Data compression method and device and computer equipment

Abstract

The invention relates to a data compression method and device and computer equipment. The method comprises the following steps: acquiring energy data of a node, environment data acquired by the node, and spatio-temporal information of the environment data; according to the spatio-temporal information, determining a spatio-temporal correlation coefficient of the environmental data; the spatio-temporal correlation coefficient is used for evaluating the redundancy degree of the environmental data; determining a dynamic energy parameter of the node according to the energy data; the dynamic energy parameter is used for representing the residual energy of the node; and compressing the environmental data according to the dynamic energy parameter and the spatial-temporal correlation coefficient. The data compression method comprehensively considers the energy state of the node and the spatial-temporal correlation of each data in the environmental data, dynamically adjusts the compression strategy of the node, and improves the energy utilization rate of the node and reduces the energy loss of the node while ensuring the effectiveness of the compressed data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data compression method, apparatus, and computer equipment. Background Art

[0002] With the rapid development of the Internet of Things and wireless sensor networks, low-power nodes are widely used in various fields such as data collection, environmental monitoring, and smart homes. However, current low-power nodes suffer from low energy utilization and low data compression efficiency when performing data compression and caching. Summary of the Invention

[0003] Based on this, it is necessary to provide a data compression method, device and computer equipment that can improve node energy utilization and compression efficiency in response to the above technical problems.

[0004] In a first aspect, the present application provides a data compression method, comprising:

[0005] Acquiring energy data of a node, environmental data collected by the node, and spatiotemporal information of the environmental data;

[0006] Determining a spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information; the spatiotemporal correlation coefficient is used to evaluate the redundancy of the environmental data;

[0007] Determining a dynamic energy parameter of the node according to the energy data; the dynamic energy parameter is used to represent the remaining energy of the node;

[0008] The environmental data is compressed according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

[0009] In one embodiment, the node includes a plurality of data collection points, the environmental data includes a plurality of groups of data, and each of the data collection points is used to collect a group of data;

[0010] Determining the spatiotemporal correlation coefficient of the environmental data according to the spatiotemporal information includes:

[0011] According to the spatiotemporal information, two sets of data in the environmental data that meet preset association conditions are determined as target data pairs;

[0012] Determining a correlation coefficient of the target data pair based on the covariance and standard deviation of the target data pair;

[0013] Determining a spatiotemporal weighting factor of the target data pair according to the correlation coefficient and the spatiotemporal information;

[0014] Determining the spatiotemporal correlation coefficient of the target data pair according to the spatiotemporal weighting factor and the correlation coefficient;

[0015] The spatiotemporal correlation coefficient of the environmental data is determined according to the spatiotemporal correlation coefficient of the target data pair.

[0016] In one embodiment, the spatiotemporal information includes spatial location information and timestamp information;

[0017] Determining the spatiotemporal weighting factor of the target data pair according to the correlation coefficient and the spatiotemporal information includes:

[0018] Acquiring the spatial distance of the target data pair according to the spatial position information of the target data pair;

[0019] Obtaining a collection time difference of the target data pair according to the timestamp information of the target data pair;

[0020] A spatiotemporal weighting factor of the target data pair is determined according to the correlation coefficient, the spatial distance, the acquisition time difference, a preset hyperparameter and a preset similarity attenuation parameter.

[0021] In one embodiment, determining the dynamic energy parameter of the node according to the energy data includes:

[0022] Obtaining the initial energy of the node at an initial moment;

[0023] Obtaining the energy consumed by the node from the initial moment to the current moment;

[0024] The remaining energy of the node at the current moment is obtained according to the initial energy and the consumed energy.

[0025] In one embodiment, the compressing the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient includes:

[0026] Determining a comprehensive weight according to the dynamic energy parameter, the spatiotemporal correlation coefficient and a preset weight coefficient;

[0027] According to the comprehensive weight, the compression strategy is adjusted to compress the environmental data.

[0028] In one embodiment, adjusting the compression strategy according to the comprehensive weight to compress the environmental data includes:

[0029] determining a target mapping function according to the spatiotemporal information and the dynamic energy parameter;

[0030] Determining a compression parameter according to the comprehensive weight and the target mapping function, wherein the compression parameter is used to characterize the compression strength of the compression strategy;

[0031] Based on the compression parameters, the compression strategy is adjusted to perform compression processing on the environmental data.

[0032] In one embodiment, determining a target mapping function according to the spatiotemporal information includes:

[0033] Acquire redundant information of the environmental data according to the spatiotemporal information;

[0034] determining a compression ratio according to the amount of redundant information and the total amount of the environmental data;

[0035] The target mapping function is determined from a plurality of preset mapping functions according to the compression ratio and the dynamic energy parameter.

[0036] In one embodiment, determining the compression parameter according to the comprehensive weight and the target mapping function includes:

[0037] When the comprehensive weight is less than the first preset weight, the compression parameter is the first preset value;

[0038] When the comprehensive weight is greater than or equal to the first preset weight and less than the second preset weight, the compression parameter is positively correlated with the comprehensive weight, and the compression parameter is greater than or equal to the first preset value;

[0039] When the comprehensive weight is greater than or equal to the second preset weight, the compression parameter is a second preset value, and the second preset value is greater than the first preset value.

[0040] In a second aspect, the present application further provides a data compression device, comprising:

[0041] A data acquisition module, configured to acquire energy data of nodes, environmental data collected by the nodes, and spatiotemporal information of the environmental data;

[0042] a spatiotemporal correlation analysis module, configured to determine a spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information; the spatiotemporal correlation coefficient is used to evaluate the redundancy of the environmental data;

[0043] an energy sensing module, configured to determine a dynamic energy parameter of the node based on the energy data; the dynamic energy parameter is used to represent the remaining energy of the node;

[0044] A compression processing module is used to compress the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

[0045] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the data compression method provided in any of the above embodiments when executing the computer program.

[0046] The above-mentioned data compression method, device and computer equipment obtain the energy data of the node, the environmental data collected by the node and the spatiotemporal information of the environmental data, determine the spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information, and determine the dynamic energy parameter of the node based on the energy data, wherein the spatiotemporal correlation coefficient reflects the redundancy of the environmental data, and the dynamic energy parameter represents the residual energy of the node. Furthermore, the environmental data is compressed based on the dynamic energy parameter and the spatiotemporal correlation coefficient. It can be understood that the residual energy of the node and the spatiotemporal correlation coefficient between each data in the environmental data collected by the node are used as references to dynamically adjust the compression strategy of the node. In the case of high data redundancy, effective information can be retained with appropriate energy consumption, thereby improving the energy utilization rate of the node. In the case of low node residual energy or low data redundancy, the processing burden of the node is reduced, while ensuring the efficiency of node data compression, the energy loss of the node is reduced, and the service life of the node is extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 1 is a flow chart of a data compression method according to an embodiment;

[0049] Figure 2 1 is a flow chart of determining the spatiotemporal correlation coefficient of environmental data based on spatiotemporal information in one embodiment;

[0050] Figure 3 FIG1 is a schematic diagram of a process for determining a dynamic energy parameter of a node based on energy data in one embodiment;

[0051] Figure 4 FIG1 is a flow chart of adjusting a compression strategy according to a comprehensive weight to compress environmental data in one embodiment;

[0052] Figure 5 is a flow chart of a data compression method according to another embodiment;

[0053] Figure 6is a structural block diagram of a data compression device in one embodiment;

[0054] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] The data compression method provided in the embodiments of the present application can be applied to sensor networks. A sensor network is a network composed of sensor nodes that can monitor, sense, and collect environmental information in the area where the nodes are deployed in real time, and can process and transmit this information externally. A sensor node can include one or more data collection points, which collect a set of data and send it to the sensor node. The sensor node can then perform operations such as compressing, storing, and forwarding the data collected by the data collection points.

[0057] In one embodiment, Figure 1 As shown, the data compression method of the present application includes steps 102 to 108.

[0058] Step 102: Obtain energy data of the node, environmental data collected by the node, and spatiotemporal information of the environmental data.

[0059] A node's energy data includes data such as its battery level, voltage, and current. The environmental data collected by a node refers to the environmental data collected by each data collection point within the node. The spatiotemporal information of the environmental data refers to its spatial location and timestamp information.

[0060] Step 104: Determine the spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information.

[0061] The spatiotemporal correlation coefficient is used to assess the redundancy of environmental data. In sensor networks, data with close timestamps and spatial proximity may be correlated and contain redundant information. Therefore, the spatiotemporal correlation coefficient of environmental data can be determined based on spatiotemporal information to quantify the redundancy of environmental data.

[0062] Step 106: Determine the dynamic energy parameters of the node according to the energy data.

[0063] The dynamic energy parameter is used to represent the remaining energy of the node.

[0064] Step 108: compress the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

[0065] Taking into account the dynamic energy parameters of the node and the spatiotemporal correlation coefficient of the environmental data, the environmental data is adaptively compressed. For example, when the node has sufficient remaining energy and the spatiotemporal correlation coefficient of the environmental data is high, a compression algorithm with high compression ratio and high computational intensity can be used to compress the environmental data; when the node has sufficient remaining energy but the spatiotemporal correlation coefficient of the environmental data is low, the environmental data can be moderately compressed to avoid wasting node energy; when the node has low remaining energy but the spatiotemporal correlation coefficient of the environmental data is high, the environmental data can be appropriately compressed, taking into account both node energy consumption and data compression efficiency; when the node has low remaining energy and the spatiotemporal correlation coefficient of the environmental data is also low, a compression algorithm with low computational intensity can be used to compress the environmental data, giving priority to ensuring node endurance, and the environmental data can be compressed by a higher-level node.

[0066] In an embodiment of the present application, the energy data of the node, the environmental data collected by the node, and the spatiotemporal information of the environmental data are obtained, the spatiotemporal correlation coefficient of the environmental data is determined based on the spatiotemporal information, and the dynamic energy parameters of the node are determined based on the energy data. Further, the environmental data is compressed based on the dynamic energy parameters and the spatiotemporal correlation coefficient. It can be understood that the remaining energy of the node and the spatiotemporal correlation coefficient between each data in the environmental data collected by the node are used as reference basis to dynamically adjust the compression strategy of the node. In the case of high data redundancy, valid information can be retained with appropriate energy consumption. In the case of low remaining energy of the node or low data redundancy, the processing burden of the node is reduced. While ensuring the efficiency of node data compression, the energy loss of the node is reduced and the service life of the node is extended.

[0067] In one embodiment, Figure 2 As shown, determining the spatiotemporal correlation coefficient of environmental data according to spatiotemporal information includes steps 202 to 210.

[0068] Step 202: Determine two sets of data in the environmental data that meet preset association conditions as target data pairs based on the spatiotemporal information.

[0069] The preset association condition refers to the proximity of timestamps or the proximity of spatial locations. For example, the preset association condition may be at least one of the difference in timestamps being less than a threshold Δt and the spatial distance being less than a threshold Δs.

[0070] Step 204: Determine the correlation coefficient of the target data pair based on the covariance and standard deviation of the target data pair.

[0071] Assume that the environmental data is , the two sets of data that meet the preset association conditions are recorded as target data pairs . For data and The covariance of and They are and The standard deviation of the target data Correlation coefficient for:

[0072]

[0073] Step 206: Determine the spatiotemporal weighting factor of the target data pair based on the correlation coefficient and the spatiotemporal information.

[0074] Spatiotemporal weighting factors can be used to combine metric data and The redundancy intensity between the two pairs of data, the spatiotemporal weighting factor is related to the acquisition time difference, spatial distance and correlation coefficient of the target data pair.

[0075] Specifically, according to the target data Spatial location information, obtain the spatial distance of the target data pair According to the target data Timestamp information, to obtain the acquisition time difference of the target data pair According to the correlation coefficient , spatial distance , collection time difference , as well as the preset hyperparameters and the preset similarity attenuation parameters, determine the target data pair The spatiotemporal weighting factor .

[0076] The preset hyperparameters include . , The specific value can be set according to the needs. The preset similarity attenuation parameters include the spatial similarity attenuation parameter and time similarity decay parameter . Used to control the effect of spatial distance on the similarity decay rate. Used to control the impact of acquisition time difference on the similarity decay rate.

[0077] Specifically, the target data pair can be calculated by formula (2): The spatiotemporal weighting factor :

[0078]

[0079] Step 208: Determine the spatiotemporal correlation coefficient of the target data pair based on the spatiotemporal weighting factor and the correlation coefficient.

[0080] Target data pair The spatiotemporal correlation coefficient It can be obtained from formula (3):

[0081]

[0082] Step 210: Determine the spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal correlation coefficient of the target data pair.

[0083] When there are multiple target data pairs in the environmental data, the average of the spatiotemporal correlation coefficients of the multiple target data pairs may be used as the spatiotemporal correlation coefficient of the environmental data.

[0084] In this embodiment, a spatiotemporal correlation analysis is performed on the environmental data based on spatiotemporal information, and the spatiotemporal correlation of the environmental data is quantified to obtain a spatiotemporal correlation coefficient, which can accurately extract redundant information in the environmental data and provide a reference basis for the redundancy level for the dynamic adjustment of subsequent compression strategies.

[0085] In one embodiment, Figure 3 As shown, determining the dynamic energy parameters of a node according to energy data includes steps 302 to 306.

[0086] Step 302: Obtain the initial energy of the node at the initial moment.

[0087] The initial time refers to the system time recorded when the node is started.

[0088] Step 304: Obtain the energy consumed by the node from the initial moment to the current moment.

[0089] The energy consumption of the node can be obtained by integrating the energy data of the node from the initial moment to the current moment.

[0090] Step 306: Obtain the remaining energy of the node at the current moment based on the initial energy and the consumed energy.

[0091] The remaining energy of the node at the current moment can be calculated according to formula (4): :

[0092]

[0093] in, represents the initial energy of the node, represents the energy consumption of the node, is the power consumption function of the node.

[0094] Since the collected node energy data is discretized during the actual processing, the above formula (4) can be discretized to obtain the residual energy of the node at the current moment.

[0095] In this embodiment, the residual energy of the node is obtained by collecting the energy data of the node in real time and performing integration and differential processing on the energy data, thereby realizing real-time detection of the node energy and providing a reference basis for the node energy for the subsequent adjustment of the compression strategy.

[0096] In one embodiment, environmental data is compressed based on dynamic energy parameters and spatiotemporal correlation coefficients, including determining a comprehensive weight based on the dynamic energy parameters, spatiotemporal correlation coefficients and preset weight coefficients, and adjusting a compression strategy based on the comprehensive weight to compress the environmental data.

[0097] The comprehensive weight is used to combine the dynamic energy parameter and the spatiotemporal correlation coefficient to achieve a balance between the node residual energy and the redundancy of the environmental data in the node data compression process.

[0098] The preset weight coefficients include the energy weight coefficient and the correlation weight coefficient , . and The value of can be reasonably set according to actual needs, and this embodiment does not impose any restrictions here.

[0099] Furthermore, the comprehensive weight W can be determined according to formula (5):

[0100]

[0101] in, is the spatiotemporal correlation coefficient of environmental data.

[0102] In this embodiment, the comprehensive weight is determined based on the dynamic energy parameter, the spatiotemporal correlation coefficient and the preset weight coefficient. The compression strategy is adjusted based on the comprehensive weight. When the node energy is sufficient, a high compression rate compression algorithm can be used to compress the environmental data. When the node energy is insufficient, the compression rate can be appropriately reduced, thereby achieving a balance between node energy loss and compression efficiency.

[0103] In one embodiment, Figure 4 As shown, the compression strategy is adjusted according to the comprehensive weight to compress the environmental data, including steps 402 to 406.

[0104] Step 402: Determine a target mapping function based on the spatiotemporal information and the dynamic energy parameters.

[0105] The target mapping function is used to map the comprehensive weight to the compression parameter. The target mapping function may be a piecewise function or a non-piecewise function, which may be a linear function, an exponential function, a step function, etc. on a continuous interval.

[0106] The target mapping function can be set in advance or dynamically adjusted according to the redundancy of the environmental data and the remaining energy of the node. For example, the amount of redundant information of the environmental data can be obtained based on the spatiotemporal information. , its redundant information I can be calculated by the following formula (6):

[0107]

[0108] According to the amount of redundant information I and the total amount of environmental data D, the compression ratio R is determined as:

[0109]

[0110] Then, based on the compression ratio and dynamic energy parameters, a target mapping function is determined from multiple preset mapping functions. The compression ratio reflects the compressibility potential of environmental data and is an important reference indicator for evaluating the rationality of a compression strategy. In this embodiment, the target mapping function is determined from multiple preset mapping functions based on the compression ratio and the node's residual energy. This allows for more precise adjustment of the compression strategy, achieving optimal compression of environmental data under varying node residual energy states.

[0111] Step 404: Determine compression parameters based on the comprehensive weight and the target mapping function.

[0112] The compression parameter is used to characterize the compression strength of the compression strategy. For example, in a dictionary-based compression algorithm (such as LZ77, LZ78, LZW, etc.), the compression parameter can be at least one of the dictionary size or the prediction step size.

[0113] Assume that the target mapping function is , compression parameters The relationship between it and the comprehensive weight W is:

[0114]

[0115] in, The default maximum compression parameter.

[0116] Step 406: Adjust the compression strategy based on the compression parameters to compress the environmental data.

[0117] In this embodiment, the comprehensive weight is mapped to the compression parameters of the compression strategy through the target mapping function, thereby achieving accurate adjustment of the compression strategy.

[0118] In one embodiment, the target mapping function is a piecewise function. The compression parameter is determined based on the comprehensive weight and the target mapping function, including: when the comprehensive weight is less than a first preset weight, the compression parameter is a first preset value; when the comprehensive weight is greater than or equal to the first preset weight and less than a second preset weight, the compression parameter is positively correlated with the comprehensive weight, and the compression parameter is greater than or equal to the first preset value; when the comprehensive weight is greater than or equal to a third preset weight, the compression parameter is a second preset value, and the second preset value is greater than the first preset value.

[0119] For example, the target mapping function may be:

[0120]

[0121] Among them, the first preset weight is 0.2, and the second preset weight is 0.7.

[0122] In order to better understand the data compression method of the present application, a more specific embodiment is used for description.

[0123] like Figure 5 As shown, the data compression method of the present application includes steps 502 to 522.

[0124] Step 502: Obtain energy data of the node, environmental data collected by the node, and spatiotemporal information of the environmental data.

[0125] Step 504: Determine two sets of data in the environmental data that meet preset association conditions as target data pairs based on the spatiotemporal information.

[0126] Step 506: Determine the correlation coefficient of the target data pair based on the covariance and standard deviation of the target data pair.

[0127] Step 508: Determine the spatiotemporal weighting factor of the target data pair based on the correlation coefficient and the spatiotemporal information.

[0128] Step 510: Determine the spatiotemporal correlation coefficient of the target data pair based on the spatiotemporal weighting factor and the correlation coefficient.

[0129] Step 512: Determine the spatiotemporal correlation coefficient of the environmental data by taking the average of the spatiotemporal correlation coefficients of the plurality of target data pairs.

[0130] Step 514: Determine the dynamic energy parameter of the node according to the energy data; the dynamic energy parameter is used to represent the remaining energy of the node.

[0131] Step 516: Determine the comprehensive weight according to the dynamic energy parameter, the spatiotemporal correlation coefficient and the preset weight coefficient.

[0132] Step 518: Determine a compression parameter based on the comprehensive weight and the target mapping function. The compression parameter is used to characterize the compression strength of the compression strategy.

[0133] Step 520: Adjust the compression strategy based on the compression parameters to compress the environmental data.

[0134] Step 522: Store the compressed environmental data.

[0135] It can be understood that when the data compression method of the present application compresses the environmental data of the node, it performs a spatiotemporal correlation analysis on the environmental data based on the spatiotemporal information of the environmental data, fully considers the spatiotemporal correlation of the environmental data, and accurately extracts the redundant information of the environmental data; the energy of the node is coupled with the spatiotemporal correlation of the environmental data, so that the node can flexibly adjust the compression strategy according to the current residual energy. When the data redundancy is high, it can retain valid information with appropriate energy consumption. When the node residual energy is low or the data redundancy is low, the processing burden of the node is reduced. While ensuring the compression efficiency of the node, the energy loss of the node is reduced and the service life of the node is extended.

[0136] It should be understood that, although the various steps in the flowcharts involved in the above embodiments are displayed in sequence according to the instructions shown in the diagram, these steps are not necessarily performed in sequence in this order. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0137] Based on the same inventive concept, embodiments of the present application further provide a data compression device for implementing the aforementioned data compression method. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations in one or more data compression device embodiments provided below can be found in the above-described limitations on the data compression method and will not be further elaborated here.

[0138] In an exemplary embodiment, Figure 6 As shown, a data compression device is provided, including: a data acquisition module 602, a spatiotemporal correlation analysis module 604, an energy sensing module 606 and a compression processing module 608, wherein:

[0139] The data acquisition module 602 is used to obtain the energy data of the node, the environmental data collected by the node, and the spatiotemporal information of the environmental data;

[0140] The spatiotemporal correlation analysis module 604 is used to determine the spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information; the spatiotemporal correlation coefficient is used to evaluate the redundancy of the environmental data;

[0141] The energy sensing module 606 is used to determine the dynamic energy parameter of the node based on the energy data; the dynamic energy parameter is used to represent the remaining energy of the node;

[0142] The compression processing module 608 is used to compress the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

[0143] In one embodiment, the spatiotemporal correlation analysis module is also used to determine two groups of data in the environmental data that meet preset correlation conditions as target data pairs based on spatiotemporal information; determine the correlation coefficient of the target data pair based on the covariance and standard deviation of the target data pair; determine the spatiotemporal weighting factor of the target data pair based on the correlation coefficient and spatiotemporal information; determine the spatiotemporal correlation coefficient of the target data pair based on the spatiotemporal weighting factor and the correlation coefficient; and determine the spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal correlation coefficient of the target data pair.

[0144] In one embodiment, the spatiotemporal correlation analysis module is also used to obtain the spatial distance of the target data pair based on the spatial position information of the target data pair; obtain the acquisition time difference of the target data pair based on the timestamp information of the target data pair; and determine the spatiotemporal weighting factor of the target data pair based on the correlation coefficient, spatial distance, acquisition time difference, preset hyperparameters and preset similarity attenuation parameters.

[0145] In one embodiment, the energy sensing module is also used to obtain the initial energy of the node at the initial moment; obtain the consumed energy of the node from the initial moment to the current moment; and obtain the remaining energy of the node at the current moment based on the initial energy and the consumed energy.

[0146] In one embodiment, the compression processing module is further used to determine a comprehensive weight based on the dynamic energy parameter, the spatiotemporal correlation coefficient and the preset weight coefficient; and adjust the compression strategy based on the comprehensive weight to compress the environmental data.

[0147] In one embodiment, the compression processing module is also used to determine the target mapping function based on the spatiotemporal information and dynamic energy parameters; determine the compression parameters based on the comprehensive weight and the target mapping function, and the compression parameters are used to characterize the compression strength of the compression strategy; based on the compression parameters, adjust the compression strategy to compress the environmental data.

[0148] In one embodiment, the compression processing module is also used to obtain the redundant information amount of the environmental data based on the spatiotemporal information; determine the compression ratio based on the redundant information amount and the total amount of the environmental data; and determine the target mapping function from multiple preset mapping functions based on the compression ratio and dynamic energy parameters.

[0149] In one embodiment, the compression processing module is also used to, when the comprehensive weight is less than the first preset weight, the compression parameter is a first preset value; when the comprehensive weight is greater than or equal to the first preset weight and less than the second preset weight, the compression parameter is positively correlated with the comprehensive weight, and the compression parameter is greater than or equal to the first preset value; when the comprehensive weight is greater than or equal to the second preset weight, the compression parameter is a second preset value, and the second preset value is greater than the first preset value.

[0150] Each module in the above-mentioned data compression device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0151] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store compressed data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data compression method is implemented.

[0152] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0153] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the data compression method provided in any of the following embodiments when executing the computer program.

[0154] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the data compression method provided in any of the above embodiments is implemented.

[0155] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the data compression method provided in any of the above embodiments is implemented.

[0156] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0157] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A data compression method, characterized in that: The method comprises: Acquiring energy data of a node, environmental data collected by the node, and spatiotemporal information of the environmental data; Determining a spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information; the spatiotemporal correlation coefficient is used to evaluate the redundancy of the environmental data; Determining a dynamic energy parameter of the node according to the energy data; the dynamic energy parameter is used to represent the remaining energy of the node; The environmental data is compressed according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

2. The method according to claim 1, characterized in that The node includes a plurality of data collection points, the environmental data includes a plurality of groups of data, and each of the data collection points is used to collect a group of data; Determining the spatiotemporal correlation coefficient of the environmental data according to the spatiotemporal information includes: According to the spatiotemporal information, two sets of data in the environmental data that meet preset association conditions are determined as target data pairs; Determining a correlation coefficient of the target data pair based on the covariance and standard deviation of the target data pair; Determining a spatiotemporal weighting factor of the target data pair according to the correlation coefficient and the spatiotemporal information; Determining the spatiotemporal correlation coefficient of the target data pair according to the spatiotemporal weighting factor and the correlation coefficient; The spatiotemporal correlation coefficient of the environmental data is determined according to the spatiotemporal correlation coefficient of the target data pair.

3. The method according to claim 2, characterized in that The spatiotemporal information includes spatial location information and timestamp information; Determining the spatiotemporal weighting factor of the target data pair according to the correlation coefficient and the spatiotemporal information includes: Acquiring the spatial distance of the target data pair according to the spatial position information of the target data pair; Obtaining a collection time difference of the target data pair according to the timestamp information of the target data pair; A spatiotemporal weighting factor of the target data pair is determined according to the correlation coefficient, the spatial distance, the acquisition time difference, a preset hyperparameter and a preset similarity attenuation parameter.

4. The method according to claim 1, wherein Determining the dynamic energy parameter of the node according to the energy data includes: Obtaining the initial energy of the node at an initial moment; Obtaining the energy consumed by the node from the initial moment to the current moment; The remaining energy of the node at the current moment is obtained according to the initial energy and the consumed energy.

5. The method according to any one of claims 1 to 4, characterized in that The compressing the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient includes: Determining a comprehensive weight according to the dynamic energy parameter, the spatiotemporal correlation coefficient and a preset weight coefficient; According to the comprehensive weight, the compression strategy is adjusted to compress the environmental data.

6. The method according to claim 5, characterized in that The adjusting the compression strategy according to the comprehensive weight to compress the environmental data includes: determining a target mapping function according to the spatiotemporal information and the dynamic energy parameter; Determining a compression parameter according to the comprehensive weight and the target mapping function, wherein the compression parameter is used to characterize the compression strength of the compression strategy; Based on the compression parameters, the compression strategy is adjusted to perform compression processing on the environmental data.

7. The method according to claim 6, characterized in that The determining of the target mapping function according to the spatiotemporal information includes: Acquire redundant information of the environmental data according to the spatiotemporal information; determining a compression ratio according to the amount of redundant information and the total amount of the environmental data; The target mapping function is determined from a plurality of preset mapping functions according to the compression ratio and the dynamic energy parameter.

8. The method according to claim 6, characterized in that The determining of the compression parameter according to the comprehensive weight and the target mapping function comprises: When the comprehensive weight is less than the first preset weight, the compression parameter is the first preset value; When the comprehensive weight is greater than or equal to the first preset weight and less than the second preset weight, the compression parameter is positively correlated with the comprehensive weight, and the compression parameter is greater than or equal to the first preset value; When the comprehensive weight is greater than or equal to the second preset weight, the compression parameter is a second preset value, and the second preset value is greater than the first preset value.

9. A data compression device, characterized in that: The device comprises: A data acquisition module, configured to acquire energy data of nodes, environmental data collected by the nodes, and spatiotemporal information of the environmental data; a spatiotemporal correlation analysis module, configured to determine a spatiotemporal correlation coefficient of the environmental data based on the spatiotemporal information; the spatiotemporal correlation coefficient is used to evaluate the redundancy of the environmental data; an energy sensing module, configured to determine a dynamic energy parameter of the node based on the energy data; the dynamic energy parameter is used to represent the remaining energy of the node; A compression processing module is used to compress the environmental data according to the dynamic energy parameter and the spatiotemporal correlation coefficient.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images