Magnetic moment measurement system and method

Abstract

The invention discloses a magnetic moment measuring system and method, and relates to the field of magnetic measurement, and the magnetic moment measuring system comprises a magnetic sensor array, a mechanical arm and a data processing device. The magnetic sensor array is used for collecting magnetic field data of a measurement area; the mechanical arm is used for carrying a magnet to enter the measurement area and transmitting real-time pose information of the tail end of the mechanical arm to the data processing device; and the data processing device is used for determining the magnetic moment of the magnet by adopting a particle swarm optimization algorithm and a differential evolution algorithm according to the magnetic field data and the real-time pose information of the tail end of the mechanical arm, according to the magnetic field data and the real-time pose information of the tail end of the mechanical arm, priori parameters of the magnet are not needed, the resolving precision and robustness of the magnetic moment are improved, and the method can be suitable for on-site real-time measurement.

Description

Technical Field

[0001] This application relates to the field of magnetic measurement, and in particular to a magnetic moment measurement system and method. Background Technology

[0002] Magnetic materials have been widely used in many fields such as industrial automation, precision manufacturing, aerospace and energy equipment. As a core parameter characterizing magnetic properties, the magnetic moment directly determines the performance of related systems in terms of positioning accuracy, driving efficiency and control stability. Therefore, accurate measurement of the magnetic moment is a basic requirement to ensure the reliable operation of cross-industry equipment.

[0003] Currently, magnetic moment measurement technologies are mainly divided into three categories: (1) Coil method: the traceability of the measurement value is achieved by comparing with the standard sample, but the equipment is large and the operation process is long, making it difficult to adapt to the rapid measurement scenario on site; (2) Magnetometer array method: in-situ measurement is achieved by using multi-point magnetic field inversion, but the requirements for sensor calibration accuracy, temperature drift suppression and background magnetic field shielding are extremely high, and system-level errors are easily generated in complex environments; (3) Quantum reference method: it can provide traceability without physical objects, but its device structure is complex and the cost is high, and it can only be used for laboratory metrology at present. In addition, the above methods all have common limitations: the geometric parameters and material properties of the magnet need to be obtained in advance, which greatly limits their application in online monitoring, real-time measurement and model-free scenarios. Summary of the Invention

[0004] The purpose of this application is to provide a magnetic moment measurement system and method that can improve the efficiency and accuracy of magnetic moment measurement.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a magnetic moment measurement system, including: a magnetic sensor array, a robotic arm, and a data processing device;

[0007] The magnetic sensor array is used to collect magnetic field data of the measurement area;

[0008] The robotic arm is used to carry the magnet into the measurement area and transmit the real-time pose information of the robotic arm end effector to the data processing device.

[0009] The data processing device is used to determine the magnetic moment of the magnet based on the magnetic field data and the real-time pose information of the robotic arm end effector, using a particle swarm optimization algorithm and a differential evolution algorithm.

[0010] Secondly, this application provides a method for measuring magnetic moment, including:

[0011] Magnetic field data of the measurement area is acquired using a magnetic sensor array;

[0012] A robotic arm carries a magnet into the measurement area and transmits the real-time pose information of the robotic arm's end effector to the data processing device.

[0013] The magnetic moment of the magnet is determined by a data processing device based on the magnetic field data and the real-time pose information of the robotic arm end effector, using a particle swarm optimization algorithm and a differential evolution algorithm.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects:

[0015] This application provides a magnetic moment measurement system and method. Through the cooperation of a magnetic sensor array, a robotic arm, and a data processing device, a measurement closed loop is formed by the collaboration of hardware and software. The data processing device uses particle swarm optimization algorithm and differential evolution algorithm to determine the magnetic moment of the magnet. It does not require prior parameters of the magnet, which improves the accuracy and robustness of the magnetic moment calculation and is suitable for real-time on-site measurement. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A structural diagram of a magnetic moment measurement system provided in an embodiment of this application;

[0018] Figure 2 A block diagram of a magnetic moment measurement system provided in one embodiment of this application;

[0019] Figure 3 This is a schematic flowchart of a magnetic moment measurement method provided in an embodiment of this application.

[0020] Explanation of reference numerals in the attached drawings: 101-Magnetic sensor array, 102-Robotic arm, 103-Data processing device, 104-Non-magnetic tray, 105-Magnet, 201-Microcontroller. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The purpose of this application is to solve the problems of low accuracy, poor efficiency and weak anti-interference ability in existing magnetic moment measurement technology. By using multi-sensor collaborative sampling, intelligent algorithm optimization and robotic arm posture synchronization, high-precision and high-time-efficiency measurement of magnetic moment can be achieved.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, a magnetic moment measurement system is provided, including: a magnetic sensor array, a robotic arm, and a data processing device.

[0025] The magnetic sensor array is used to collect magnetic field data of the measurement area. In a specific application example, the magnetic field data includes magnetic field data without magnets and magnetic field data with magnets. The magnetic sensor array adopts a 4×4 grid layout MAG3110 magnetic sensor array.

[0026] The robotic arm is used to carry the magnet into the measurement area and transmit the real-time pose information of its end effector to the data processing device. Specifically, the end effector of the robotic arm is used to hold the magnet, and the robotic arm and the data processing device establish a communication connection via a wireless TCP / IP protocol. A non-magnetic tray is fixed to the end effector of the robotic arm via a flange, and the magnet is placed on the non-magnetic tray. The real-time pose information of the robotic arm end effector is the coordinates of the center point of the flange end effector.

[0027] The data processing device is used to determine the magnetic moment of the magnet based on the magnetic field data and the real-time pose information of the robotic arm end effector, using a particle swarm optimization algorithm and a differential evolution algorithm.

[0028] In a specific application example, the data processing device is a computer. The computer serves as the core control unit, integrating data preprocessing, Particle Swarm Optimization (PSO) algorithm, Differential Evolution Algorithm (DE) algorithm, and COMSOL magnetic field simulation module, constructing a closed-loop working system of "magnetic field signal acquisition - dynamic pose tracking - algorithm iterative optimization - simulation result verification".

[0029] In another exemplary embodiment, the magnetic moment measurement system further includes a non-magnetic support stage. The magnetic sensor array is fixed to the surface of the non-magnetic support stage.

[0030] In another exemplary embodiment, the magnetic moment measurement system further includes a microcontroller. The microcontroller filters the magnetic field data before transmitting it to the data processing device. Specifically, the input terminal of the microcontroller is connected to the output terminal of the magnetic sensor array, and the output terminal of the microcontroller is connected to the input terminal of the data processing device via a serial port. The magnetic sensor array is fixed in a 4×4 grid on the surface of a non-magnetic support platform. The microcontroller collects triaxial magnetic field data from 16 sensors in a time-division multiplexing manner and uploads it to the data processing device.

[0031] When performing magnetic moment measurement, the robotic arm transmits real-time position and orientation information of its end effector to the data processing device, and the magnetic sensor array transmits magnetic field data to the data processing device through a microcontroller, forming a closed loop for measurement data transmission at the hardware level.

[0032] In a specific application example, the data processing device includes: a net magnetic field acquisition module, a coordinate transformation module, a population optimization module, a simulation module, and a magnetic moment determination module.

[0033] The computer first receives the magnetic field data in the absence of magnets transmitted by the microcontroller, stores it as a reference value, and simultaneously receives 16 channels of magnetic field data containing magnets transmitted by the microcontroller and the real-time pose information of the robotic arm end effector transmitted by the robotic arm.

[0034] The net magnetic field acquisition module is used to determine the net magnetic field data based on the magnetic field data containing the magnet and the magnetic field data without the magnet. The net magnetic field data is the difference between the magnetic field data containing the magnet and the magnetic field data without the magnet.

[0035] The coordinate transformation module is used to convert the real-time pose information of the robotic arm end effector into the real-time pose information of the magnet.

[0036] Specifically, taking the initial moment when the magnet is directly below the center point of the flange, let the coordinates of the center point of the flange end be (x1, y1, z1, Rx1, Ry1, Rz1) = (0, 0, 0, 0, 0, 0), and the coordinates of the magnet be (x2, y2, z2, Rx2, Ry2, Rz2) = (0, 0, h, 0, 0, 0). Where x1 is the x-axis coordinate of the center point of the flange end, y1 is the y-axis coordinate of the center point of the flange end, z1 is the z-axis coordinate of the center point of the flange end, Rx1 is the yaw angle of the center point of the flange end, Ry1 is the pitch angle of the center point of the flange end, Rz1 is the roll angle of the center point of the flange end, x2 is the x-axis coordinate of the magnet, y2 is the y-axis coordinate of the magnet, z2 is the z-axis coordinate of the magnet, Rx2 is the yaw angle of the magnet, Ry2 is the pitch angle of the magnet, Rz2 is the roll angle of the magnet, and h is the distance from the magnet to the center point of the flange end. The new coordinates after a period of time can be obtained by the following formula: loca new =Rot·loca old ; where, loca newFor the new coordinates, loca old The coordinates are the old coordinates, and Rot is the rotation matrix from the initial coordinates to the current coordinates. The calculation first uses the initial and current coordinates of the center point at the end of the flange to calculate the rotation matrix, and then uses the initial coordinates of the magnet and the rotation matrix to obtain the current coordinates of the magnet.

[0037] The population optimization module is used to iteratively optimize the magnetic moment parameters using the particle swarm optimization algorithm. During the optimization process, the differential evolution algorithm is used to mutate and crossover the particles to obtain the magnetic moment parameters.

[0038] Specifically, the algorithm parameters are first initialized, including the particle swarm size, maximum number of iterations, magnetic moment parameter range, DE scaling factor, crossover probability, and PSO inertia weight. Then, multiple initial particle groups are randomly generated within the magnetic moment parameter range, with each particle corresponding to a set of magnetic moment parameters. The magnet is radially divided into five coaxial sub-magnets, each assigned an independent magnetic moment parameter. The PSO algorithm is then used to update particle positions, leveraging its global search capability for rapid convergence. The DE algorithm is then used to mutate and crossover particles, enhancing local fine-tuning and iteratively updating the magnetic moment parameters. Finally, the fitness value of each particle is calculated, and the best-performing particles are retained to update the population.

[0039] In this differential evolution algorithm, the crossover probability of particles changes according to the number of iterations during the crossover operation. Specifically, the formula is... The crossover probabilities are determined; where CR is the crossover probability, CRmin is the lower bound of the crossover probability (CRmin = 0.8), CRmax is the upper bound of the crossover probability (CRmax = 0.2), k is the current iteration number, and K is the maximum iteration number. This setup allows the PSO algorithm to dominate the search in the early stages of iteration, gradually shifting to the DE algorithm as the iteration number increases. Compared to the traditional PSO-DE fusion algorithm, this improves convergence speed while avoiding premature convergence caused by particle swarm optimization.

[0040] The simulation module is used to perform magnetic field simulation using a pre-built magnetic field simulation model based on the magnetic moment parameters and the real-time pose information of the magnet, thereby obtaining simulated magnetic field data. Specifically, the magnetic moment parameters and the real-time pose information of the magnet are input into the magnetic field simulation model. MATLAB is used to call COMSOL to solve the Maxwell equations using the finite element method to obtain the spatial magnetic field distribution of the magnet. Then, the relative spatial position of the magnet and the magnetic sensor array is calculated based on their positions, resulting in the triaxial magnetic field of the magnet at each magnetic sensor, which is used as the simulated magnetic field data.

[0041] The magnetic field simulation model constructed in this embodiment includes the spatial coordinates of the magnetic sensor array, the geometric structure of multiple coaxial sub-magnets that divide the magnet radially, and the magnetic-space coupling boundary conditions. The number of sub-magnets is five.

[0042] The magnetic moment determination module is used to determine whether the convergence condition is met based on the simulated magnetic field data and the net magnetic field data. If it is, the magnetic moment of the magnet is determined based on the magnetic moment parameters. If not, the magnetic moment parameters are iteratively optimized again using the ion swarm optimization algorithm.

[0043] The convergence condition is that the maximum number of iterations is reached or the error between the simulated magnetic field data and the net magnetic field data obtained from three consecutive iterations is less than 10. -5 T. The error is the sum of the triaxial errors of the 16 magnetic sensors.

[0044] Furthermore, if the convergence condition is not met, and the error in the current iteration is smaller than the error in the previous iteration, then the magnetic moment parameter obtained in the current iteration is used as the global optimal magnetic moment parameter for iterative optimization again; otherwise, the global optimal magnetic moment parameter is not changed and iterative optimization is performed again.

[0045] In this embodiment, the magnetic moment parameters include the magnetic moment parameters of multiple sub-magnets. The magnetic moment determination module performs vector superposition of the magnetic moment parameters of the multiple sub-magnets to obtain the magnetic moment of the magnet.

[0046] Specifically, the magnetic moment parameters are M = (m1, m2, m3, m4, m5, n, p), where m1 to m5 are the magnetic moment moduli of the five sub-magnets, n is the cosine of the angle between the x-axis and p is the cosine of the angle between the y-axis. That is, the magnetic moments of the five sub-magnets are in the same direction, but their magnitudes are different.

[0047] The magnetic moment measurement system provided in this application consists of hardware and software working together. The hardware includes a computer as the core, connected to a 4×4 grid-layout MAG3110 magnetic sensor array, a robotic arm, and a non-magnetic support platform, forming a measurement closed loop. The software employs a PSO-DE nested fusion algorithm, combined with COMSOL magnetic field simulation, to calculate the magnetic moment. The core process is as follows: the computer converts the flange pose returned by the robotic arm into the magnet's center of mass pose; the magnetic field data containing the magnet is subtracted from the non-magnetic magnetic field data to obtain the net magnetic field data; the magnetic moment parameters are iteratively optimized through PSO-DE, driving COMSOL to obtain the simulated magnetic field data at the magnetic sensor array; the simulated magnetic field data is compared with the net magnetic field data, and the magnetic moment is output after the convergence condition is met. This application does not require prior magnet parameters and improves the calculation accuracy and robustness through fusion algorithms and simulation modeling, making it suitable for real-time on-site measurement.

[0048] In terms of implementation, the environmental baseline calibration is first completed by acquiring magnetic field data in a non-magnetic state. Subsequently, when the robotic arm carries the magnet into the measurement area, it simultaneously acquires magnetic field data containing magnetic interference and flange pose information. The computer first converts the flange pose information into the spatial pose of the magnet's center of mass, and then obtains the net magnetic field data generated only by the magnet by subtracting the environmental baseline magnetic field. For the calculation of the magnet's magnetic moment, it is equivalent to five coaxial sub-magnets distributed radially. The PSO algorithm is first used to explore the global solution space of the magnetic moment parameters to generate an initial optimized population. On this basis, the DE algorithm is used to perform mutation and crossover operations on the population to achieve local fine optimization, and iteratively outputs the magnetic moment parameters of the current sub-magnet. These magnetic moment parameters and the magnet's center of mass pose are input into the pre-constructed magnetic field simulation model in COMSOL. The magnetic field distribution is solved by the finite element method, and the simulated magnetic field data at the location of the magnetic sensor array is output. The computer compares the simulated magnetic field data with the net magnetic field data to determine whether the iteration has converged. If the convergence condition is not met, the PSO-DE algorithm continues to iteratively optimize the magnetic moment parameters. After the convergence condition is met, the magnetic moment parameters of the five sub-magnets are vector superimposed to finally output the magnetic moment of the magnet.

[0049] Compared to traditional technologies, this application improves upon traditional methods in terms of ease of operation, measurement accuracy, robustness, and universality. Ease of operation is achieved through hardware collaboration and automated algorithm design, enabling a fully adaptive operation from magnetic field acquisition and pose calculation to magnetic moment output, completing the measurement loop without manual intervention. Measurement accuracy benefits from the dual support of a magnet segmentation solution strategy and magnetic field forward modeling technology. The magnet segmentation solution strategy treats the magnet as multiple coaxial sub-magnets, calculating them separately and then superimposing the vectors, which more closely reflects the actual magnetization distribution. Simultaneously, the magnetic field forward modeling technology directly inputs the magnetization intensity into the finite element simulation software COMSOL, avoiding errors introduced by the simplification assumptions of traditional mathematical models. Robustness stems from the PSO-DE embedded fusion algorithm. The core of PSO-DE fusion is to complement each other's weaknesses through a two-stage search strategy and a perturbation enhancement mechanism. It utilizes the rapid directional search capability of particle swarm optimization, combined with the random differential perturbation mechanism of differential evolution, to jointly reduce parameter sensitivity and improve global convergence accuracy. This approach avoids premature convergence while compensating for slow convergence speed, achieving a balance between global and local search. Its universality stems from the fact that magnetic moment calculation only requires the magnet's geometric structure information, eliminating the need for prior material property knowledge. This allows it to adapt to magnetic units with different radial structures, expanding the system's application range.

[0050] Based on the same inventive concept, embodiments of this application also provide a magnetic moment measurement method using the magnetic moment measurement system described above. For example... Figure 3 As shown, the magnetic moment measurement method includes the following steps 301 to 303.

[0051] Step 301: Collect magnetic field data of the measurement area using a magnetic sensor array.

[0052] First, in the absence of magnets, magnetic field data of the space environment is collected by a magnetic sensor array. The microcontroller then selects 16 magnetic sensors in a time-division multiplexing manner through a data selector. After each data stream is filtered by 30 averages, it is uploaded to the data processing device via a serial port protocol. The data processing device stores this data as the reference value of the environmental magnetic field.

[0053] Step 302: The robotic arm carries the magnet into the measurement area and transmits the real-time pose information of the robotic arm end effector to the data processing device.

[0054] After the robotic arm carries the magnet into the measurement area, the magnetic field data collected by the magnetic sensor array is the magnetic field data containing the magnet. After being filtered by the microcontroller, it is uploaded to the data processing device.

[0055] Step 303: Based on the magnetic field data and the real-time pose information of the robotic arm's end effector, the magnetic moment of the magnet is determined using a particle swarm optimization algorithm and a differential evolution algorithm via a data processing device. The data processing device is a computer.

[0056] In a specific application example, step 303 includes steps 31 to 35.

[0057] Step 31: Determine the net magnetic field data based on the magnetic field data containing the magnet and the magnetic field data without the magnet. Specifically, subtract the magnetic field data without the magnet from the magnetic field data containing the magnet to obtain the net magnetic field data generated solely by the magnet.

[0058] Step 32: Convert the real-time pose information of the robotic arm end effector into the real-time pose information of the magnet. Specifically, based on the coordinate transformation algorithm, the pose of the magnet's center of mass is derived from the pose of the center of the flange end effector.

[0059] Step 33: The magnetic moment parameters are iteratively optimized using the particle swarm optimization algorithm, and the differential evolution algorithm is used to mutate and crossover the particles during the optimization process to obtain the magnetic moment parameters.

[0060] Specifically, first, the algorithm parameters are set.

[0061] Step 34: Based on the magnetic moment parameters and the real-time pose information of the magnet, a pre-constructed magnetic field simulation model is used to perform magnetic field simulation to obtain simulated magnetic field data.

[0062] Step 35: Based on the simulated magnetic field data and the net magnetic field data, determine whether the convergence condition is met. If yes, determine the magnetic moment of the magnet based on the magnetic moment parameters. If no, iteratively optimize the magnetic moment parameters again using the ion swarm optimization algorithm.

[0063] In summary, this application integrates robotic arm pose feedback, magnetic sensor array detection, COMSOL magnetic field simulation, and closed-loop calculation using swarm intelligence algorithms. It can achieve high-precision measurement without relying on the pre-defined parameters of the magnet, meeting the requirements for accurate magnetic moment measurement in real-time and without prior information.

[0064] 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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0065] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0067] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A magnetic moment measurement system, characterized in that, The magnetic moment measurement system includes: a magnetic sensor array, a robotic arm, and a data processing device; The magnetic sensor array is used to collect magnetic field data of the measurement area; The robotic arm is used to carry the magnet into the measurement area and transmit the real-time pose information of the robotic arm end effector to the data processing device. The data processing device is used to determine the magnetic moment of the magnet based on the magnetic field data and the real-time pose information of the robotic arm end effector, using a particle swarm optimization algorithm and a differential evolution algorithm.

2. The magnetic moment measurement system according to claim 1, characterized in that, The magnetic moment measurement system also includes a non-magnetic support platform; the magnetic sensor array is fixed on the surface of the non-magnetic support platform.

3. The magnetic moment measurement system according to claim 1, characterized in that, The magnetic moment measurement system also includes a microcontroller; the microcontroller is used to filter the magnetic field data and then transmit it to the data processing device.

4. The magnetic moment measurement system according to claim 1, characterized in that, The magnetic field data includes magnetic field data without magnets and magnetic field data with magnets.

5. The magnetic moment measurement system according to claim 4, characterized in that, The data processing device includes: The net magnetic field acquisition module is used to determine the net magnetic field data based on the magnetic field data containing magnets and the magnetic field data without magnets. The coordinate transformation module is used to convert the real-time pose information of the robotic arm end effector into the real-time pose information of the magnet. The population optimization module is used to iteratively optimize the magnetic moment parameters using the particle swarm optimization algorithm, and to perform mutation and crossover operations on the particles during the optimization process to obtain the magnetic moment parameters. The simulation module is used to perform magnetic field simulation using a pre-built magnetic field simulation model based on the magnetic moment parameters and the real-time pose information of the magnet, and to obtain simulated magnetic field data. The magnetic moment determination module is used to determine whether the convergence condition is met based on the simulated magnetic field data and the net magnetic field data. If it is, the magnetic moment of the magnet is determined based on the magnetic moment parameters. If not, the magnetic moment parameters are iteratively optimized again using the ion swarm optimization algorithm.

6. The magnetic moment measurement system according to claim 5, characterized in that, When the differential evolution algorithm performs a crossover operation on particles, the crossover probability of the particles changes according to the number of iterations.

7. The magnetic moment measurement system according to claim 5, characterized in that, The magnetic field simulation model includes the spatial coordinates of the magnetic sensor array, the geometric structure of multiple coaxial sub-magnets that divide the magnet radially, and the magnetic-space coupling boundary conditions.

8. The magnetic moment measurement system according to claim 5, characterized in that, The magnetic moment parameters include the magnetic moment parameters of multiple sub-magnets; the magnetic moment determination module performs vector superposition of the magnetic moment parameters of multiple sub-magnets to obtain the magnetic moment of the magnet.

9. The magnetic moment measurement system according to claim 1, characterized in that, The data processing device is a computer.

10. A method for measuring magnetic moment, using the magnetic moment measurement system according to any one of claims 1-9, characterized in that, The magnetic moment measurement method includes: Magnetic field data of the measurement area is acquired using a magnetic sensor array; A robotic arm carries a magnet into the measurement area and transmits the real-time pose information of the robotic arm's end effector to the data processing device. The magnetic moment of the magnet is determined by a data processing device based on the magnetic field data and the real-time pose information of the robotic arm end effector, using a particle swarm optimization algorithm and a differential evolution algorithm.

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