Information collection method and device based on big data

Abstract

The invention provides an information acquisition method and device based on big data. The method comprises the following steps: acquiring position information, signal sending power and signal quality detection data of each first device in a preset area; analyzing each signal quality detection data by using a big data model, and determining channel loss of the wireless signal transmitted in the preset area according to an analysis result and each piece of position information; determining a signal-to-noise ratio corresponding to each piece of first equipment according to the channel loss and each piece of signal sending power; according to each signal-to-noise ratio and a preset signal-to-noise ratio threshold value, judging whether each first device meets an information acquisition quality requirement or not; if the number of the first devices meeting the requirement is greater than or equal to a preset threshold value, sending an information acquisition instruction to at least one first device; and otherwise, sending a power adjustment instruction to the at least one first device, and recalculating the adjusted signal-to-noise ratio until the number of the first devices meeting the requirement is greater than or equal to the preset threshold.

Description

Technical Field

[0001] This application relates to the field of transformer monitoring technology, and in particular to an information acquisition method and device based on big data. Background Technology

[0002] Information collection is the foundation of information technology, and with the development of the Internet of Things (IoT), more and more electronic devices need to read or send information data, which leads to mutual interference between wireless signals. This interference can be divided into two aspects: one is the interference between transmitted signals caused by multiple transmitters sending information data to the same receiver; the other is the interference between request signals caused by multiple receivers requesting information data from the same transmitter.

[0003] Currently, to suppress the two types of interference mentioned above, the following two methods are generally used: transmitting signals through different channels and transmitting signals through the same channel at different times. However, with the information explosion and the advent of the big data era, the demand for signal transmission has also experienced explosive growth. However, wireless channel resources are limited, and many time-division multiplexing methods have reduced channel utilization, resulting in delays in wireless signal transmission, which is increasingly unable to meet the information collection requirements of the big data era.

[0004] Therefore, how to reduce mutual interference between signals during information acquisition while simultaneously improving channel communication utilization has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for information acquisition based on big data, in order to solve the technical problem of how to reduce mutual interference between signals during information acquisition and at the same time improve the utilization rate of channel communication.

[0006] Firstly, this application provides a method for information collection based on big data, including:

[0007] Acquire the location information, signal transmission power, and signal quality detection data of each first device in the preset area; analyze the signal quality detection data using a big data model, and determine the channel loss of wireless signal transmission in the preset area based on the analysis results and the location information.

[0008] Based on channel loss and the transmission power of each signal, determine the signal-to-noise ratio (SNR) for each first device; based on each SNR and a preset SNR threshold, determine whether each first device meets the information acquisition quality requirements;

[0009] If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, an information collection instruction is sent to at least one first device. The information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device.

[0010] Otherwise, a power adjustment command is sent to at least one first device, and the adjusted signal-to-noise ratio is recalculated until the number of first devices that meet the information acquisition quality requirements is greater than or equal to a preset threshold.

[0011] In one possible design, the channel loss includes: a first channel loss and a second channel loss, wherein the first channel loss includes the loss of signal transmission between the respective first devices, and the second channel loss includes the average loss of signal transmission between each first device and at least one second device.

[0012] Based on channel loss and the transmission power of each signal, the signal-to-noise ratio (SNR) for each first device is determined, including:

[0013] Based on the loss of each first channel and the transmission power of each signal, calculate the total interference value of each first device caused by interference from other first devices;

[0014] The signal-to-noise ratio corresponding to each first device is determined based on the transmission power of each signal, the loss of each second channel, and the total interference value.

[0015] In one possible design, based on the first channel loss and the signal transmission power, the total interference value of each first device caused by interference from other first devices is calculated, including:

[0016] interrere i =ω i ∑Lost1 i,j ·P i (i,j∈N)

[0017] Among them, interfere i Lost1 represents the total interference value of the i-th first device. i,j P is the first channel loss between the i-th first device and the j-th first device. i Let ω be the signal transmission power of the i-th first device. i Let N be the weight, and N be the set of all the first devices.

[0018] In one possible design, the signal-to-noise ratio (SNR) for each first device is determined based on the transmission power of each signal, the loss of each second channel, and the total interference value, including:

[0019]

[0020] Among them, interfere i Lost2 represents the total interference value of the i-th first device. i P is the second channel loss between the i-th first device and at least one second device within the information acquisition range of the first device. i Let be the signal transmission power of the i-th first device. This is for adjusting the coefficient.

[0021] In one possible design, sending a power adjustment command to at least one first device includes:

[0022] Obtain the current power distribution map of the preset area. The power distribution map is used to characterize the distribution of signal transmission power of all first devices in the preset area.

[0023] Based on the total interference values ​​and power distribution diagram, determine the power adjustment commands for one or more first devices.

[0024] In one possible design, based on the total interference values ​​and the power distribution diagram, power adjustment commands for one or more first devices are determined, including:

[0025] Obtain the current information collection range of each primary device;

[0026] By using big data models to model and analyze the various information collection ranges and power distribution maps, the information collection probability distribution function and the power distribution probability density function corresponding to each first device are obtained;

[0027] Based on the probability distribution function of each information acquisition and each total interference value, determine the shape control parameters of the power distribution probability density function;

[0028] The adjustment values ​​of shape control parameters are calculated using big data models, and power adjustment commands are determined based on these adjustment values.

[0029] In one possible design, the shape control parameters of the power distribution probability density function are determined based on the probability distribution functions of each information acquisition and each total interference value, including:

[0030] S C =h(F(r) i interfere i )

[0031] Among them, S C For shape control parameters, F(r) i ) represents the probability distribution function for information collection, interfere i Let h(*) be the total interference value, and h(*) be the power distribution probability density function.

[0032] In one possible design, when the power distribution probability density function is a probability density function that follows a beta distribution, the shape control parameters include: a first parameter α and a second parameter β.

[0033] Secondly, this application provides an information collection device based on big data, comprising:

[0034] The acquisition module is used to acquire the location information, signal transmission power, and signal quality detection data of each first device in the preset area;

[0035] Processing module, used for:

[0036] The big data model is used to analyze the signal quality detection data of each signal, and the channel loss of wireless signal transmission in the preset area is determined based on the analysis results and the location information.

[0037] The signal-to-noise ratio corresponding to each first device is determined based on channel loss and the transmission power of each signal;

[0038] Based on the signal-to-noise ratio and the preset signal-to-noise ratio threshold, determine whether each first device meets the information acquisition quality requirements;

[0039] If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, an information collection instruction is sent to at least one first device. The information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device.

[0040] Otherwise, a power adjustment command is sent to at least one first device, and the adjusted signal-to-noise ratio is recalculated until the number is greater than or equal to a preset threshold.

[0041] Thirdly, this application provides a device suitable for use in an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0042] The memory stores the instructions that the computer executes;

[0043] The processor executes computer execution instructions stored in memory to implement any of the possible big data-based information acquisition methods provided in the first aspect.

[0044] Fourthly, this application provides a storage medium that stores computer-executable instructions, which, when executed by a processor, are used to implement any of the possible big data-based information acquisition methods provided in the first aspect.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the possible big data-based information collection methods provided in the first aspect.

[0046] This application provides a big data-based information acquisition method and apparatus. It acquires the location information, signal transmission power, and signal quality detection data of each first device in a preset area; analyzes the signal quality detection data using a big data model; determines the channel loss of wireless signal transmission in the preset area based on the analysis results and location information; determines the signal-to-noise ratio (SNR) for each first device based on the channel loss and the transmission power of each signal; determines whether each first device meets the information acquisition quality requirements based on the SNR and a preset SNR threshold; if the number of first devices meeting the requirements is greater than or equal to the preset threshold, an information acquisition command is sent to at least one first device; otherwise, a power adjustment command is sent to at least one first device, and the adjusted SNR is recalculated until the number of first devices meeting the requirements is greater than or equal to the preset threshold. By using the SNR of signal transmission between monitoring electronic devices to adjust the signal transmission power of each device, the technical problem of reducing mutual interference between signals during information acquisition and simultaneously improving channel communication utilization is solved. Because signal transmission always involves losses, controlling the signal transmission power can control the range of influence of electronic devices. In this way, even if signal collisions occur, the energy of the interfering signal will not reach the level of affecting the target signal, thus avoiding the invalid retransmission of the target signal and achieving the technical effect of improving channel utilization. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] Figure 1 A schematic diagram of the structure of an information collection system provided in this application;

[0049] Figure 2 A flowchart illustrating an information collection method based on big data, provided as an embodiment of this application;

[0050] Figure 3 A flowchart illustrating another big data-based information collection method provided for the implementation of this application;

[0051] Figure 4 A flowchart illustrating a possible implementation of step S306 provided in an embodiment of this application;

[0052] Figure 5 A schematic diagram of the structure of an information collection device based on big data provided in this application embodiment;

[0053] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0054] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort, including but not limited to combinations of multiple embodiments, are within the scope of protection of this application.

[0056] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] With the increasing number of information acquisition devices, wireless signal interference occurs during data transmission between these devices. Currently, two common methods are used: frequency division multiplexing (FDM) and time division multiplexing (TDM), which transmit signals through different channels and transmit signals through the same channel at different times. However, with the information explosion and the advent of the big data era, the demand for signal transmission has also exploded. Wireless channel resources are limited, and many time-division multiplexing methods reduce channel utilization, resulting in latency in wireless signal transmission. This increasingly fails to meet the information acquisition requirements of the big data era. Therefore, how to reduce mutual interference between signals during information acquisition while simultaneously improving channel communication utilization has become an urgent technical problem to be solved.

[0058] To solve the above problems, the inventive concept of this application is:

[0059] Currently, both frequency division and time division transmission employ a fixed operating mode for electronic devices to collect information. Neither approach considers the physical space of the wireless signal. The physical space refers to a specific three-dimensional physical region. If, at any given moment, the target signal in this region has an absolute energy advantage, reducing the transmission energy of non-target signals (interference signals), the receiver can identify the target signal without being affected by interference. Therefore, by dynamically controlling the signal transmission power of each device within this three-dimensional physical region, it is possible to minimize signal collisions and interference in both the channel and time dimensions.

[0060] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0061] Figure 1 This is a schematic diagram of the structure of an information collection system provided in this application. Figure 1 As shown, the information collection system includes: multiple data receiving ends, namely the first device 110, multiple data sending ends, namely the second device 120, and a regional server 130.

[0062] It should be noted that an electronic device can act as a first device 110 to receive information collection data, or as a second device 120 to send information collection data, at different times. The area server 130 is a big data processing server, which acts as a control center to uniformly allocate the signal power distribution corresponding to the signal transmission power of various electronic devices in the preset area.

[0063] Figure 2 This is a flowchart illustrating an information collection method based on big data, provided as an embodiment of this application. Figure 2 As shown, this data processing method is applied to Figure 1 The management platform or central control platform in the data processing network 130 includes the following specific steps:

[0064] S201. Obtain the location information, signal transmission power, and signal quality detection data of each first device in the preset area; analyze the signal quality detection data using a big data model, and determine the channel loss of wireless signal transmission in the preset area based on the analysis results and the location information.

[0065] In this step, the preset area is the area corresponding to the area server 130, and there are one or more first devices 110 and one or more second devices 120 in the preset area. Optionally, the location of the first device 110 can be fixed or can change over time, that is, the first device 110 can be a mobile terminal.

[0066] The signal quality detection data includes the signal strength and signal recognition probability fed back by each first device 110 to the regional server 130.

[0067] The big data model calculates the distance between any two first devices 110 using their location information. Then, by using signal command detection data and the aforementioned distances, a correspondence model or functional relationship is established between the two, thereby obtaining the channel loss generated when the wireless signal is transmitted in a preset area. It is worth noting that this channel loss is distance-dependent; generally, the greater the distance, the greater the loss. However, due to other environmental factors within the preset area, it does not strictly follow a direct proportional relationship. Therefore, a big data model is needed to obtain the channel loss at different locations and distances.

[0068] S202. Determine the signal-to-noise ratio (SNR) for each first device based on channel loss and the transmission power of each signal; determine whether each first device meets the information acquisition quality requirements based on the SNR and the preset SNR threshold.

[0069] In this step, the signal-to-noise ratio (SNR) for each first device is determined based on channel loss and the transmission power of each signal, including:

[0070] First, the regional server 130 determines the number and identification information of the first devices 110 that need to send information collection request signals to the second device 120 at the current moment, based on the information collection plan of each first device 110 or the information collection request uploaded by the first device 110.

[0071] Then, based on the location information of each first device 110 that sends the information collection application signal as needed, the location of the interference source received by each first device 110 in the preset area is determined.

[0072] Then, based on the location of each interference source and the signal transmission power corresponding to the interference signal (i.e. the information collection request signal sent by other first devices 110), the interference power intensity transmitted to each first device 110 can be calculated. Then, by superimposing each interference power, the total interference value received by each first device 110 can be obtained.

[0073] Finally, the ratio of the signal transmission power of each first device 110 to the total interference value is its corresponding signal-to-noise ratio.

[0074] In this embodiment, the information acquisition quality requirements include: the signal-to-noise ratio (SNR) corresponding to the first device 110 should be greater than or equal to a preset SNR threshold. This ensures that interference signals are not sufficient to affect the identification of the target signal sent or received by the first device.

[0075] If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, then step S203 is executed; otherwise, step S204 is executed until the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold.

[0076] S203, Send an information collection command to at least one first device.

[0077] In this step, the information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device.

[0078] Specifically, if the number of first devices meeting the information acquisition quality requirements is greater than or equal to a preset threshold, it proves that the mutual interference between the first devices in the preset area has been reduced to an acceptable range. At this point, at least one target first device for information acquisition can be determined from the intersection of the first list of first devices meeting the information acquisition quality requirements and the second list of first devices that need to acquire information at the current moment, and an information acquisition command can be sent to it. After receiving the information acquisition command, the target first device establishes wireless communication with each second device within its current information acquisition range using its current signal transmission power and receives the information transmitted by these second devices.

[0079] It is worth noting that at this time, interference between the various second devices can be avoided by using existing same-frequency time-division multiplexing transmission or different-frequency transmission methods, which will not be elaborated here.

[0080] S204. Send a power adjustment command to at least one first device.

[0081] In this step, the area server 130 sends one or more transmission power adjustment commands to one or more first devices in a preset area whose signal-to-noise ratio (SNR) is less than a preset SNR threshold, so that their SNR is greater than or equal to the preset SNR threshold. That is, as follows... Figure 2 As shown in this embodiment, after completing step S204, it is necessary to return to step S202.

[0082] This embodiment provides a big data-based information acquisition method. It acquires the location information, signal transmission power, and signal quality detection data of each first device in a preset area. A big data model is used to analyze the signal quality detection data, and based on the analysis results and location information, the channel loss of wireless signal transmission in the preset area is determined. Based on the channel loss and the signal transmission power, the signal-to-noise ratio (SNR) for each first device is determined. Based on the SNR and a preset SNR threshold, it is determined whether each first device meets the information acquisition quality requirements. If the number of first devices meeting the requirements is greater than or equal to the preset threshold, an information acquisition command is sent to at least one first device; otherwise, a power adjustment command is sent to at least one first device, and the adjusted SNR is recalculated until the number of first devices meeting the requirements is greater than or equal to the preset threshold. By using the SNR of signal transmission between monitoring electronic devices to adjust the signal transmission power of each device, the technical problem of how to reduce mutual interference between signals during information acquisition and simultaneously improve channel communication utilization is solved. Because signal transmission always involves losses, controlling the signal transmission power can control the range of influence of electronic devices. In this way, even if signal collisions occur, the energy of the interfering signal will not reach the level of affecting the target signal, thus avoiding the invalid retransmission of the target signal and achieving the technical effect of improving channel utilization.

[0083] To facilitate understanding, the specific implementation of S202 will be further explained below:

[0084] Figure 3 A flowchart illustrating another big data-based information collection method provided for the implementation of this application. (For example...) Figure 3 As shown, the specific steps of this data processing method include:

[0085] S301. Obtain the location information, signal transmission power, and signal quality detection data of each first device in the preset area.

[0086] For step S301, please refer to step S201, which will not be repeated here.

[0087] S302. Analyze the signal quality detection data using a big data model, and determine the channel loss of wireless signal transmission in the preset area based on the analysis results and location information.

[0088] In this embodiment, channel loss includes: first channel loss and second channel loss. The first channel loss includes the loss of signal transmission between each first device, and the second channel loss includes the average loss of signal transmission between each first device and at least one second device.

[0089] S303. Determine the signal-to-noise ratio for each first device based on channel loss and the transmission power of each signal.

[0090] In this embodiment, the specific steps include:

[0091] S3031. Based on the loss of each first channel and the transmission power of each signal, calculate the total interference value of each first device caused by interference from other first devices.

[0092] Specifically, in one possible implementation, this step can be represented by formula (1), which is shown below:

[0093] interfere i =ω i ∑Lost1 i,j ·P i (i,j∈N) (2)

[0094] Among them, interfere i Lost1 represents the total interference value of the i-th first device. i,j P is the first channel loss between the i-th first device and the j-th first device. i Let ω be the signal transmission power of the i-th first device. i Let N be the weight, and N be the set of all the first devices.

[0095] S3032. Determine the signal-to-noise ratio corresponding to each first device based on the transmission power of each signal, the loss of each second channel, and the total interference value.

[0096] Specifically, in one possible implementation, this step can be represented by formula (2), which is shown below:

[0097]

[0098] Among them, interfere i Lost2 represents the total interference value of the i-th first device. i P is the second channel loss between the i-th first device and at least one second device within the information acquisition range of the first device. i Let be the signal transmission power of the i-th first device. This is for adjusting the coefficient.

[0099] S304. Determine whether each first device meets the information acquisition quality requirements based on the signal-to-noise ratio and the preset signal-to-noise ratio threshold.

[0100] In this step, it is specifically determined whether each signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio threshold.

[0101] If the number of first devices that meet the information collection quality requirements is less than the preset threshold, then step S305 is executed; otherwise, step S307 is executed until the number of first devices that meet the information collection quality requirements is greater than or equal to the preset threshold.

[0102] S305. Obtain the current power distribution map of the preset area.

[0103] In this step, the power distribution map is used to characterize the distribution of signal transmission power of all first devices in the preset area.

[0104] S306. Based on the total interference values ​​and the power distribution diagram, determine one or more power adjustment commands for the first device.

[0105] In one possible design, the specific implementation of this step is as follows: Figure 4 As shown:

[0106] Figure 4 This is a flowchart illustrating one possible implementation of step S306 provided in an embodiment of this application. Figure 4 As shown, the specific steps include:

[0107] S401. Obtain the current information collection range of each first device.

[0108] In this step, the information acquisition range is related to the signal transmission power. The information acquisition radius of each first device can be calculated using big data models and channel loss, thereby obtaining the information acquisition range in three-dimensional space.

[0109] S402. Through big data modeling, model and analyze each information collection range and power distribution map to obtain the information collection probability distribution function and the power distribution probability density function corresponding to each first device.

[0110] S403. Determine the shape control parameters of the power distribution probability density function based on the probability distribution function of each information acquisition and each total interference value.

[0111] In steps S402 and S403, the shape control parameters can be specifically expressed by formula (3):

[0112] S C =h(F(r) i interfere i (3)

[0113] Among them, S C For shape control parameters, F(r) i ) represents the probability distribution function for information collection, interfere iLet h(*) be the total interference value, and h(*) be the power distribution probability density function.

[0114] Optionally, when the power distribution probability density function is a probability density function that follows a β-beta distribution, the shape control parameters include: the first parameter α and the second parameter β.

[0115] At this point, the probability density function of the β distribution can be expressed by formula (4):

[0116]

[0117] Where gamma(*) is the gamma function, α is the first parameter, and β is the second parameter.

[0118] S404. Calculate the adjustment value of the shape control parameters through a big data model, and determine the power adjustment command based on the adjustment value.

[0119] In this step, the shape control parameters are continuously adjusted through multiple iterations of calculations using a big data model. This alters the shape of the power distribution probability density function, thereby adjusting the signal transmission power distribution of each first device within a preset area. This achieves the goal of avoiding or reducing mutual interference between the first devices based on big data or statistical principles. Simultaneously, it improves channel utilization because it allows for the reuse of wireless channels in different three-dimensional areas. Furthermore, since it operates in different areas, it is not time-limited, enabling area-based, non-time-division multiplexing of channels. This significantly expands the resource utilization of wireless signal transmission, meeting the signal transmission needs of large-scale concurrent information acquisition in big data applications.

[0120] S307. Send an information collection command to at least one first device.

[0121] In this step, the information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device.

[0122] This embodiment provides a big data-based information acquisition method. It acquires the location information, signal transmission power, and signal quality detection data of each first device in a preset area. A big data model is used to analyze the signal quality detection data, and based on the analysis results and location information, the channel loss of wireless signal transmission in the preset area is determined. Based on the channel loss and the signal transmission power, the signal-to-noise ratio (SNR) for each first device is determined. Based on the SNR and a preset SNR threshold, it is determined whether each first device meets the information acquisition quality requirements. If the number of first devices meeting the requirements is greater than or equal to the preset threshold, an information acquisition command is sent to at least one first device; otherwise, a power adjustment command is sent to at least one first device, and the adjusted SNR is recalculated until the number of first devices meeting the requirements is greater than or equal to the preset threshold. By using the SNR of signal transmission between monitoring electronic devices to adjust the signal transmission power of each device, the technical problem of how to reduce mutual interference between signals during information acquisition and simultaneously improve channel communication utilization is solved. Because signal transmission always involves losses, controlling the signal transmission power can control the range of influence of electronic devices. In this way, even if a signal collision occurs, the energy of the interfering signal will not be enough to affect the target signal, thus avoiding the invalid retransmission of the target signal and achieving the technical effect of improving channel utilization.

[0123] Figure 5 This is a schematic diagram of a big data-based information collection device provided in an embodiment of this application. The big data-based information collection device 500 can be implemented through software, hardware, or a combination of both.

[0124] like Figure 5 As shown, the big data-based information collection device 500 includes:

[0125] The acquisition module 501 is used to acquire the location information, signal transmission power and signal quality detection data of each first device in the preset area;

[0126] Processing module 502 is used for:

[0127] The big data model is used to analyze the signal quality detection data of each signal, and the channel loss of wireless signal transmission in the preset area is determined based on the analysis results and the location information.

[0128] The signal-to-noise ratio corresponding to each first device is determined based on channel loss and the transmission power of each signal;

[0129] Based on the signal-to-noise ratio and the preset signal-to-noise ratio threshold, determine whether each first device meets the information acquisition quality requirements;

[0130] If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, an information collection instruction is sent to at least one first device. The information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device.

[0131] Otherwise, a power adjustment command is sent to at least one first device, and the adjusted signal-to-noise ratio is recalculated until the number is greater than or equal to a preset threshold.

[0132] In one possible design, the channel loss includes: a first channel loss and a second channel loss, wherein the first channel loss includes the loss of signal transmission between the respective first devices, and the second channel loss includes the average loss of signal transmission between each first device and at least one second device.

[0133] Processing module 502 is used for:

[0134] Based on the loss of each first channel and the transmission power of each signal, calculate the total interference value of each first device caused by interference from other first devices;

[0135] The signal-to-noise ratio corresponding to each first device is determined based on the transmission power of each signal, the loss of each second channel, and the total interference value.

[0136] In one possible design, the processing module 502 is used to calculate the total interference value of each first device caused by interference from other first devices based on the first channel loss and the signal transmission power, including:

[0137] interfere i =ω i ∑Lost1 i,j ·P i (i,j∈F)

[0138] Among them, interfere i Lost1 represents the total interference value of the i-th first device. i,j P is the first channel loss between the i-th first device and the j-th first device. i Let ω be the signal transmission power of the i-th first device. i Let N be the weight, and N be the set of all the first devices.

[0139] In one possible design, the processing module 502 is configured to determine the signal-to-noise ratio (SNR) for each first device based on the transmission power of each signal, the loss of each second channel, and the total interference value, including:

[0140]

[0141] Among them, interfere iLost2 represents the total interference value of the i-th first device. i P is the second channel loss between the i-th first device and at least one second device within the information acquisition range of the first device. i Let be the signal transmission power of the i-th first device. This is for adjusting the coefficient.

[0142] In one possible design, the acquisition module 501 is also used to acquire the current power distribution map of the preset area, which is used to characterize the distribution of signal transmission power of all first devices in the preset area.

[0143] The processing module 502 is also used to determine power adjustment instructions for one or more first devices based on the total interference values ​​and the power distribution diagram.

[0144] In one possible design, the acquisition module 501 is also used to acquire the current information collection range of each first device;

[0145] Processing module 502 is also used for:

[0146] By using big data models to model and analyze the various information collection ranges and power distribution maps, the information collection probability distribution function and the power distribution probability density function corresponding to each first device are obtained;

[0147] Based on the probability distribution function of each information acquisition and each total interference value, determine the shape control parameters of the power distribution probability density function;

[0148] The adjustment values ​​of shape control parameters are calculated using big data models, and power adjustment commands are determined based on these adjustment values.

[0149] In one possible design, the processing module 502 is further configured to determine the shape control parameters of the power distribution probability density function based on the probability distribution functions of each information acquisition and each total interference value, including:

[0150] S C =h(F(r) i interfere i )

[0151] Among them, S C For shape control parameters, F(r) i ) represents the probability distribution function for information collection, interfere i Let h(*) be the total interference value, and h(*) be the power distribution probability density function.

[0152] In one possible design, when the power distribution probability density function is a probability density function that follows a beta distribution, the shape control parameters include: a first parameter α and a second parameter β.

[0153] It is worth noting that, Figure 5 The apparatus provided in the illustrated embodiments can execute the methods provided in any of the above method embodiments. Their specific implementation principles, technical features, explanations of technical terms, and technical effects are similar and will not be repeated here.

[0154] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 600 may include at least one processor 601 and a memory 602. Figure 6 The device shown is an example of a processor.

[0155] The memory 602 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions.

[0156] The memory 602 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0157] The processor 601 is used to execute computer execution instructions stored in the memory 602 to implement the methods described in the above embodiments.

[0158] The processor 601 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0159] Optionally, the memory 602 can be either standalone or integrated with the processor 601. When the memory 602 is a device independent of the processor 601, the electronic device 600 may further include:

[0160] Bus 603 is used to connect the processor 601 and the memory 602. The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not mean there is only one bus or one type of bus.

[0161] Optionally, in a specific implementation, if the memory 602 and the processor 601 are integrated on a single chip, the memory 602 and the processor 601 can communicate through an internal interface.

[0162] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used in the methods described in the above-mentioned method embodiments.

[0163] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods described in the above-described method embodiments.

[0164] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0165] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for information collection based on big data, characterized in that, include: Acquire the location information, signal transmission power, and signal quality detection data of each first device in the preset area; The signal quality detection data are analyzed using a big data model. Based on the analysis results and the location information, the channel loss of the wireless signal in the preset area is determined. Based on the channel loss and the transmission power of each signal, the signal-to-noise ratio (SNR) of each first device is determined; based on the SNR and a preset SNR threshold, it is determined whether each first device meets the information acquisition quality requirements. If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, then an information collection instruction is sent to at least one first device. The information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device. Otherwise, a power adjustment command is sent to at least one of the first devices, and the adjusted signal-to-noise ratio is recalculated until the number is greater than or equal to the preset threshold.

2. The information collection method based on big data according to claim 1, characterized in that, The channel loss includes: a first channel loss and a second channel loss, wherein the first channel loss includes the loss of signal transmission between each of the first devices, and the second channel loss includes the average loss of signal transmission between each of the first devices and at least one second device. The step of determining the signal-to-noise ratio (SNR) for each of the first devices based on the channel loss and the transmission power of each signal includes: Based on the loss of each first channel and the transmission power of each signal, calculate the total interference value of each first device caused by the interference of other first devices. The signal-to-noise ratio corresponding to each of the first devices is determined based on the transmission power of each of the signals, the loss of each of the second channels, and the total interference value.

3. The information collection method based on big data according to claim 2, characterized in that, The step of calculating the total interference value of each first device caused by interference from other first devices based on the respective first channel loss and the respective signal transmission power includes: interfere i =ω i ∑Lost1 i,j ·P i (i,j∈N) Among them, interfere i Lost1 is the total interference value of the i-th first device. i,j P is the first channel loss between the i-th first device and the j-th first device. i ω is the signal transmission power of the i-th first device. i Let N be the weight, and N be the set of the first devices.

4. The information collection method based on big data according to claim 2, characterized in that, Determining the signal-to-noise ratio (SNR) for each of the first devices based on the transmission power of each signal, the loss of each of the second channels, and the total interference value includes: Among them, interfere i Lost2 is the total interference value of the i-th first device. i For the information acquisition range of the i-th first device and the second channel loss of at least one second device, P i The signal transmission power of the i-th first device, This is for adjusting the coefficient.

5. The information collection method based on big data according to any one of claims 1-4, characterized in that, Sending a power adjustment command to at least one of the first devices includes: Obtain the current power distribution map of the preset area, the power distribution map being used to characterize the distribution of the signal transmission power of all the first devices in the preset area; Based on the total interference values ​​and the power distribution diagram, one or more power adjustment commands for the first device are determined.

6. The information collection method based on big data according to claim 5, characterized in that, The step of determining one or more power adjustment commands for the first device based on the total interference values ​​and the power distribution map includes: Obtain the current information collection range of each of the first devices; The big data model is used to model and analyze each of the information collection ranges and the power distribution map to obtain the information collection probability distribution function and the power distribution probability density function corresponding to each of the first devices. Based on the probability distribution functions of each of the information acquisitions and the total interference values, determine the shape control parameters of the power distribution probability density function; The adjustment value of the shape control parameter is calculated using the big data model, and the power adjustment command is determined based on the adjustment value.

7. The information collection method based on big data according to claim 6, characterized in that, The step of determining the shape control parameters of the power distribution probability density function based on each of the information acquisition probability distribution functions and each of the total interference values ​​includes: S C =h(F(r i ),interfere i ) Among them, S C For the shape control parameter, F(r) i ) is the probability distribution function for the information collection, interfere i Let h(*) be the total interference value, and h(*) be the power distribution probability density function.

8. The information collection method based on big data according to claim 6, characterized in that, When the power distribution probability density function is a probability density function that follows a beta distribution, the shape control parameters include: a first parameter α and a second parameter β.

9. An information collection device based on big data, characterized in that, include: The acquisition module is used to acquire the location information, signal transmission power, and signal quality detection data of each first device in the preset area; Processing module, used for: The signal quality detection data are analyzed using a big data model. Based on the analysis results and the location information, the channel loss of the wireless signal in the preset area is determined. Based on the channel loss and the transmission power of each signal, the signal-to-noise ratio corresponding to each of the first devices is determined; Based on the signal-to-noise ratio and the preset signal-to-noise ratio threshold, it is determined whether each of the first devices meets the information acquisition quality requirements; If the number of first devices that meet the information collection quality requirements is greater than or equal to a preset threshold, then an information collection instruction is sent to at least one first device. The information collection instruction is used to instruct the first device to establish wireless communication with at least one second device and to receive information sent by the second device. Otherwise, a power adjustment command is sent to at least one of the first devices, and the adjusted signal-to-noise ratio is recalculated until the number is greater than or equal to the preset threshold.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the big data-based information collection method as described in any one of claims 1 to 8.

Images