Intelligent device Internet of Things management system and method

Abstract

The invention relates to an intelligent device Internet of Things management system and method, and relates to the technical field of Internet of Things, and the method comprises the steps: setting the size of a particle swarm, and the initial position and speed of particles, the size of the particle swarm being the number of the particles; defining a fitness function for evaluating the quality of each group of working parameters; intelligent equipment is connected to a management platform through the Internet of Things, and real-time operation data is obtained; processing the real-time operation data, and extracting the operation state and related indexes of the equipment; and calculating the fitness value of each particle according to the fitness function, updating the speed and position of each particle until a preset number of iterations is reached to obtain a final working parameter combination, and adjusting the working parameters of the intelligent equipment according to the final working parameter combination. According to the method, the efficiency and the real-time performance of data processing are improved, and the flexibility and the self-adaptability of the management method are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a smart device Internet of Things management system and method. Background Art

[0002] Existing intelligent device management methods usually rely on traditional centralized control systems, which often encounter problems such as low data processing efficiency and lack of real-time performance when processing data from a large number of devices. In particular, when faced with massive amounts of data, the computing power and response speed of traditional methods are difficult to meet the needs, resulting in delays in the optimization and adjustment of device parameters and failure to adapt to changes in device status in a timely manner.

[0003] In addition, existing management methods have some obvious defects in data processing. First, the data processing capacity is limited and it is difficult to cope with the data flood generated by large-scale equipment. When there are a large number of devices and data exchange is frequent, traditional methods may not be able to process this data in real time, resulting in limited monitoring of equipment status and optimization of parameters. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an intelligent device Internet of Things management system and method, which not only improves the efficiency and real-time performance of data processing, but also enhances the flexibility and adaptability of the management method.

[0005] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for managing the Internet of Things of smart devices includes:

[0007] A particle swarm is created for each smart device on the management platform, and each particle represents a set of solutions for the device's operating parameters;

[0008] Set the size of the particle group, the initial position and speed of the particles. The size of the particle group is the number of particles.

[0009] Define the fitness function to evaluate the quality of each set of working parameters;

[0010] Connect smart devices to the management platform through the Internet of Things to obtain real-time operation data;

[0011] Process the real-time operation data to extract the operation status and related indicators of the equipment;

[0012] The fitness value of each particle is calculated according to the fitness function, and the speed and position of each particle are updated until a preset number of iterations is reached to obtain a final working parameter combination, so as to adjust the working parameters of the smart device according to the final working parameter combination.

[0013] Furthermore, real-time operation data includes the working status, energy consumption and efficiency of the equipment.

[0014] Furthermore, the real-time operation data is processed to extract the operation status and related indicators of the equipment, including:

[0015] Preprocessing the real-time operation data to obtain preprocessed data;

[0016] extracting key features from the preprocessed data, including temperature, pressure, and rotation speed;

[0017] According to the key features, identifying the current operating status of the device;

[0018] Calculate key performance indicators based on the equipment's operating status and historical data.

[0019] Further, according to the key features, identifying the current operating state of the device includes:

[0020] Determine the number of clusters K, and randomly determine K points as the initial cluster centers;

[0021] Calculate the distance from each data point to each cluster center and assign it to the corresponding cluster center;

[0022] Recalculate the mean of all data points in each cluster and repeat the updating step until the preset number of iterations is reached to obtain the clustering result;

[0023] Calculate the silhouette coefficient of the clustering results, and assign a device operating status label to each cluster based on the clustering results;

[0024] For newly collected real-time data, the distance between it and each cluster center is calculated and assigned to the corresponding cluster to determine the current operating status of the equipment.

[0025] In a second aspect, a smart device Internet of Things management system includes:

[0026] The acquisition module is used to create a particle swarm for each smart device on the management platform, where each particle represents a set of solutions for the device's operating parameters; set the size of the particle swarm, the initial position and speed of the particles, and the size of the particle swarm is the number of particles; define a fitness function for evaluating the quality of each set of operating parameters; connect the smart device to the management platform through the Internet of Things to obtain real-time operating data;

[0027] The processing module is used to process the real-time operation data and extract the operation status and related indicators of the equipment; calculate the fitness value of each particle according to the fitness function, update the speed and position of each particle until the preset number of iterations is reached to obtain the final working parameter combination, so as to adjust the working parameters of the smart device according to the final working parameter combination.

[0028] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0029] By creating a particle swarm for each smart device and based on the particle swarm optimization algorithm, the present invention can achieve efficient management of the working parameters of a large number of smart devices. This method avoids the tedious process of adjusting parameters one by one in the traditional method, and greatly improves the management efficiency. By defining a fitness function to evaluate the pros and cons of each set of working parameters, the present invention can automatically find the optimal combination of working parameters, thereby optimizing the performance of smart devices. This optimization is based on the real-time operating data of the device, so it is more accurate and effective. The present invention obtains the operating data of smart devices in real time through the Internet of Things, and can dynamically adjust the working parameters of the device. This real-time and dynamic nature makes the management method more flexible and can respond to changes in the status of the device in a timely manner.

[0030] By processing and analyzing real-time operation data, the present invention can timely detect abnormal conditions of equipment and prevent or mitigate potential problems by adjusting operating parameters, thereby improving the stability and reliability of the entire system. The automated parameter optimization process reduces the need for manual intervention, thereby reducing the maintenance cost of the equipment. In addition, preventive parameter adjustment can also reduce the occurrence of equipment failures, further saving the cost of repairing and replacing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Some specific embodiments of the present application will be described in detail in an illustrative and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

[0032] Figure 1 It is a flow chart of the smart device Internet of Things management method of the present invention.

[0033] Figure 2 It is a schematic diagram of the smart device Internet of Things management system of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0035] The following embodiments of the present application take the smart device Internet of Things management system and method as an example to explain the solution of the present application in detail, but this embodiment cannot limit the scope of protection of the present application.

[0036] like Figure 1 As shown, the present invention provides a method for managing the Internet of Things of smart devices, the method comprising:

[0037] Step 11, create a particle swarm for each smart device on the management platform, where each particle represents a set of solutions for the device's operating parameters;

[0038] Step 12, setting the size of the particle group, the initial position and speed of the particles, the size of the particle group is the number of particles;

[0039] Step 13, defining a fitness function for evaluating the quality of each set of working parameters;

[0040] Step 14, connecting the smart device to the management platform through the Internet of Things to obtain real-time operation data, which includes the working status, energy consumption and efficiency of the device;

[0041] Step 15, processing the real-time operation data to extract the operation status and related indicators of the equipment;

[0042] Step 16, calculating the fitness value of each particle according to the fitness function, updating the speed and position of each particle until a preset number of iterations is reached to obtain a final working parameter combination, and adjusting the working parameters of the smart device according to the final working parameter combination.

[0043] In an embodiment of the present invention, step 11, by creating a particle swarm for each smart device, the method can perform refined management according to the characteristics of each device. The application of the particle swarm optimization algorithm enables efficient exploration of the search space of the working parameters, which helps to find a better combination of parameters. Step 12, reasonably setting the size and initial state of the particle swarm can ensure the comprehensiveness and efficiency of the search process. By adjusting the number of particles and the initial position and speed, the breadth and depth of the search can be better balanced, and the probability of finding the optimal solution can be increased. Step 13, the definition of the fitness function is the key to the optimization process, which directly affects the direction and results of parameter optimization. A reasonable fitness function can accurately reflect the performance of the device, thereby guiding the particle swarm to converge to a better combination of parameters. Step 14, real-time operation data is obtained through the Internet of Things connection, so that the management method can be adjusted based on the latest device status. This real-time performance ensures the timeliness and effectiveness of parameter optimization, especially when the device status changes, it can respond quickly. Step 15, data processing and indicator extraction are key steps to ensure the accuracy of management decisions. Through in-depth analysis of real-time operation data, the current status of the device can be more accurately understood, providing a reliable basis for subsequent parameter optimization. Step 16, by iteratively calculating the fitness value of the particle and updating its speed and position, this method can gradually approach the optimal working parameter combination. This iterative optimization method not only improves the accuracy of parameter adjustment, but also ensures the robustness of the optimization process. The final working parameter combination can significantly improve the performance and efficiency of the device.

[0044] In another preferred embodiment of the present invention, the above step 11 may include:

[0045] The management platform identifies and lists all connected smart devices, and for each device, creates a particle swarm. Each particle swarm contains multiple particles, each particle represents a possible combination of working parameters; in the database of the management platform, storage space is allocated for each device to save its corresponding particle swarm data and related information. The above step 12 may include: This step involves setting basic properties for the particle swarm, including the number of particles (i.e., the size of the particle swarm), the initial position and velocity of each particle; based on the complexity of the problem and computing resources, the number of particles in each particle swarm is determined, and an initial position is assigned to each particle randomly or according to a certain strategy. In this scenario, the position represents a combination of working parameters, and similarly, an initial velocity is assigned to each particle. In PSO, velocity determines the direction and step size of the particle's movement in the search space. The above step 13 may include:

[0046] Determine the evaluation indicators, such as the equipment's operating efficiency, energy consumption, stability, etc. Based on the above indicators, build a mathematical function to quantify the performance of each set of working parameters. This function should be able to return a value that represents the fitness of the set of parameters.

[0047] The above step 14 may include:

[0048] Connect smart devices to the management platform and collect the device's operating data in real time. Ensure that each smart device can be stably connected to the management platform through the Internet or other communication methods; establish a data transmission channel to ensure that the device can send operating data to the management platform in real time; on the management platform side, set up a data receiving mechanism to store the received real-time operating data in the database; verify and clean the received data to ensure the accuracy and completeness of the data.

[0049] In a preferred embodiment of the present invention, the specific calculation formula of the fitness function is:

[0050]

[0051] Where F is the value of the fitness function; w1 is the weight coefficient of efficiency; w2 is the weight coefficient of energy consumption; w3 is the weight coefficient of stability; TC is the number of tasks actually completed; MTP is the maximum number of tasks that can be completed; PU is power usage; OT is the running time; PU max Indicates the maximum available power usage of the device; FL indicates the number of faults.

[0052] In an embodiment of the present invention, the function can comprehensively evaluate the working performance of the intelligent device, not only taking into account the efficiency of completing tasks (reflected by the ratio of the number of tasks actually completed to the maximum number of tasks that can be completed), but also taking into account the energy consumption (represented by the product of the power usage of the device and the running time and the ratio of the maximum available power of the device) and the stability of the device (calculated by 1 minus the ratio of the number of failures to the running time). By adjusting the weight coefficient, different performance indicators can be flexibly emphasized according to actual needs. The fitness function is designed to guide the particle swarm optimization algorithm to find the corresponding working parameter combination. By maximizing the value of the fitness function, the intelligent device can achieve a comprehensive optimal state in terms of efficiency, energy consumption and stability. Since the function depends on real-time operating data (such as the number of tasks actually completed, power usage, running time and number of failures, etc.), it is suitable for real-time monitoring and dynamic adjustment of the working parameters of the intelligent device. This helps the device to maintain corresponding performance under different working environments. By considering the impact of the number of failures on fitness, the function can reflect the reliability of the device to a certain extent. When the number of failures increases, the fitness value will decrease, thereby prompting the manager to perform necessary maintenance and repairs to reduce possible future failures.

[0053] In a preferred embodiment of the present invention, the above step 15, processing the real-time operation data to extract the operation status and related indicators of the equipment, may include:

[0054] Step 151, preprocessing the real-time operation data to obtain preprocessed data;

[0055] Step 152, extracting key features from the preprocessed data, including temperature, pressure and rotation speed;

[0056] Step 153, identifying the current operating state of the device according to the key features;

[0057] Step 154, calculating key performance indicators based on the operating status and historical data of the equipment.

[0058] In the embodiment of the present invention, step 151 removes or corrects abnormal values, duplicate values ​​or missing values ​​in real-time operation data to ensure the accuracy and integrity of the data; converts the data into a unified standard or range; uses filtering technology to reduce noise in the data and improve the signal-to-noise ratio of the data. Step 152, based on the equipment characteristics and business requirements, selects the features that have the greatest impact on equipment performance, such as temperature, pressure and speed, and uses signal processing technology to extract the selected key features from the preprocessed data, specifically including:

[0059] For slowly changing signals such as temperature and pressure, features such as mean, standard deviation, peak, valley, etc. can be extracted directly in the time domain; for periodic or quasi-periodic signals (such as speed signals or vibration signals), they can be converted to the frequency domain through Fourier transform (FFT) to analyze the signal's spectral characteristics, such as main frequency, amplitude, etc.; for non-stationary signals, methods such as wavelet transform and short-time Fourier transform (STFT) can be used to perform analysis in both the time domain and the frequency domain to extract features such as energy and entropy in specific frequency bands. Calculate the mean, median, standard deviation and other statistical quantities of the signal to reflect the average level and dispersion of the signal; identify the peaks and valleys in the signal, as well as their frequency and amplitude, and calculate the waveform factor, peak factor, pulse factor, etc. of the signal to describe the waveform characteristics of the signal; obtain the signal's spectrum through FFT, identify the main frequency component and its corresponding amplitude, calculate the center of mass, bandwidth and other parameters of the spectrum to reflect the distribution characteristics of the signal in the frequency domain; use wavelet transform to analyze the energy distribution of the signal at different times and frequencies, extract the energy in a specific frequency band as a feature, or calculate the signal's energy entropy, wavelet entropy, etc. to reflect the complexity and uncertainty of the signal.

[0060] Step 153: Based on the extracted key features, identify the current operating status of the equipment, such as normal, abnormal or warning status; display the identified operating status in real time on the management platform for management personnel to refer to and make decisions. Step 154: Integrate the real-time operating status data of the equipment with the historical data, and calculate key performance indicators (KPIs) such as equipment efficiency, failure rate, mean trouble-free operation time, etc. based on the integrated data; analyze the calculated performance indicators to evaluate the overall performance and health of the equipment.

[0061] In a preferred embodiment of the present invention, the above step 153, identifying the current operating state of the device according to the key feature, includes:

[0062] Step 1531, determine the number of clusters K, and randomly determine K points as initial cluster centers;

[0063] Step 1532, calculating the distance between each data point and each cluster center, and assigning it to the corresponding cluster center;

[0064] Step 1533, recalculate the mean of all data points in each cluster, and repeat the updating step until a preset number of iterations is reached to obtain a clustering result;

[0065] Step 1534, calculating the silhouette coefficient of the clustering result, and assigning a device operation status label to each cluster according to the clustering result;

[0066] Step 1535, for the newly collected real-time data, calculate its distance from each cluster center and assign it to the corresponding cluster to determine the current operating status of the device.

[0067] In the embodiment of the present invention, step 1531, determine the number of clusters K, this value represents the number of types of equipment operating states that are expected to be identified, and randomly select K points from the preprocessed data set as the initial cluster centers. Step 1532, for each point in the data set, calculate its distance from each cluster center (such as Euclidean distance), and assign each point to the cluster represented by the cluster center closest to it. Step 1533, for each cluster, calculate the mean of all data points within it as the new cluster center; repeat steps 1532 and 1533 until the preset number of iterations is reached, at which time the clustering result is considered stable; step 1534, calculate the silhouette coefficient of the clustering result to evaluate the quality of the clustering. The closer the silhouette coefficient value is to 1, the better the clustering effect; based on the clustering results, assign a label representing a specific operating state of the device to each cluster (such as "normal", "abnormal", etc.). Step 1535, for the newly collected real-time data point, calculate its distance from each cluster center and assign it to the nearest cluster, and determine the current operating status of the device based on the cluster label of the data point.

[0068] In the embodiment of the present invention, clustering can automatically discover the inherent structure and grouping in the data, and adapt to the diversity and changes in the operating status of the equipment; for newly collected real-time data, it can be quickly assigned to the existing clusters to achieve real-time monitoring of the operating status of the equipment; by assigning a clear operating status label to each cluster, the interpretability and practicality of the results are improved; the number of clusters K can be adjusted according to actual needs to adapt to different application scenarios and needs; by calculating the silhouette coefficient, the effect of clustering can be quantitatively evaluated.

[0069] In a preferred embodiment of the present invention, in updating the speed and position of each particle, the speed update formula is:

[0070] v I (t+1)=(ω+ω decay ·t)·v i (t)+c1·r1·(pbest i -x i (t))+c2·r2·

[0071] (gbest-x i (t))+c3·r3·(nbest i -x i (t))+∈;

[0072] Among them, v i (t+1) represents the velocity of particle i at time t+1; ω is the initial inertia weight, ω decay is the decay rate of the inertia weight, t is the current number of iterations; v i (t) represents the velocity of particle i at time t; c1 represents the individual learning factor; r1 represents a random number in the range [0, 1]; pbest i represents the historical position of particle i; x i (t) represents the position of particle i at time t; c2 represents the social learning factor; r2 represents a random number in the range [0, 1]; gbest represents the global corresponding position of the entire particle swarm; c3 represents the neighborhood learning factor; r3 represents a random number in the range [0, 1]; nbest i represents the corresponding position in the neighborhood of particle i; ∈ represents the random perturbation term.

[0073] In the embodiment of the present invention, by introducing the individual learning factor c1, the social learning factor c2 and the neighborhood learning factor c3, the formula enables the particle to simultaneously consider the individual historical best position, the global best position and the neighborhood best position when updating the speed. This helps to maintain the diversity of the particle swarm, thereby improving the algorithm's ability to find the global optimal solution in a complex search space. By introducing the inertia weight ω and its decay rate ω decay , which enables the particle to adaptively adjust the inertial part of the velocity during the search process. As the iterations proceed, the inertia weight gradually decreases, which helps the algorithm to conduct a broad search in the early stage and a more refined local search in the later stage.

[0074] In a preferred embodiment of the present invention, in updating the velocity and position of each particle, the updating formula of the position is:

[0075] x i (t+1)=x i (t)+(α+α decay ·t)·v i (t+1)+β·(gbest-x i (t));

[0076] Among them, x i (t+1) represents the position of particle i at time t+1; α represents the initial step length, α decay represents the step size decay rate, t represents the current iteration number, and β represents the global guidance factor.

[0077] In the embodiment of the present invention, by introducing the initial step length α and the step length attenuation rate α decay , this formula enables particles to adaptively adjust the step size during the search process. As the iteration proceeds, the step size gradually decreases, which helps the algorithm to conduct extensive searches in the early stages and more refined local searches in the later stages. By introducing the global guidance factor β and the global best position gbest, this formula enables particles to consider the information of the global optimal solution when updating their positions. This helps to accelerate the algorithm's convergence to the global optimal solution and improve search efficiency. This formula combines the particle's velocity v i (t+1) and position x i (t) to update the new position of the particle. The speed part reflects the movement trend of the particle, while the position part takes into account the guidance of the global optimal solution. This dynamic balance helps the algorithm maintain a balance between diversity and convergence during the search process. By adjusting the initial step size α, the step size decay rate α decay The value of and the global guidance factor β can flexibly control the search behavior of particles and the convergence speed of the algorithm. This makes the algorithm adaptable to different types of optimization problems and different search space characteristics.

[0078] like Figure 2As shown, a smart device Internet of Things management system 20 includes:

[0079] The acquisition module 21 is used to create a particle swarm for each smart device on the management platform, where each particle represents a set of solutions for the device's working parameters; set the size of the particle swarm, the initial position and speed of the particles, and the size of the particle swarm is the number of particles; define a fitness function for evaluating the quality of each set of working parameters; connect the smart device to the management platform through the Internet of Things to obtain real-time operation data;

[0080] The processing module 22 is used to process the real-time operation data, extract the operation status and related indicators of the device; calculate the fitness value of each particle according to the fitness function, update the speed and position of each particle until a preset number of iterations is reached to obtain the final working parameter combination, so as to adjust the working parameters of the smart device according to the final working parameter combination.

[0081] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0082] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0083] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing the Internet of Things of smart devices, characterized in that: include: A particle swarm is created for each smart device on the management platform, and each particle represents a set of solutions for the device's operating parameters; Set the size of the particle group, the initial position and speed of the particles. The size of the particle group is the number of particles. Define the fitness function to evaluate the quality of each set of working parameters; Connect smart devices to the management platform through the Internet of Things to obtain real-time operation data; Process the real-time operation data to extract the operation status and related indicators of the equipment; The fitness value of each particle is calculated according to the fitness function, and the speed and position of each particle are updated until a preset number of iterations is reached to obtain a final working parameter combination, so as to adjust the working parameters of the smart device according to the final working parameter combination.

2. The method for managing the Internet of Things of smart devices according to claim 1, characterized in that: Real-time operating data includes equipment operating status, energy consumption and efficiency.

3. The method for managing the Internet of Things of smart devices according to claim 2, characterized in that: Process the real-time operation data to extract the operation status and related indicators of the equipment, including: Preprocessing the real-time operation data to obtain preprocessed data; extracting key features from the preprocessed data, including temperature, pressure, and rotation speed; According to the key features, identifying the current operating status of the device; Calculate key performance indicators based on the equipment's operating status and historical data.

4. The method for managing the Internet of Things of smart devices according to claim 3, characterized in that: Based on the key features, the current operating status of the device is identified, including: Determine the number of clusters K, and randomly determine K points as the initial cluster centers; Calculate the distance from each data point to each cluster center and assign it to the corresponding cluster center; Recalculate the mean of all data points in each cluster and repeat the updating step until the preset number of iterations is reached to obtain the clustering result; Calculate the silhouette coefficient of the clustering results, and assign a device operating status label to each cluster based on the clustering results; For newly collected real-time data, the distance between it and each cluster center is calculated and assigned to the corresponding cluster to determine the current operating status of the equipment.

5. A smart device Internet of Things management system, the system implements the method according to claim 1, characterized in that: include: The acquisition module is used to create a particle swarm for each smart device on the management platform, where each particle represents a set of solutions for the device's operating parameters; set the size of the particle swarm, the initial position and speed of the particles, and the size of the particle swarm is the number of particles; define a fitness function for evaluating the quality of each set of operating parameters; connect the smart device to the management platform through the Internet of Things to obtain real-time operating data; The processing module is used to process the real-time operation data and extract the operation status and related indicators of the equipment; calculate the fitness value of each particle according to the fitness function, update the speed and position of each particle until the preset number of iterations is reached to obtain the final working parameter combination, so as to adjust the working parameters of the smart device according to the final working parameter combination.

6. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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