CN115802438A - Data processing method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the application discloses a data processing method, a data processing device, data processing equipment and a data processing medium. Wherein, the method comprises the following steps: acquiring average residual energy in the clusters; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located; determining a target value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head; according to the target numerical value, sending observation data to the cluster head, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster; wherein the cluster head is predetermined based on a low power consumption adaptive clustering hierarchical protocol. According to the technical scheme, the average residual energy of each terminal device in the cluster is balanced, the effect of prolonging the service life of the network is achieved, and in the data transmission process, the data transmission cost is reduced due to the fact that the data transmission quantity is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and medium.
Background
In the field of edge computing, the application of 4G and 5G technologies is benefited, data transmission is faster and more convenient, but the transmission cost of a large amount of data is obviously increased; how to transmit data from the edge to the cloud end at low cost is a problem to be solved urgently.
The traditional data compression technology can complete the compression of data through complex operation, thereby reducing the cost of data returning from the edge to the cloud; however, the computing power of the terminal devices in the edge computing field is uneven, and if the computing power of the terminal devices in the edge computing field is poor, the computing power of the terminal devices in the edge computing field is excessively occupied by the conventional data compression technology.
Disclosure of Invention
The invention provides a data processing method, a data processing device, data processing equipment and a data processing medium, which can reduce the data transmission cost on the premise of not occupying the computing capacity of terminal equipment too much, can balance the average residual energy of each terminal equipment in a cluster, and further prolong the service life of a network. According to an aspect of the present invention, there is provided a data processing method, the method including:
acquiring average residual energy in the cluster; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located;
determining a target numerical value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head;
according to the target numerical value, sending observation data to the cluster head, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster;
wherein the cluster head is predetermined based on a low power consumption adaptive clustering hierarchical protocol.
According to another aspect of the present invention, there is provided a data processing apparatus comprising:
the average residual energy acquisition module is used for acquiring the average residual energy in the clusters; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located;
the target value determining module is used for determining a target value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head;
the observation data sending module is used for sending observation data to the cluster head according to the target numerical value so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster;
wherein the cluster head is predetermined based on a low power consumption adaptive cluster hierarchical protocol.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the data processing method according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a data processing method according to any one of the embodiments of the present invention when the computer instructions are executed.
The technical scheme of the embodiment of the application comprises the following steps: acquiring average residual energy in the cluster; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located; determining a target numerical value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head; according to the target numerical value, sending observation data to the cluster head, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster; wherein the cluster head is predetermined based on a low power consumption adaptive cluster hierarchical protocol. According to the technical scheme, whether each terminal device in the cluster participates in data acquisition or not is determined through the target numerical value, so that a part of terminal devices with more residual energy replace another part of terminal devices with lower residual energy in the cluster to participate in data acquisition, the condition that the residual energy difference value of each terminal device is too large is avoided as much as possible, the average residual energy of each terminal device in the cluster is balanced, the effect of prolonging the service life of a network is achieved, and in the data transmission process, the data transmission cost is reduced due to the reduction of the data transmission amount.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present application;
fig. 2 is a schematic diagram of a wireless sensor network according to an embodiment of the present application;
FIG. 3 is a flowchart of a data processing method according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device implementing a data processing method according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a data processing method, which is applicable to a case of performing data acquisition and data transmission on a terminal device according to an embodiment of the present application, and the method may be performed by a data processing apparatus, which may be implemented in a form of hardware and/or software, and the data processing apparatus may be configured in an electronic device (terminal device) with data processing capability. As shown in fig. 1, the method includes:
s110, acquiring average residual energy in the cluster; and the average residual energy in the cluster is the average value of the residual energy of all the terminal equipment in the cluster where the current terminal equipment is located.
According to the technical scheme, the data volume collected by the terminal equipment in the wireless sensor network can be reduced in the edge computing scene, the data transmission cost is reduced on the premise that the computing capacity of the terminal equipment is not occupied too much, the residual energy of each terminal equipment in a cluster can be balanced in the data collection and data transmission processes, and the service life of the network is further prolonged.
Specifically, each terminal device in the wireless sensor network is divided into at least one cluster in advance, each cluster determines a cluster head from the terminal devices in the cluster in advance, each terminal device in the cluster sends respective residual energy to the cluster head, and the cluster head determines the average residual energy in the cluster according to the residual energy of each terminal device in the cluster and the number of the terminal devices in the cluster, so that the embodiment of the application can acquire the average residual energy in the cluster from the cluster head; or the cluster head sends the average residual energy in the cluster to each terminal device in the cluster, and each terminal device acquires the average residual energy in the cluster from the corresponding storage medium. The cluster head is predetermined based on a Low power consumption Adaptive Clustering Hierarchy (LEACH) protocol.
In the embodiment of the application, each terminal device collects data, and the data received by the sink node (cloud end) is collected in one round of data collection. It should be noted that, if the cluster head recalculates the average remaining energy in the cluster after each round of data acquisition is completed, in this case, the average remaining energy in the cluster cannot be acquired when the first round participates in data acquisition, and therefore, before the first round participates in data acquisition, each terminal device in the cluster sends the remaining energy of the current terminal device to the cluster head, so that after the cluster head calculates the average remaining energy in the cluster, the average remaining energy in the cluster is fed back to each terminal device in the cluster.
An exemplary network architecture for a wireless sensor network is shown in fig. 2.
S120, determining a target numerical value according to the average residual energy in the cluster and the residual energy of the current terminal equipment; the target value can reflect the probability that the current terminal equipment sends observation data to the cluster head.
The residual energy of the current terminal equipment refers to the energy remaining after the current terminal equipment completes the previous round of data acquisition.
Specifically, as shown in fig. 2, since communication distances between each terminal device in the cluster and the cluster head may be different, communication energy consumption of each terminal device may be different, and if the probability of performing data acquisition on the terminal device with higher residual energy in the current round is increased, average residual energy of each terminal device in the cluster may be further balanced, thereby prolonging the network life.
Illustratively, if the current residual energy of the terminal device is higher than the average residual energy in the cluster, the target value may be adaptively determined, so that when the current terminal device determines whether to perform data acquisition according to the target value, the probability of performing data acquisition is greater than the probability when the current residual energy of the terminal device is lower than or equal to the average residual energy in the cluster.
In a possible embodiment, a calculation function may be preset, and a target value is obtained after the average residual energy in the cluster and the current terminal device residual energy are input to the calculation function.
According to the technical scheme, in the data acquisition process of each terminal device, part of the terminal devices with more residual energy replace the other part of the terminal devices with lower residual energy in the cluster to participate in data acquisition, the condition that the residual energy difference of each terminal device is too large is avoided as far as possible, the average residual energy of each terminal device in the cluster is balanced, and the effect of prolonging the service life of the network is achieved.
And S130, sending observation data to the cluster head according to the target numerical value, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster.
The observation data can reflect the collected data, the collected data is obtained when the current terminal carries out data collection, and the collected data can be subjected to adaptive processing to obtain the observation data. The integrated data includes all observation data in the cluster where the current device is located, and for example, the obtaining process of the integrated data may be: and weighting the observation data sent by each terminal device in the cluster by the cluster head, and converging the observation data into integrated data.
Specifically, whether data acquisition is carried out or not is determined according to the target numerical value, and if the data acquisition is carried out, observation data is determined according to the acquired data and sent to the cluster head.
It should be noted that, because the technical solution of the embodiment of the present application controls some devices in each terminal device to perform data acquisition, and the sink node cannot receive data of all terminal devices, the acquired data needs to be adaptively processed to obtain observation data, so that the sink node can restore data of all terminal devices according to the observation data of each terminal device. Illustratively, the observation data may be determined by a compressed sensing technique, and the sink node restores data of all terminal devices by the compressed sensing technique.
The technical scheme of the embodiment of the application comprises the following steps: acquiring average residual energy in the cluster; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located; determining a target numerical value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head; and sending observation data to the cluster head according to the target numerical value, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster. According to the technical scheme, whether each terminal device in the cluster participates in data acquisition or not is determined through the target numerical value, so that a part of terminal devices with more residual energy replace another part of terminal devices with lower residual energy in the cluster to participate in data acquisition, the condition that the residual energy difference value of each terminal device is too large is avoided as much as possible, the average residual energy of each terminal device in the cluster is balanced, the effect of prolonging the service life of a network is achieved, and in the data transmission process, the data transmission cost is reduced due to the reduction of the data transmission amount.
Example two
Fig. 3 is a flowchart of a data processing method according to a second embodiment of the present application, where the second embodiment of the present application is optimized based on the first embodiment.
As shown in fig. 3, the method of the embodiment of the present application specifically includes the following steps:
s210, acquiring average residual energy in the cluster; and the average residual energy in the cluster is the average value of the residual energy of all the terminal equipment of the cluster where the current terminal equipment is located.
And S220, determining the target numerical value according to the average residual energy in the cluster, the current terminal equipment residual energy, the random value and the residual energy influence factor.
Wherein the random value is a randomly generated numerical value. The residual energy influence factor may be determined according to actual conditions, which is not limited in the embodiment of the present application.
Specifically, the influence degree of the residual energy can be adjusted by the residual energy influence factor. Through the random value, the determined target value has randomness, so that terminal equipment with higher residual energy still possibly does not participate in the data acquisition of the current round.
In this embodiment of the application, optionally, determining the target value according to the average residual energy in the cluster, the current terminal device residual energy, the random value, and the residual energy impact factor includes: the target value may be determined by the following equation:
p＝rand()+α×(power j (i)-power avg (i))/power j (i)；
where p is the target value, rand () is the random value, α is the residual energy impact factor, power j (i) For the current terminal equipment remaining energy, power avg (i) The average remaining energy within the cluster.
And S230, if the target numerical value meets a preset condition, acquiring sampling data.
The preset condition may be determined according to an actual situation, which is not limited in the embodiment of the present application. Specifically, a preset condition (for example, a certain value range) may be preset, and if it is determined that the target value satisfies the preset condition, data acquisition may be performed to obtain sampling data.
In this embodiment of the application, optionally, if the target value meets a preset condition, acquiring sampling data includes: and if the target value is smaller than a first threshold value or larger than or equal to a second threshold value, acquiring the sampling data.
The first threshold and the second threshold may be determined according to actual conditions, which is not limited in the embodiments of the present application. Illustratively, the base sampling rate p is preset base The first threshold may be: p is a radical of formula base /2. The second threshold may be: 1-p base /2。
And S240, determining observation data according to the sampling data and the numerical value corresponding to the observation matrix.
Wherein each value corresponding to the observation matrix corresponds to a respective sampled value. It should be noted that, in the compressed sensing technology, the original data may be restored according to the observation matrix, the sparse representation base, and the compressed data, so that each terminal device in this embodiment of the present application may determine a value corresponding to the observation matrix in the current data acquisition, so that the sink node may extract the observation matrix from the integrated data, and further restore the original data.
In this embodiment of the application, optionally, determining the observation data according to the sampling data and the numerical value corresponding to the observation matrix includes: if the target value is smaller than a first threshold value, the value corresponding to the observation matrix is 1; if the target value is greater than or equal to a second threshold, the value corresponding to the observation matrix is-1; and determining observation data according to the sampling data and the numerical value corresponding to the observation matrix.
It should be understood that the present disclosure is only one specific example for determining the values corresponding to the observation matrix, and in the practical application, those skilled in the art can easily conceive of other schemes for determining the values corresponding to the observation matrix according to the present disclosure, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
In this embodiment, optionally, determining the observation data according to the sampling data and the numerical value corresponding to the observation matrix includes:
and multiplying the sampling data by the numerical value corresponding to the observation matrix to obtain the observation data.
Illustratively, the observed value may be expressed as:
u j (i)＝Φ ij x j (i)；
wherein u is j (i) To observe the value,. Phi ij Is a value corresponding to an observation matrix, x j (i) Is the sampled data.
S250, sending observation data to the cluster head so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster; wherein the cluster head is predetermined based on a low power consumption adaptive clustering hierarchical protocol.
In this embodiment of the application, optionally, observation data is sent to the cluster head according to the target value, so that after the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster, the method further includes: and sending new residual energy to the cluster head of the next round so that the cluster head of the next round determines new average residual energy according to the new residual energy of all the terminal equipment in the cluster, and sending the new average residual energy to each terminal equipment in the cluster to prepare for data acquisition of the next round.
For example, after the data acquisition of the current round is completed, the cluster head of the next round can be determined according to the LEACH protocol. It should be noted that, in the embodiment of the present application, the number of data acquisition rounds is not limited.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present application, where the apparatus is capable of executing a data processing method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus includes:
an average remaining energy obtaining module 310, configured to obtain an average remaining energy within a cluster; the average residual energy in the cluster is the average value of the residual energy of all the terminal equipment of the cluster where the current terminal equipment is located;
a target value determining module 320, configured to determine a target value according to the average residual energy in the cluster and the current terminal device residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head;
an observation data sending module 330, configured to send observation data to the cluster head according to the target value, so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster;
wherein the cluster head is predetermined based on a low power consumption adaptive cluster hierarchical protocol.
Optionally, the observation data sending module 330 includes:
the sampling data acquisition unit is used for acquiring sampling data if the target numerical value meets a preset condition;
and the observation value determining unit is used for determining observation data according to the sampling data and the value corresponding to the observation matrix.
Optionally, the sampling data obtaining unit includes:
a sampling data obtaining subunit, configured to obtain sampling data if the target value is smaller than a first threshold, or is greater than or equal to a second threshold;
an observed value determining unit comprising:
a first value determining subunit, configured to determine, if the target value is smaller than a first threshold, that a value corresponding to the observation matrix is 1;
a second value determining subunit for, if the target value is greater than or equal to a second threshold value, determining that a value corresponding to the observation matrix is-1;
and the observation value determining subunit is used for determining the observation data according to the sampling data and the value corresponding to the observation matrix.
Optionally, the observation value determining subunit is specifically configured to:
and multiplying the sampling data by the numerical value corresponding to the observation matrix to obtain the observation data.
Optionally, the target value determining module 320 includes:
and the target value determining unit is used for determining the target value according to the average residual energy in the cluster, the current terminal equipment residual energy, the random value and the residual energy influence factor.
Optionally, the target value determining unit includes: the target value may be determined by the following equation:
p＝rand()+α×(power j (i)-power avg (i))/power j (i)；
where p is the target value, rand () is the random value, α is the residual energy impact factor, power j (i) For the current terminal equipment residual energy, power avg (i) The average remaining energy within the cluster.
Optionally, the apparatus further comprises:
and the residual energy sending module is used for sending new residual energy to the next round of cluster heads so that the next round of cluster heads determine new average residual energy according to the new residual energy of all the terminal devices in the cluster and send the new average residual energy to each terminal device in the cluster to prepare for next round of data acquisition.
The data processing device provided by the embodiment of the application can execute the data processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a data processing method.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of data processing, comprising:
acquiring average residual energy in the cluster; the average residual energy in the cluster is the average value of the residual energy of all the terminal equipment of the cluster where the current terminal equipment is located;
determining a target value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head;
according to the target numerical value, sending observation data to the cluster head so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster;
wherein the cluster head is predetermined based on a low power consumption adaptive clustering hierarchical protocol.
2. The method of claim 1, wherein sending observation data to a cluster head based on the target value comprises:
if the target numerical value meets a preset condition, acquiring sampling data;
and determining observation data according to the sampling data and the numerical value corresponding to the observation matrix.
3. The method of claim 2, wherein obtaining the sample data if the target value satisfies a predetermined condition comprises:
if the target value is smaller than a first threshold value or larger than or equal to a second threshold value, acquiring sampling data;
determining observation data from the sampled data and values corresponding to an observation matrix, comprising:
if the target value is smaller than a first threshold value, the value corresponding to the observation matrix is 1;
if the target value is greater than or equal to a second threshold value, the value corresponding to the observation matrix is-1;
and determining observation data according to the sampling data and the numerical value corresponding to the observation matrix.
4. The method of claim 3, wherein determining observation data from the sampled data and values corresponding to an observation matrix comprises:
and multiplying the sampled data by the numerical value corresponding to the observation matrix to obtain the observation data.
5. The method of claim 1, wherein determining the target value based on the average remaining energy in the cluster and the current terminal device remaining energy comprises:
and determining the target numerical value according to the average residual energy in the cluster, the current terminal equipment residual energy, the random value and the residual energy influence factor.
6. The method of claim 5, wherein determining the target value according to the average residual energy within the cluster, the current terminal equipment residual energy, the random value and the residual energy impact factor comprises: the target value may be determined by the following equation:
p＝rand()+α×(power j (i)-power avg (i))/power j (i)；
where p is the target value, rand () is the random value, α is the residual energy impact factor, power j (i) For the current terminal equipment remaining energy, power avg (i) The average remaining energy within the cluster.
7. The method of claim 1, wherein the observation data is sent to the cluster head according to the target value, so that after the cluster head sends the integrated data to the aggregation node according to the observation data of each terminal device in the cluster, the method further comprises:
and sending new residual energy to the cluster head of the next round so that the cluster head of the next round determines new average residual energy according to the new residual energy of all the terminal equipment in the cluster, and sending the new average residual energy to each terminal equipment in the cluster to prepare for data acquisition of the next round.
8. A data processing apparatus, characterized by comprising:
the average residual energy acquisition module is used for acquiring the average residual energy in the cluster; the average residual energy in the cluster is the average value of the residual energy of all terminal equipment in the cluster where the current terminal equipment is located;
the target value determining module is used for determining a target value according to the average residual energy in the cluster and the current terminal equipment residual energy; the target numerical value can reflect the probability that the current terminal equipment sends observation data to the cluster head;
the observation data sending module is used for sending observation data to the cluster head according to the target numerical value so that the cluster head sends the integrated data to the sink node according to the observation data of each terminal device in the cluster;
wherein the cluster head is predetermined based on a low power consumption adaptive clustering hierarchical protocol.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the data processing method of any of claims 1-7 when executed.
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| CN202211333329.0A CN115802438A (en) | 2022-10-28 | 2022-10-28 | Data processing method, device, equipment and medium |
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| CN202211333329.0A CN115802438A (en) | 2022-10-28 | 2022-10-28 | Data processing method, device, equipment and medium |
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