuse these two measurements to estimate the state x; using the Kalman filter fusion method, first predict and update according to their respective measurements to obtain the estimated value and And the corresponding covariance matrices P1 and P2; then, the fused estimate And the covariance matrix P is calculated by the following formula:

[0022]

[0023]

[0024] The benefits of this application are:

[0025] 1. Intelligent computing platform based on high-throughput computing integrates multiple key functions and tools into one platform, reducing the complexity and cost of development;

[0026] 2. The intelligent computing platform based on high-throughput computing provides a wealth of algorithms and application reference solutions, greatly improving the efficiency of robot development;

[0027] 3. The intelligent computing platform chip tool chain and basic system software driver based on high-throughput computing can effectively improve the performance and stability of the robot;

[0028] 4. The intelligent computing platform based on high-throughput computing covers multiple fields such as natural language interaction, video analysis, and visual perception, enabling robots to have more comprehensive capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0031] like Figure 1 As shown, an intelligent computing platform based on high-throughput computing technology is shown. The intelligent computing platform based on high-throughput computing includes an algorithm warehouse, a chip tool chain, an application development middleware, an algorithm and business strategy block pool, and a basic system software driver, specifically:

[0032] Algorithm warehouse: covers algorithms in the fields of intelligent interaction, video structuring, and 2.5 / 3D vision, supporting robots to perform natural language interaction, video content analysis, and 3D visual perception tasks;

[0033] Chip tool chain: Provides model training, conversion, compilation, and prediction library tools to optimize the performance and efficiency of machine learning models;

[0034] Application development middleware: A development framework for robot behavior, including reference solutions for automatic recharging, behavior recognition, and environmental perception scenarios, helping developers quickly build basic robot functions;

[0035] Algorithm and business strategy block pool: It integrates a variety of algorithms and business strategies, including SLAM intelligent mapping, parallel computing and multi-mode fusion algorithms, which can be used for robot navigation path planning, environmental modeling and multi-sensor data fusion tasks;

[0036] Basic system software driver: provides high-performance driver, video processing framework, memory management and kernel driver functions to ensure the stability and efficiency of robot operation.

[0037] The specific SLAM intelligent mapping algorithm in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is:

[0038] When the robot’s position in the environment is xt , the observed feature point is z t , the map feature point is m, then the algorithm formula for predicting the robot's posture through the Bayesian filtering method is:

[0039] p(x t |z 1：t-1 ,u 1：t )=∫p(x t |x t-1 ,u t )p(x t-1 |z 1：t-1 ,u 1：t-1 )dx t-1

[0040] Where: p(x t |x t-1 ,u t ) represents the state transition probability, which describes the control input u t Under the action of t-1 Transfer to pose x t The probability of

[0041] p(x t-1 |z 1：t-1 ,u 1：t-1 ) is the posterior probability of the pose at the previous moment.

[0042] The parallel computing in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is specifically as follows:

[0043] A large-scale parallel computing architecture is adopted to process multiple computing tasks at the same time, and the above integral operation is decomposed into multiple subtasks for parallel execution. Efficient memory management and data caching technology are used to reduce data access delay and further improve computing efficiency. The specific formula for updating the robot's posture estimation is:

[0044] p(x t |z 1：t ,u 1：t )∝p(z t |x t ,m)p(x t |z 1：t-1 ,u 1：t )

[0045] Where: p(z t |x t , m) is the observation model describing the robot at a given position x t and map feature point m, the feature point z is observed t probability.

[0046] The multi-fusion algorithm in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is specifically:

[0047] When there are two sensor modes, the measurement value of mode one is y1, and its measurement is p(y1|x), and the measurement value of mode two is y2, and its measurement model is p(y2|x); then the goal is to fuse these two measurements to estimate the state x; using the Kalman filter fusion method, first predict and update according to their respective measurements to obtain the estimated value and And the corresponding covariance matrices P1 and P2; then, the fused estimate And the covariance matrix P is calculated by the following formula:

[0048]

[0049]

[0050] As shown, when using the intelligent computing platform based on high-throughput computing, robot developers select hardware configurations according to their needs, such as intelligent industrial control boards, intelligent industrial computers and intelligent chips, and select the required algorithms in the algorithm warehouse, such as intelligent interaction, video structuring and 2.5 / 3D vision, and then enter the next chip tool chain to optimize the performance and efficiency of the machine learning model. Through training, conversion and compilation, the application development middleware is achieved to quickly build the basic behavior functions of the robot, perform automatic recharging, behavior recognition and environmental perception, and perform tasks such as robot navigation path planning, environmental modeling and multi-sensor data fusion in the algorithm and business strategy block pool to accelerate development; As shown, with the advantages of high-throughput computing, a large amount of observation data can be processed quickly; by processing multiple observation data points in parallel and performing probability calculations at the same time, real-time updates of posture are achieved; in addition, hardware acceleration technology is used, such as a dedicated image processing unit (GPU) for image feature extraction and matching to speed up the calculation of the observation model;

[0051] In actual calculations, in order to simplify calculations and improve efficiency, approximate methods such as particle filtering or extended Kalman filtering are often used. Taking particle filtering as an example, a large number of particles are generated by random sampling to represent the probability distribution of posture. Each particle has a corresponding posture and weight, and the weight is updated according to the consistency of observation and prediction.

[0052] Suppose we have N particles, and the position of the i-th particle is The weight is The weight update formula is:

[0053]

[0054] in: is a suggested distribution, usually a simple model is chosen to generate particles.

[0055] As shown in the intelligent computing platform, high-throughput computing enables the calculation and update of many particles to be processed simultaneously; by parallel computing the weight update of multiple particles, the execution speed of the algorithm is significantly improved; and by using the distributed computing framework, the computing tasks are distributed to multiple computing nodes to achieve large-scale particle filter calculations;

[0056] As shown in the figure, after multiple iterations and resampling, the distribution of particles gradually converges to the true posterior probability distribution of the posture, thereby achieving accurate estimation of the robot's posture and construction of the map; the high-speed computing and big data processing capabilities of the intelligent computing platform make this convergence process faster and more stable.

[0057] As shown in the intelligent computing platform, high-throughput computing is reflected in the use of parallel matrix operation technology to process multiple measurements and covariance matrices at the same time and quickly complete fusion calculations. In addition, through optimized algorithms and hardware acceleration, computing time and resource consumption are reduced; through its powerful high-throughput computing capabilities, optimized algorithm architecture and efficient data processing mechanism, these core algorithms can be implemented quickly and accurately, thereby significantly improving the performance and intelligence level of the robot.

[0058] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. An intelligent computing platform based on high-throughput computing technology, characterized in that: The intelligent computing platform based on high-throughput computing includes an algorithm warehouse, a chip tool chain, an application development middleware, an algorithm and business strategy block pool, and a basic system software driver, specifically: Algorithm warehouse: covers algorithms in the fields of intelligent interaction, video structuring, and 2.5 / 3D vision, supporting robots to perform natural language interaction, video content analysis, and 3D visual perception tasks; Chip tool chain: Provides model training, conversion, compilation, and prediction library tools to optimize the performance and efficiency of machine learning models; Application development middleware: A development framework for robot behavior, including reference solutions for automatic recharging, behavior recognition, and environmental perception scenarios, helping developers quickly build basic robot functions; Algorithm and business strategy block pool: It integrates a variety of algorithms and business strategies, including SLAM intelligent mapping, parallel computing and multi-mode fusion algorithms, which can be used for robot navigation path planning, environmental modeling and multi-sensor data fusion tasks; Basic system software driver: provides high-performance driver, video processing framework, memory management and kernel driver functions to ensure the stability and efficiency of robot operation.

2. The intelligent computing platform based on high-throughput computing technology according to claim 1, characterized in that: The algorithm of the intelligent computing platform based on high-throughput computing and the SLAM intelligent mapping algorithm in the business strategy block pool are specifically: When the robot’s position in the environment is x t , the observed feature point is z t , the map feature point is m, then the algorithm formula for predicting the robot's posture through the Bayesian filtering method is: p(x t |z 1：t-1 ,u 1:t )=∫p(x t |x t-1 ,u t )p(x t-1 |z 1：t-1 ,u 1：t-1 )dx t-1 Where: p(x t |x t-1 ,u t ) represents the state transition probability, which describes the control input u t Under the action of t-1 Transfer to pose x t The probability of p(x t-1 |z 1：t-1 ,u 1：t-1 ) is the posterior probability of the pose at the previous moment.

3. The intelligent computing platform based on high-throughput computing technology according to claim 1, characterized in that: The parallel computing in the algorithm and business strategy block pool of the intelligent computing platform based on high-throughput computing is specifically as follows: A large-scale parallel computing architecture is adopted to process multiple computing tasks at the same time, and the above integral operation is decomposed into multiple subtasks for parallel execution. Efficient memory management and data caching technology are used to reduce data access delay and further improve computing efficiency. The specific formula for updating the robot's posture estimation is: p(x t |z 1：t ,u 1：t )∝p(z t |x t，m )p(x t |z 1：t ,u 1:t ) Where: p(z t |x t , m) is the observation model describing the robot at a given position x t and map feature point m, the feature point z is observed t probability.

4. The intelligent computing platform based on high-throughput computing technology according to claim 1, characterized in that: The algorithm of the intelligent computing platform based on high-throughput computing and the multi-fusion algorithm in the business strategy block pool are specifically: When there are two sensor modes, the measurement value of mode one is y1, and its measurement is p(y1|x), and the measurement value of mode two is y2, and its measurement model is p(y2|x); then the goal is to fuse these two measurements to estimate the state x; using the Kalman filter fusion method, first predict and update according to their respective measurements to obtain the estimated value and And the corresponding covariance matrices P1 and P2; then, the fused estimate And the covariance matrix P is calculated by the following formula:

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