Data acquisition method and device

Abstract

This application discloses a data acquisition method and apparatus, belonging to the field of communication technology. The data acquisition method, executed by a first device, includes: acquiring input information and / or output information from an artificial intelligence network on the first device side; and sending first information and / or second information to a second device, wherein the first information and / or the second information satisfies a first requirement, the first information being selected from the input information, and the second information being selected from the output information. The technical solution of this application embodiment can improve the performance of a communication system.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a data acquisition method and apparatus. Background Technology

[0002] In existing technologies, communication equipment often only considers how to collect data under ideal interference conditions, without considering how to collect data under non-ideal interference conditions. Summary of the Invention

[0003] This application provides a data acquisition method and apparatus that can improve the performance of a communication system.

[0004] In a first aspect, embodiments of this application provide a data acquisition method, executed by a first device, the method comprising:

[0005] Collect input and / or output information from the artificial intelligence network on the first device side;

[0006] Send first information and / or second information to a second device, wherein the first information and / or second information satisfy a first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0007] Secondly, embodiments of this application provide a data acquisition device applied to a first device, the device comprising:

[0008] The acquisition module is used to acquire input and / or output information of the artificial intelligence network on the first device side;

[0009] The reporting module is used to send first information and / or second information to a second device, wherein the first information and / or second information meet a first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0010] Thirdly, a first device is provided, the terminal including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0011] Fourthly, a first device is provided, including a processor and a communication interface, wherein the processor is used to collect input information and / or output information of an artificial intelligence network on the first device side; the communication interface is used to send first information and / or second information to a second device, wherein the first information and / or the second information satisfies a first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0012] Fifthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0013] In a sixth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0014] In a seventh aspect, a computer program / program product is provided, the computer program / program product being stored in a non-volatile storage medium, the program / program product being executed by at least one processor to implement the steps of the method as described in the first aspect.

[0015] In this embodiment, the first device collects input and / or output information from the artificial intelligence network; it sends the first and / or second information to the second device. The first and / or second information meets the first requirement. Thus, the first and / or second information reported to the second device meets the first requirement, which can effectively improve the efficiency and accuracy of data collection on the first device side, improve the performance of the artificial intelligence module in the wireless network, and thereby improve the performance of the communication system. Attached Figure Description

[0016] Figure 1 A schematic diagram showing a wireless communication system;

[0017] Figure 2 A schematic diagram showing the structure of a neural network;

[0018] Figure 3 A schematic diagram showing the structure of a neuron;

[0019] Figure 4 This is a flowchart illustrating a data acquisition method performed by a first device according to an embodiment of this application.

[0020] Figure 5 This is a schematic diagram showing the structure of the data acquisition device applied to the first device according to an embodiment of this application;

[0021] Figure 6 This is a schematic diagram of the structure of the communication device according to an embodiment of this application;

[0022] Figure 7 This is a schematic diagram illustrating the composition of a terminal according to an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to applications other than NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0026] Figure 1This diagram illustrates a block diagram of a wireless communication system applicable to embodiments of this application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can also be referred to as a terminal device or user equipment (UE). The terminal 11 can be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), wearable device, vehicle-mounted device (VUE), pedestrian terminal (PUE), etc. Wearable devices include smartwatches, wristbands, headphones, glasses, etc. It should be noted that this application does not limit the specific type of terminal 11. Network-side equipment 12 can be a base station or a core network. The base station can be referred to as a node B, evolved node B, access point, base transceiver station (BTS), radio base station, radio transceiver, basic service set (BSS), extended service set (ESS), B node, evolved B node (eNB), home B node, home evolved B node, WLAN access point, WiFi node, transmitting and receiving point (TRP), or any other suitable term in the field, as long as the same technical effect is achieved. The base station is not limited to specific technical terms. It should be noted that in this application embodiment, only the base station in the NR system is used as an example, but the specific type of base station is not limited. The core network equipment can be a location management device, such as a location management function (LMF, E-SLMC), etc.

[0027] Artificial intelligence (AI) has been widely applied in various fields. AI can be implemented in various ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. Figure 2 The diagram shown illustrates the architecture of a neural network, which is composed of neurons. A schematic diagram of a neuron is shown below. Figure 3As shown, a1, a2, ..., aK are the inputs, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, tanh, ReLU (Rectified Linear Unit), etc.

[0028] The parameters of a neural network are optimized using optimization algorithms. An optimization algorithm is an algorithm that minimizes or maximizes an objective function (also called a loss function), which is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f(.) is constructed. Using this model, the predicted output f(x) can be obtained from the input x, and the difference between the predicted value and the true value (f(x) - Y) can be calculated. This difference is the loss function. The goal of the optimization algorithm is to find suitable values ​​W and b that minimize the value of the loss function. The smaller the loss value, the closer the model is to the reality.

[0029] Most common optimization algorithms are based on the error back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two parts: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample is introduced from the input layer, processed layer by layer by the hidden layers, and then propagated to the output layer. If the actual output of the output layer does not match the expected output, the process transitions to the error back propagation stage. Error back propagation involves propagating the output error back to the input layer layer by layer through the hidden layers, distributing the error to all units in each layer, thus obtaining the error signal of each unit. This error signal serves as the basis for adjusting the weights of each unit. This process of adjusting the weights through forward and backward propagation is repeated continuously. This continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the predetermined number of learning iterations is reached.

[0030] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum descent, Nesterov (named after the inventor, specifically stochastic gradient descent with momentum), Adagrad (Adaptive Gradient Descent), Adadelta, RMSprop (root mean square prop), and Adam (Adaptive Moment Estimation).

[0031] During error backpropagation, these optimization algorithms calculate the gradient by taking the derivative or partial derivative of the error or loss obtained from the loss function with respect to the current neuron, and then adding the influence of the learning rate, previous gradients or derivatives or partial derivatives, etc., and then pass the gradient to the previous layer.

[0032] This application provides a data acquisition method, such as... Figure 4 As shown, the method includes:

[0033] Step 101: Collect the input and / or output information of the artificial intelligence network on the first device side;

[0034] Step 102: Send the first information and / or the second information to the second device, wherein the first information and / or the second information satisfy the first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0035] In this embodiment, the first device collects input and / or output information from the artificial intelligence network; it sends the first and / or second information to the second device. The first and / or second information meets the first requirement. Thus, the first and / or second information reported to the second device meets the first requirement, which can effectively improve the efficiency and accuracy of data collection on the first device side, improve the performance of the artificial intelligence module in the wireless network, and thereby improve the performance of the communication system.

[0036] Optionally, if the input information meets the first requirement, the first information can be directly determined from the input information; if the input information does not meet the first requirement, the input information needs to be processed to obtain the first information that meets the first requirement. If the output information meets the first requirement, the second information can be directly determined from the output information; if the output information does not meet the first requirement, the output information needs to be processed to obtain the second information that meets the first requirement.

[0037] Optionally, the first device and the second device can be the same device or different devices. When the first device and the second device are the same device, the sender and receiver of the first information and / or the second information are different modules of that device. The first device can be a terminal, and the second device can be a terminal or a network-side device.

[0038] The sender of the first information and / or the second information sends the first information and / or the second information to the second device. The second device can process the second information with a matching AI module, or with a non-AI module, or the second device can use the second information directly without processing it.

[0039] Since data acquisition may be conducted under non-ideal interference, the second device can incorporate non-ideal interference when using the first and / or second information to simulate various non-ideal situations. For example, if the first and / or second information is high signal-to-noise ratio (SNR) data, white noise can be added to simulate a low SNR situation; or, a uniformly varying phase shift can be added to the first and / or second information to simulate timing inaccuracies. Furthermore, GANs (Generative Adversarial Networks) can be used to add non-ideal conditions to the first and / or second information.

[0040] In some embodiments, after the step of collecting input and / or output information of the first device-side artificial intelligence network, the method further includes:

[0041] The system reports the associated parameters and / or statistical characteristics of the first requirement. This allows the second device to know whether the first information and / or the second information meet the first requirement, and to process the first information and / or the second information quickly and accurately. The collected input information and / or output information may or may not meet the first requirement. If the collected input information and / or output information does not meet the first requirement, the input information is processed to obtain first information that meets the first requirement, and / or the output information is processed to obtain second information that meets the first requirement.

[0042] In some embodiments, prior to the step of sending the first information and / or the second information to the second device, the method further includes:

[0043] The statistical range of statistical features sent by the second device is received, wherein the statistical features are the statistical features of the first required correlation parameters.

[0044] In this embodiment, the second device can configure the statistical range of the statistical features and send the statistical range of the statistical features to the first device. This allows the statistical range to be limited according to the required information, thus preventing the first device from acquiring too large a range of statistical features.

[0045] In some embodiments, the statistical characteristics of the correlation parameter of the first requirement include at least one of the following:

[0046] The probability distribution of the associated parameters includes the probability density function (PDF), the cumulative distribution function (CDF), and the probability mass function (PMF), which can specifically be the probability distribution of TA, the probability distribution of signal-to-noise ratio, etc.

[0047] The probability distribution of the errors of the associated parameters, such as the probability distribution of the TA estimation error and the probability distribution of the signal-to-noise ratio estimation error;

[0048] The mean of the correlation parameters, such as the mean of TA and the mean of signal-to-noise ratio;

[0049] The variance of the correlation parameters, such as the variance of TA and the variance of the signal-to-noise ratio;

[0050] The mean of the errors of the associated parameters, such as the mean of the estimation error of TA and the mean of the estimation error of signal-to-noise ratio;

[0051] The variance of the errors of the associated parameters, such as the variance of the estimation error of TA and the variance of the estimation error of the signal-to-noise ratio;

[0052] The temporal correlation of the associated parameters;

[0053] The temporal correlation of the errors of the associated parameters;

[0054] The cumulative distribution function (CDF) of the associated parameter at preset thresholds, such as the CDF value at 5%, CDF value at 10%, CDF value at 50%, CDF value at 90%, and CDF value at 95%.

[0055] The CDF value of the error of the associated parameter at a preset threshold, such as the value at CDF 5%, CDF 10%, CDF 50%, CDF 90%, and CDF 95%.

[0056] The aforementioned preset thresholds can be defined by the protocol, configured by the network-side device, or pre-configured.

[0057] In some embodiments, the associated parameter of the first requirement includes at least one of the following:

[0058] timing;

[0059] Timing estimation error;

[0060] TA is measured in advance at a fixed time;

[0061] Estimation error of timing lead time;

[0062] Signal-to-noise ratio;

[0063] Signal-to-noise ratio estimation error;

[0064] Noise power, such as white noise, phase noise, etc.;

[0065] Interference within the community, such as interference from other users within the community;

[0066] Inter-cell interference, such as interference from base stations and / or users in other cells;

[0067] Nonlinear parameters of signal processing modules, such as the nonlinear parameters of power amplifiers (PA);

[0068] Mobility parameters, the mobility parameters of the first device, include the mobility speed and mobility angle of the first device. Mobility speed includes absolute speed and relative speed, such as the relative speed of the first device to the network-side base station and the relative mobility angle of the first device to the base station.

[0069] The location of the first device includes absolute location and relative location. The absolute location can be the latitude and longitude of the first device, and the relative location can be the relative location of the first device with respect to the base station. Optionally, if the sender of the first information and / or the second information is the positioning service module or positioning module of the first device, then the first requirement needs to include this parameter.

[0070] The estimation error of the position of the first device;

[0071] Inter-carrier interference;

[0072] Beam quality can be layer 1 RSRP (L1-RSRP), L1-SINR, L1-RSRP, L1-RSRQ, layer 3 RSRP (L3-RSRP), L3-SINR, L3-RSRP, L3-RSRQ, etc.;

[0073] Beam quality estimation error.

[0074] In this embodiment, a first requirement is defined by an association parameter, and the first requirement may include at least one of the following:

[0075] At least one of the associated parameters is greater than a preset first threshold;

[0076] At least one of the associated parameters is greater than or equal to a preset second threshold;

[0077] At least one of the associated parameters is less than a preset third threshold;

[0078] At least one of the associated parameters is less than or equal to a preset fourth threshold.

[0079] Among them, the above first threshold, second threshold, third threshold, and fourth threshold can be defined by the protocol, can be configured by the network-side device, or can be pre-configured.

[0080] In a specific example, the first requirement may be that the mean value of the signal-to-noise ratio is greater than or equal to 30 dB, at the same time, the mean value of the inter-cell interference is less than -5 dB, and at the same time, the TA estimation error is within a certain range, for example, threshold 1 < the value at the 90% CDF of the TA estimation error < threshold 2.

[0081] In some embodiments, the step of collecting the input information and / or output information of the artificial intelligence network on the first device side includes:

[0082] Collecting the input information that meets the first requirement; and / or

[0083] Collecting the output information that meets the first requirement.

[0084] In this embodiment, during the collection, the input information and output information that meet the first requirement can be collected, so that the first information and the second information can be directly obtained without processing the input information and output information.

[0085] In some embodiments, before sending the first information and / or the second information to the second device, the method further includes:

[0086] Processing the collected input information through a preset processing method to obtain the first information; and / or

[0087] Processing the collected output information through a preset processing method to obtain the second information.

[0088] In this embodiment, during the collection, the input information and output information that do not meet the first requirement can be collected, and the input information and output information are processed to obtain the first information and the second information.

[0089] [[ID=~]]In some embodiments, the output information meets the first requirement; and / or

[0090] The output information meets the first requirement after being processed by a preset processing method; and / or

[0091] The input information meets the first requirement; and / or

[0092] The input information meets the first requirement after being processed by a preset processing method.

[0093] In some embodiments, the error between the first information and the first reference information is less than a preset threshold; and / or

[0094] The error between the second information and the second reference information is less than a preset threshold.

[0095] For example, the protocol defines at least one example, which includes first reference information and / or second reference information, wherein the first reference information is a label or reference value of the first information, and the second reference information is a label or reference value of the second information. For example, a first device inputs the first reference information into an artificial intelligence network to obtain the second information, where the first requirement is that the error between the second information and the second reference information is less than a preset threshold.

[0096] In some embodiments, the error includes any one or a combination of the following:

[0097] Mean square error;

[0098] Normalized mean square error;

[0099] Cosine similarity;

[0100] Correlation.

[0101] The first and second reference information can be some reference values ​​defined by the protocol. For example, the first and second reference information can be obtained by algorithms that are close to the theoretical optimality. The error between the first information and the first reference signal that meets the first requirement is within a certain threshold, and the error between the second information and the second reference signal that meets the first requirement is within a certain threshold. The error can be calculated by MSE (mean square error), NMSE (normalized mean square error), cosine similarity, correlation, etc., and at least one of them, as a mathematical relationship, such as a*NMSE+b*cosine similarity, that is, the first error NMSE is calculated using normalized mean square error, the second error is calculated using cosine similarity, and a*first error+b*second error is taken as the final error.

[0102] In some embodiments, the artificial intelligence network is located in the Channel State Information (CSI) encoding module of the first device, and for the CSI encoding module, the input information may not meet the first requirement.

[0103] In some embodiments, the artificial intelligence network is located in a joint module of the first device. The joint module includes at least a CSI feedback module. For example, the joint module can be a combination of a CSI feedback module and a CSI reference signal estimation module, in which case the input information and / or the output information satisfies the first requirement; and / or, the input information and / or the output information satisfies the first requirement after being processed by a preset processing method. As another example, the joint module can be a combination of a precoding module, a CSI feedback module, and a CSI reference signal estimation module, in which case the input information and / or the output information satisfies the first requirement; and / or, the input information and / or the output information satisfies the first requirement after being processed by a preset processing method. As yet another example, the joint module can be a combination of a scheduling module, a CSI feedback module, and a CSI reference signal estimation module, in which case the input information and / or the output information satisfies the first requirement; and / or, the input information and / or the output information satisfies the first requirement after being processed by a preset processing method. For example, the combined module can be a combination of a precoding module, a scheduling module, a CSI feedback module, and a CSI reference signal estimation module, in which case the input information and / or the output information satisfies the first requirement; and / or the input information and / or the output information satisfies the first requirement after being processed by a preset processing method.

[0104] In some embodiments, the preset processing method is a protocol definition, or the second device sends it to the first device, or the first device sends it to the second device, or the network-side device configures it, or the first device reports it to the network-side device.

[0105] In some embodiments, the input information and the output information include at least one of the following:

[0106] Demodulate the reference signal DMRS data;

[0107] Channel estimation data;

[0108] Reference signal estimation data;

[0109] The first required associated parameter includes at least one of the following:

[0110] Timing, and / or timing inaccuracy, leads to uniform phase changes;

[0111] Timing estimation error;

[0112] Timing advance;

[0113] Estimation error of timing lead time;

[0114] Signal-to-noise ratio;

[0115] Signal-to-noise ratio estimation error;

[0116] Noise power;

[0117] Inter-carrier interference, and / or Doppler shift due to lack of cyclic prefix (CP) and moving speed;

[0118] Interference within the community;

[0119] Inter-cell interference.

[0120] When collecting data, the input information can be disregarded, and only the second information and / or output information need to be considered to see if they meet the first requirement.

[0121] In some embodiments, the input information and the output information include location data, and the first required association parameter includes at least one of the following:

[0122] Timing, and / or timing inaccuracy, leads to uniform phase changes;

[0123] Timing estimation error;

[0124] Timing advance;

[0125] Estimation error of timing lead time;

[0126] Signal-to-noise ratio;

[0127] Signal-to-noise ratio estimation error;

[0128] Noise power;

[0129] Inter-carrier interference, and / or Doppler shift due to lack of cyclic prefix (CP) and moving speed;

[0130] Interference within the community;

[0131] Inter-cell interference;

[0132] The location of the first device;

[0133] The estimation error of the position of the first device.

[0134] In some embodiments, the input information and the output information include beam data, and the first required correlation parameter includes at least one of the following:

[0135] Timing, and / or timing inaccuracy, leads to uniform phase changes;

[0136] Timing estimation error;

[0137] Timing advance;

[0138] Estimation error of timing lead time;

[0139] Signal-to-noise ratio;

[0140] Signal-to-noise ratio estimation error;

[0141] Noise power;

[0142] Inter-carrier interference, and / or Doppler shift due to lack of cyclic prefix (CP) and moving speed;

[0143] Interference within the community;

[0144] Inter-cell interference;

[0145] Beam quality;

[0146] Beam quality estimation error.

[0147] In some embodiments, the first requirement is a protocol definition, or the second device sends it to the first device, or the second device sends it to the first device, or the network-side device configures it, or the first device reports it to the network-side device.

[0148] It should be noted that the data acquisition method provided in this application embodiment can be executed by a data acquisition device or a module within that data acquisition device for executing the loading data acquisition method. This application embodiment uses the execution of the loading data acquisition method by a data acquisition device as an example to illustrate the data acquisition method provided in this application embodiment.

[0149] This application provides a data acquisition device applied to a first device 300, such as... Figure 5 As shown, the device includes:

[0150] The acquisition module 310 is used to acquire input information and / or output information of the artificial intelligence network on the first device side;

[0151] The reporting module 320 is used to send first information and / or second information to a second device, wherein the first information and / or the second information meets a first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0152] In some embodiments, the reporting module 320 is also used to report the first required correlation parameters and / or the statistical characteristics of the correlation parameters.

[0153] In some embodiments, the apparatus further includes:

[0154] The receiving module is used to receive the statistical range of statistical features sent by the second device, wherein the statistical features are the statistical features of the first required correlation parameters.

[0155] In some embodiments, the statistical characteristics of the correlation parameter of the first requirement include at least one of the following:

[0156] The probability distribution of the associated parameters;

[0157] The probability distribution of the error of the associated parameters;

[0158] The mean of the associated parameters;

[0159] The variance of the associated parameters;

[0160] The mean of the error of the associated parameters;

[0161] The variance of the error of the associated parameters;

[0162] The temporal correlation of the associated parameters;

[0163] The temporal correlation of the errors of the associated parameters;

[0164] The cumulative distribution function (CDF) of the associated parameters at a preset threshold;

[0165] The CDF value of the error of the associated parameter at a preset threshold.

[0166] In some embodiments, the associated parameter of the first requirement includes at least one of the following:

[0167] timing;

[0168] Timing estimation error;

[0169] Timing advance;

[0170] Estimation error of timing lead time;

[0171] Signal-to-noise ratio;

[0172] Signal-to-noise ratio estimation error;

[0173] Noise power;

[0174] Interference within the community;

[0175] Inter-cell interference;

[0176] Nonlinear parameters of the signal processing module;

[0177] Movement parameters;

[0178] The location of the first device;

[0179] The estimation error of the position of the first device;

[0180] Inter-carrier interference;

[0181] Beam quality;

[0182] Beam quality estimation error.

[0183] In some embodiments, the first requirement includes at least one of the following:

[0184] At least one of the associated parameters is greater than a preset first threshold;

[0185] At least one of the associated parameters is greater than or equal to a preset second threshold;

[0186] At least one of the associated parameters is less than a preset third threshold;

[0187] At least one of the associated parameters is less than or equal to a preset fourth threshold.

[0188] In some embodiments, the acquisition module is specifically used to acquire input information that meets the first requirement; and / or

[0189] Collect output information that meets the first requirement.

[0190] In some embodiments, the apparatus further includes:

[0191] The processing module is used to process the collected input information using a preset processing method to obtain the first information; and / or

[0192] The second information is obtained by processing the collected output information using a preset processing method.

[0193] In some embodiments, the output information satisfies the first requirement; and / or

[0194] The output information, after being processed by a preset processing method, meets the first requirement; and / or

[0195] The input information satisfies the first requirement; and / or

[0196] The input information, after being processed by a preset processing method, meets the first requirement.

[0197] In some embodiments, the error between the first information and the first reference information is less than a preset threshold; and / or

[0198] The error between the second information and the second reference information is less than a preset threshold.

[0199] In some embodiments, the error includes any one or a combination of the following:

[0200] Mean square error;

[0201] Normalized mean square error;

[0202] Cosine similarity;

[0203] Correlation.

[0204] In some embodiments, the artificial intelligence network is located in the Channel State Information (CSI) encoding module of the first device, and the input information does not meet the first requirement.

[0205] In some embodiments, the artificial intelligence network is located in a joint module of the first device, and the joint module includes at least a CSI feedback module.

[0206] The input information and / or the output information satisfy the first requirement; and / or

[0207] The input information and / or the output information, after being processed by a preset processing method, meet the first requirement.

[0208] In some embodiments, the preset processing method is a protocol definition, or the second device sends it to the first device, or the first device sends it to the second device, or the network-side device configures it, or the first device reports it to the network-side device.

[0209] In some embodiments, the input information and the output information include at least one of the following:

[0210] Demodulate the reference signal DMRS data;

[0211] Channel estimation data;

[0212] Reference signal estimation data;

[0213] The first required associated parameter includes at least one of the following:

[0214] timing;

[0215] Timing estimation error;

[0216] Timing advance;

[0217] Estimation error of timing lead time;

[0218] Signal-to-noise ratio;

[0219] Signal-to-noise ratio estimation error;

[0220] Noise power;

[0221] Inter-carrier interference;

[0222] Interference within the community;

[0223] Inter-cell interference.

[0224] In some embodiments, the input information and the output information include location data, and the first required association parameter includes at least one of the following:

[0225] timing;

[0226] Timing estimation error;

[0227] Timing advance;

[0228] Estimation error of timing lead time;

[0229] Signal-to-noise ratio;

[0230] Signal-to-noise ratio estimation error;

[0231] Noise power;

[0232] Inter-carrier interference;

[0233] Interference within the community;

[0234] Inter-cell interference;

[0235] The location of the first device;

[0236] The estimation error of the position of the first device.

[0237] In some embodiments, the input information and the output information include beam data, and the first required correlation parameter includes at least one of the following:

[0238] timing;

[0239] Timing estimation error;

[0240] Timing advance;

[0241] Estimation error of timing lead time;

[0242] Signal-to-noise ratio;

[0243] Signal-to-noise ratio estimation error;

[0244] Noise power;

[0245] Inter-carrier interference;

[0246] Interference within the community;

[0247] Inter-cell interference;

[0248] Beam quality;

[0249] Beam quality estimation error.

[0250] In some embodiments, the first requirement is a protocol definition, or the second device sends it to the first device, or the second device sends it to the first device, or the network-side device configures it, or the first device reports it to the network-side device.

[0251] The data acquisition device in this application embodiment can be a device, a device or electronic device with an operating system, or a component, integrated circuit, or chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal. For example, a mobile terminal can include, but is not limited to, the types of terminals 11 listed above, while a non-mobile terminal can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not impose specific limitations.

[0252] The data acquisition device provided in this application embodiment can achieve... Figure 4 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0253] Optional, such as Figure 6 As shown, this application embodiment also provides a communication device 500, including a processor 501, a memory 502, and a program or instructions stored in the memory 502 and executable on the processor 501. For example, when the communication device 500 is a first device, when the program or instructions are executed by the processor 501, they implement the various processes of the data acquisition method embodiment applied to the first device described above, and achieve the same technical effect. To avoid repetition, further details are omitted here.

[0254] This application embodiment also provides a first device, which can be a terminal, including a processor and a communication interface. The processor is used to collect input information and / or output information of an artificial intelligence network on the first device side; the communication interface is used to send first information and / or second information to a second device, wherein the first information and / or the second information satisfies a first requirement, the first information is selected from the input information, and the second information is selected from the output information. This terminal embodiment corresponds to the above-described terminal (i.e., first device) side method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and achieve the same technical effect. Specifically, Figure 7 A schematic diagram of the hardware structure of a terminal to implement an embodiment of this application.

[0255] The terminal 1000 includes, but is not limited to, at least some of the following components: radio frequency unit 1001, network module 1002, audio output unit 1003, input unit 1004, sensor 1005, display unit 1006, user input unit 1007, interface unit 1008, memory 1009, and processor 1010.

[0256] Those skilled in the art will understand that the terminal 1000 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 7 The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0257] It should be understood that, in this embodiment, the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042. The GPU 10041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1006 may include a display panel 10061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1007 includes a touch panel 10071 and other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0258] In this embodiment, the radio frequency unit 1001 receives downlink data from the network-side device and processes it for the processor 1010; additionally, it sends uplink data to the network-side device. Typically, the radio frequency unit 1001 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0259] The memory 1009 can be used to store software programs or instructions and various data. The memory 1009 may primarily include a program or instruction storage area and a data storage area. The program or instruction storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1009 may include high-speed random access memory and non-volatile memory, wherein the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. For example, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0260] Processor 1010 may include one or more processing units; optionally, processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications or instructions, and the modem processor mainly handles wireless communication, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1010.

[0261] The processor 1010 is used to collect input information and / or output information of the artificial intelligence network on the first device side; and send the first information and / or the second information to the second device, wherein the first information and / or the second information meets the first requirement, the first information is selected from the input information, and the second information is selected from the output information.

[0262] In some embodiments, the processor 1010 is configured to report the first required association parameters and / or the statistical characteristics of the association parameters.

[0263] In some embodiments, the processor 1010 is configured to receive the statistical range of the statistical features sent by the second device.

[0264] In some embodiments, the statistical characteristics include at least one of the following:

[0265] The probability distribution of the associated parameters;

[0266] The probability distribution of the error of the associated parameters;

[0267] The mean of the associated parameters;

[0268] The variance of the associated parameters;

[0269] The mean of the error of the associated parameters;

[0270] The variance of the error of the associated parameters;

[0271] The temporal correlation of the associated parameters;

[0272] The temporal correlation of the errors of the associated parameters;

[0273] The cumulative distribution function (CDF) of the associated parameters at a preset threshold;

[0274] The CDF value of the error of the associated parameter at a preset threshold.

[0275] In some embodiments, the associated parameter of the first requirement includes at least one of the following:

[0276] timing;

[0277] Timing estimation error;

[0278] Timing advance;

[0279] Estimation error of timing lead time;

[0280] Signal-to-noise ratio;

[0281] Signal-to-noise ratio estimation error;

[0282] Noise power;

[0283] Interference within the community;

[0284] Inter-cell interference;

[0285] Nonlinear parameters of the signal processing module;

[0286] Movement parameters;

[0287] The location of the first device;

[0288] The estimation error of the position of the first device;

[0289] Inter-carrier interference;

[0290] Beam quality;

[0291] Beam quality estimation error.

[0292] In some embodiments, the first requirement includes at least one of the following:

[0293] At least one of the associated parameters is greater than a preset first threshold;

[0294] At least one of the associated parameters is greater than or equal to a preset second threshold;

[0295] At least one of the associated parameters is less than a preset third threshold;

[0296] At least one of the associated parameters is less than or equal to a preset fourth threshold.

[0297] In some embodiments, the processor 1010 is configured to acquire input information that satisfies the first requirement; and / or

[0298] Collect output information that meets the first requirement.

[0299] In some embodiments, the processor 1010 is configured to process the collected input information using a preset processing method to obtain the first information; and / or

[0300] The second information is obtained by processing the collected output information using a preset processing method.

[0301] In some embodiments, the output information satisfies the first requirement; and / or

[0302] The output information, after being processed by a preset processing method, meets the first requirement; and / or

[0303] The input information satisfies the first requirement; and / or

[0304] The input information, after being processed by a preset processing method, meets the first requirement.

[0305] In some embodiments, the error between the first information and the first reference information is less than a preset threshold; and / or

[0306] The error between the second information and the second reference information is less than a preset threshold.

[0307] In some embodiments, the error includes any one or a combination of the following:

[0308] Mean square error;

[0309] Normalized mean square error;

[0310] Cosine similarity;

[0311] Correlation.

[0312] In some embodiments, the artificial intelligence network is located in the Channel State Information (CSI) encoding module of the first device, and the input information does not meet the first requirement.

[0313] In some embodiments, the artificial intelligence network is located in a joint module of the first device, and the joint module includes at least a CSI feedback module.

[0314] The input information and / or the output information satisfy the first requirement; and / or

[0315] The input information and / or the output information, after being processed by a preset processing method, meet the first requirement.

[0316] In some embodiments, the preset processing method is a protocol definition, or the second device sends it to the first device, or the first device sends it to the second device, or the network-side device configures it, or the first device reports it to the network-side device.

[0317] In some embodiments, the input information and the output information include at least one of the following:

[0318] Demodulate the reference signal DMRS data;

[0319] Channel estimation data;

[0320] Reference signal estimation data;

[0321] The first required associated parameter includes at least one of the following:

[0322] timing;

[0323] Timing estimation error;

[0324] Timing advance;

[0325] Estimation error of timing lead time;

[0326] Signal-to-noise ratio;

[0327] Signal-to-noise ratio estimation error;

[0328] Noise power;

[0329] Inter-carrier interference;

[0330] Interference within the community;

[0331] Inter-cell interference.

[0332] In some embodiments, the input information and the output information include location data, and the first required association parameter includes at least one of the following:

[0333] timing;

[0334] Timing estimation error;

[0335] Timing advance;

[0336] Estimation error of timing lead time;

[0337] Signal-to-noise ratio;

[0338] Signal-to-noise ratio estimation error;

[0339] Noise power;

[0340] Inter-carrier interference;

[0341] Interference within the community;

[0342] Inter-cell interference;

[0343] The location of the first device;

[0344] The estimation error of the position of the first device.

[0345] In some embodiments, the input information and the output information include beam data, and the first required correlation parameter includes at least one of the following:

[0346] timing;

[0347] Timing estimation error;

[0348] Timing advance;

[0349] Estimation error of timing lead time;

[0350] Signal-to-noise ratio;

[0351] Signal-to-noise ratio estimation error;

[0352] Noise power;

[0353] Inter-carrier interference;

[0354] Interference within the community;

[0355] Inter-cell interference;

[0356] Beam quality;

[0357] Beam quality estimation error.

[0358] In some embodiments, the first requirement is a protocol definition, or the second device sends it to the first device, or the second device sends it to the first device, or the network-side device configures it, or the first device reports it to the network-side device.

[0359] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described data acquisition method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0360] The processor mentioned above is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0361] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above data acquisition method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0362] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0363] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0364] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0365] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A data acquisition method, characterized by, The method is executed by a first device, and comprises: collecting input information and / or output information of an artificial intelligence network on the first device side; sending first information and / or second information to a second device, the first information and / or the second information meeting a first requirement, the first information being selected from the input information, and the second information being selected from the output information; the associated parameters of the first requirement comprising at least one of the following: timing; an estimation error of timing; timing advance; an estimation error of timing advance; signal-to-noise ratio; an estimation error of signal-to-noise ratio; noise power; intra-cell interference; inter-cell interference; a nonlinear parameter of a signal processing module; a movement parameter; a position of the first device; an estimation error of the position of the first device; inter-carrier interference; beam quality; a beam quality estimation error.

2. The data collection method of claim 1, wherein, After the step of collecting the input information and / or the output information of the artificial intelligence network on the first device side, the method further comprises: reporting the associated parameters of the first requirement and / or statistical characteristics of the associated parameters.

3. The data collection method of claim 1, wherein, Before the step of sending the first information and / or the second information to the second device, the method further comprises: receiving a statistical range of a statistical characteristic sent by the second device, the statistical characteristic being a statistical characteristic of the associated parameters of the first requirement.

4. The data collection method of claim 1, wherein, The statistical characteristics of the associated parameters of the first requirement comprise at least one of the following: a probability distribution of the associated parameters; a probability distribution of errors of the associated parameters; a mean value of the associated parameters; a variance of the associated parameters; a mean value of errors of the associated parameters; a variance of errors of the associated parameters; a time correlation of the associated parameters; a time correlation of errors of the associated parameters; a value of a cumulative distribution function (CDF) of the associated parameters at a preset threshold; a value of a CDF of errors of the associated parameters at a preset threshold.

5. The data collection method of claim 1, wherein, The first requirement comprises at least one of the following: at least one of the associated parameters being greater than a preset first threshold; at least one of the associated parameters being greater than or equal to a preset second threshold; at least one of the associated parameters being less than a preset third threshold; at least one of the associated parameters being less than or equal to a preset fourth threshold.

6. The data collection method of claim 1, wherein, The step of collecting the input information and / or the output information of the artificial intelligence network on the first device side comprises: collecting input information meeting the first requirement; and / or collecting output information meeting the first requirement.

7. The data collection method of claim 1, wherein, Before the step of sending the first information and / or the second information to the second device, the method further comprises: obtaining the first information by processing the collected input information in a preset processing manner; and / or obtaining the second information by processing the collected output information in a preset processing manner.

8. The data collection method according to claim 6, wherein: the output information meets the first requirement; and / or the output information meets the first requirement after being processed in a preset processing manner; and / or the input information meets the first requirement; and / or the input information meets the first requirement after being processed in a preset processing manner.

9. The data collection method according to claim 1, wherein: an error between the first information and first reference information is less than a preset threshold; and / or The error of the second information and the second reference information is less than a preset threshold.

10. The data collection method of claim 9, wherein, The error includes any one or more of the following in combination: mean square error; normalized mean square error; cosine similarity; correlation.

11. The data collection method of claim 1, wherein, The artificial intelligence network is located in a channel state information (CSI) encoding module of the first device, and the input information does not meet the first requirement.

12. The data collection method of claim 1, wherein, The artificial intelligence network is located in a joint module of the first device, and the joint module at least includes a CSI feedback module, The input information and / or the output information meet the first requirement; and / or The input information and / or the output information meet the first requirement after being processed by a preset processing mode.

13. The data acquisition method of claim 7 or 8 or 12, wherein, The preset processing mode is defined by a protocol, or the second device sends to the first device, or the first device sends to the second device, or a network side device configures, or the first device reports to the network side device.

14. The data collection method of claim 1, wherein, The input information and the output information include at least one of the following: demodulation reference signal (DMRS) data; channel estimation data; reference signal estimation data; The associated parameters of the first requirement include at least one of the following: timing; estimated error of timing; timing advance; estimated error of timing advance; signal-to-noise ratio; estimated error of signal-to-noise ratio; noise power; inter-carrier interference; intra-cell interference; inter-cell interference.

15. The data collection method of claim 1, wherein, The input information and the output information include positioning data, and the associated parameters of the first requirement include at least one of the following: timing; estimated error of timing; timing advance; estimated error of timing advance; signal-to-noise ratio; estimated error of signal-to-noise ratio; noise power; inter-carrier interference; intra-cell interference; inter-cell interference; position of the first device; estimated error of the position of the first device.

16. The data collection method of claim 1, wherein, The input information and the output information include beam data, and the associated parameters of the first requirement include at least one of the following: timing; estimated error of timing; timing advance; estimated error of timing advance; signal-to-noise ratio; estimated error of signal-to-noise ratio; noise power; inter-carrier interference; intra-cell interference; inter-cell interference; beam quality; estimated error of beam quality.

17. The data collection method of claim 1, wherein The first requirement is defined by a protocol, or the second device sends to the first device, or the second device sends to the first device, or a network side device configures, or the first device reports to the network side device.

18. A data acquisition device, characterized by The device is applied to a first device and includes: a collection module configured to collect input information and / or output information of an artificial intelligence network on the first device side; a reporting module configured to send first information and / or second information to a second device, wherein the first information and / or the second information meet a first requirement, the first information is selected from the input information, and the second information is selected from the output information; The associated parameters of the first requirement include at least one of the following: timing; estimated error of timing; timing advance; estimated error of timing advance; signal-to-noise ratio; estimated error of signal-to-noise ratio; noise power; intra-cell interference; inter-cell interference; nonlinear parameter of a signal processing module; movement parameter; position of the first device; an estimation error of a position of the first device; inter-carrier interference; beam quality; a beam quality estimation error.

19. The data acquisition device of claim 18, wherein, The reporting module is further configured to report the first required correlation parameter and / or a statistical feature of the correlation parameter.

20. The data acquisition device of claim 18, wherein, The apparatus further includes: a receiving module configured to receive a statistical range of the statistical feature sent by the second device, the statistical feature being a statistical feature of the first required correlation parameter.

21. The data acquisition device of claim 18, wherein, The statistical feature of the first required correlation parameter includes at least one of: a probability distribution of the correlation parameter; a probability distribution of an error of the correlation parameter; a mean value of the correlation parameter; a variance of the correlation parameter; a mean value of an error of the correlation parameter; a variance of an error of the correlation parameter; a time correlation of the correlation parameter; a time correlation of an error of the correlation parameter; a value of a cumulative distribution function (CDF) of the correlation parameter at a preset threshold; a value of a CDF of an error of the correlation parameter at a preset threshold.

22. The data acquisition device of claim 18, wherein, The first requirement includes at least one of: at least one of the correlation parameters being greater than a preset first threshold; at least one of the correlation parameters being greater than or equal to a preset second threshold; at least one of the correlation parameters being less than a preset third threshold; at least one of the correlation parameters being less than or equal to a preset fourth threshold.

23. The data acquisition device of claim 18, wherein, The collecting module is specifically configured to collect input information satisfying the first requirement; and / or collect output information satisfying the first requirement.

24. The data acquisition device of claim 18, wherein, The apparatus further includes: a processing module configured to obtain the first information by processing the collected input information in a preset processing manner; and / or obtain the second information by processing the collected output information in a preset processing manner.

25. The data collection apparatus according to claim 23, wherein the output information satisfies the first requirement; and / or the output information satisfies the first requirement after being processed in a preset processing manner; and / or the input information satisfies the first requirement; and / or the input information satisfies the first requirement after being processed in a preset processing manner.

26. The data collection apparatus according to claim 18, wherein an error between the first information and first reference information is less than a preset threshold; and / or an error between the second information and second reference information is less than a preset threshold.

27. The data acquisition device of claim 26, wherein, The error includes any one or a combination of: a mean square error; a normalized mean square error; a cosine similarity; a correlation.

28. The data acquisition device of claim 18, wherein, The artificial intelligence network is located in a channel state information (CSI) encoding module of the first device, and the input information does not satisfy the first requirement.

29. The data acquisition device of claim 18, wherein, The artificial intelligence network is located in a joint module of the first device, the joint module at least including a CSI feedback module, the input information and / or the output information satisfy the first requirement; and / or the input information and / or the output information satisfy the first requirement after being processed in a preset processing manner.

30. The data acquisition device of claim 24 or 25 or 29, wherein, The preset processing manner is defined by a protocol, or is sent by the second device to the first device, or is sent by the first device to the second device, or is configured by a network side device, or is reported by the first device to the network side device.

31. The data collection device of claim 18, wherein, The input information and the output information comprise at least one of: Demodulation reference signal (DMRS) data; Channel estimation data; Reference signal estimation data; The associated parameters of the first requirement comprise at least one of: Timing; Estimation error of the timing; Timing advance; Estimation error of the timing advance; Signal-to-noise ratio (SNR); Estimation error of the SNR; Noise power; Inter-carrier interference (ICI); Intra-cell interference (ICI); Inter-cell interference (ICI).

32. The data collection device of claim 18, wherein, The input information and the output information comprise positioning data, and the associated parameters of the first requirement comprise at least one of: Timing; Estimation error of the timing; Timing advance; Estimation error of the timing advance; Signal-to-noise ratio (SNR); Estimation error of the SNR; Noise power; Inter-carrier interference (ICI); Intra-cell interference (ICI); Inter-cell interference (ICI); Position of the first device; Estimation error of the position of the first device.

33. The data collection device of claim 18, wherein, The input information and the output information comprise beam data, and the associated parameters of the first requirement comprise at least one of: Timing; Estimation error of the timing; Timing advance; Estimation error of the timing advance; Signal-to-noise ratio (SNR); Estimation error of the SNR; Noise power; Inter-carrier interference (ICI); Intra-cell interference (ICI); Inter-cell interference (ICI); Beam quality; Beam quality estimation error.

34. The data collection apparatus of claim 18, wherein the first requirement is defined by a protocol, or the second device sends to the first device, or the second device sends to the first device, or a network-side device configures, or the first device reports to the network-side device. A processor, a memory, and a program or instructions stored in the memory and executable on the processor, the program or instructions being executed by the processor to implement the steps of the data collection method of any one of claims 1 to 17.

35. A first device, comprising: A readable storage medium storing a program or instructions, the program or instructions being executed by a processor to implement the steps of the data collection method of any one of claims 1 to 17.

36. A readable storage medium, characterized by, ​

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