Data decomposition method and device, equipment and medium

Abstract

The invention discloses a data decomposition method and device, equipment and a medium, and relates to the technical field of data processing. The method comprises the steps of obtaining to-be-decomposed data, the number of river horse individuals in a river horse population and a river horse population iteration cut-off condition; for any river horse individual, executing a fitness value determination step to obtain a current individual fitness value of the river horse individual; determining the current relative speed change rate of the individual river horse according to the current individual fitness value of the individual river horse and the number of times of repeatedly executing the fitness value determination step; according to the current relative speed change rate of the individual river horse, the initial position of the individual river horse is updated, and the updated position of the individual river horse is obtained; updating the initial position of the individual river horse into the updated position of the individual river horse, and repeatedly executing the fitness value determination step until the iteration cut-off condition of the river horse population is reached; and outputting decomposition result data corresponding to the optimal current individual fitness value in the latest obtained current individual fitness values of the river horse individuals.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and more particularly, to a data decomposition method, apparatus, device, and medium. Background Art

[0002] Currently, when decomposing data, the Variational Mode Decomposition (VMD) algorithm is usually used. Among them, the VMD algorithm is an adaptive signal decomposition method used to decompose a complex signal into multiple Intrinsic Mode Functions (IMFs) with specific frequency bandwidths. IMFs are also called sub-modal components. The mode number k and the penalty factor alpha are two key parameters of the VMD algorithm, which directly affect the accuracy of signal decomposition.

[0003] Currently, the values of k and alpha are usually set according to experience, which results in a large difference between the reconstructed signal corresponding to the k IMFs decomposed by the VMD for a complex input signal and the input signal. On this basis, when using the historical pose sequence for pose prediction, in order to reduce the complexity of the historical pose sequence, first, the VMD algorithm is used to decompose the historical pose sequence into k IMFs, and then the decomposed k IMFs are used for pose prediction, and the obtained prediction result will have an inaccurate problem.

[0004] Therefore, how to determine the values of k and alpha of the VMD algorithm to achieve accurate decomposition of data has become a technical problem to be solved urgently. Summary of the Invention

[0005] An object of the present application is to provide a new technical solution for data decomposition.

[0006] According to a first aspect of the present application, there is provided a data decomposition method, including:

[0007] Obtain the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cut-off condition of the hippopotamus population;

[0008] For any hippopotamus individual, perform a fitness value determination step to obtain the current individual fitness value of the hippopotamus individual. The fitness value determination step includes: determining a data decomposition algorithm according to the initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, and determining the current individual fitness value of the hippopotamus individual according to the decomposition result data. The initial position of the hippopotamus individual is used to represent the parameter value of the data decomposition algorithm;

[0009] Determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeating the execution of the fitness value determination step;

[0010] Update the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual;

[0011] Update the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeat the execution of the fitness value determination step until the hippopotamus population iteration cutoff condition is reached;

[0012] Output the decomposition result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individual.

[0013] Optionally, the hippopotamus population iteration cutoff condition includes:

[0014] Repeat the execution of the fitness value determination step until the hippopotamus population iteration number is reached;

[0015] Or, the error between the reconstructed data corresponding to the decomposition result data and the data to be decomposed is less than a preset error.

[0016] Optionally, the determining the current individual fitness value of the hippopotamus individual according to the decomposition result data includes:

[0017] Determine the reconstructed data according to the decomposition result data;

[0018] Determine the Euclidean distance between the reconstructed data and the data to be decomposed as the current individual fitness value of the hippopotamus individual;

[0019] Or, determine the sum of the fuzzy entropies corresponding to the decomposition result data according to the decomposition result data, and determine the sum of the fuzzy entropies as the current individual fitness value of the hippopotamus individual.

[0020] Optionally, before determining the data decomposition algorithm according to the initial position of the hippopotamus individual, the method further includes:

[0021] Determine the random mapping value of the hippopotamus individual according to the random mapping;

[0022] Determine the chaotic mapping value of the hippopotamus individual according to the random mapping value and the chaotic mapping;

[0023] Determine the initial position of the hippopotamus individual according to the chaotic mapping value of the hippopotamus individual and the initial position constraint condition of the hippopotamus individual.

[0024] Optionally, determining the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeating the fitness value determination step includes:

[0025] When the number of times of repeating the fitness value determination step is 0, determining that the current relative speed change rate of the hippopotamus individual is infinite;

[0026] When the number of times of repeating the fitness value determination step is greater than 0, determining the current optimal fitness value according to the current individual fitness value of each hippopotamus individual;

[0027] Determining the historical optimal fitness value according to the historical fitness value obtained by each hippopotamus individual in the previous execution of the fitness value determination step;

[0028] Obtaining the historical individual fitness value obtained by the hippopotamus individual in the previous execution of the fitness value determination step;

[0029] Determining the current relative speed change rate of the hippopotamus individual according to the historical individual fitness value, the current individual fitness value, the historical optimal fitness value, and the current optimal fitness value.

[0030] Optionally, updating the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual includes:

[0031] When the current relative speed change rate of the hippopotamus individual is greater than the preset change rate, updating the initial position of the hippopotamus individual according to the position update method in the first exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual;

[0032] When the current relative speed change rate of the hippopotamus individual is less than or equal to the preset change rate, updating the initial position of the hippopotamus individual according to the position update method in the second exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual;

[0033] For any hippopotamus individual, updating the updated intermediate position of the hippopotamus individual according to the position update method in the exploitation stage of the hippopotamus optimization algorithm to obtain the updated position of the hippopotamus individual.

[0034] Optionally, updating the initial position of the hippopotamus individual according to the position update formula in the first exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual includes:

[0035] Determining the adaptive weight of the hippopotamus individual according to the current individual fitness value of each hippopotamus individual;

[0036] Update the position update formula in the first exploration stage of the hippopotamus optimization algorithm according to the adaptive weight to obtain the updated position update method in the first exploration stage;

[0037] Update the initial position of the hippopotamus individual and the updated intermediate position of the hippopotamus individual according to the updated position update method in the first exploration stage.

[0038] According to a second aspect of the present application, there is provided a data decomposition device, including:

[0039] An acquisition module, configured to acquire data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cut-off condition of the hippopotamus population;

[0040] An execution module, configured to, for any hippopotamus individual, execute a fitness value determination step to obtain the current individual fitness value of the hippopotamus individual. The fitness value determination step includes: determining a data decomposition algorithm according to the initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, and determining the current individual fitness value of the hippopotamus individual according to the decomposition result data. The initial position of the hippopotamus individual is used to represent the parameter value of the data decomposition algorithm;

[0041] A determination module, configured to determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeating the execution of the fitness value determination step;

[0042] An update module, configured to update the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual;

[0043] An iteration module, configured to update the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeat the execution of the fitness value determination step until the iteration cut-off condition of the hippopotamus population is reached;

[0044] An output module, configured to output the decomposition result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individual.

[0045] According to a third aspect of the present application, there is provided an electronic device, where the electronic device includes the device described in the second aspect;

[0046] Alternatively, it includes a memory and a processor. The memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method described in any one of the first aspects.

[0047] According to a fourth aspect of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of the first aspects.

[0048] The present application provides a data decomposition method, including: obtaining data to be decomposed, the number of hippopotamus individuals in a hippopotamus population, and the iteration cutoff condition of the hippopotamus population; for any hippopotamus individual, performing a fitness value determination step to obtain the current individual fitness value of the hippopotamus individual, and the fitness value determination step includes: determining a data decomposition algorithm according to the initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, determining the current individual fitness value of the hippopotamus individual according to the decomposition result data, and the initial position of the hippopotamus individual is used to represent the parameter value of the data decomposition algorithm; determining the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeatedly performing the fitness value determination step; updating the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual; updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeatedly performing the fitness value determination step until the iteration cutoff condition of the hippopotamus population is reached; outputting the decomposition result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individuals. When the data decomposition algorithm is the VMD algorithm, the data decomposition method provided by the present application can determine the optimal k value and alpha value in the VMD algorithm through an improved hippopotamus algorithm, thereby realizing accurate decomposition of the data to be decomposed.

[0049] Other features and advantages of the present application will become clear from the following detailed description of exemplary embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0051] Figure 1 is a block diagram of the hardware configuration of an electronic device for implementing the data decomposition method according to an embodiment of the present application Figure 1 ;

[0052] Figure 2 is a schematic flowchart of a data decomposition method according to an embodiment of the present application;

[0053] Figure 3 is a schematic diagram of the distribution structure of a random mapping and a chaotic mapping according to an embodiment of the present application;

[0054] Figure 4It is a schematic diagram of the values of the tanh function when a takes different values according to an embodiment of the present application;

[0055] Figure 5 It is a schematic structural diagram of a data decomposition device according to an embodiment of the present application;

[0056] Figure 6 It is a block diagram of the hardware configuration of an electronic device for implementing a data decomposition method according to an embodiment of the present application Figure 2 。 Detailed implementation manners

[0057] Now, various exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and values set forth in these embodiments do not limit the scope of the present application.

[0058] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way serves as a limitation on the present application, its application, or its use.

[0059] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and devices should be regarded as part of the specification.

[0060] In all the examples shown and discussed here, any specific values should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0061] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0062] Figure 1 It is a block diagram of the hardware configuration of an electronic device for implementing a data decomposition method according to an embodiment of the present application.

[0063] The electronic device 1000 can be a terminal or a server. Further, the terminal can be a portable computer, a tablet computer, a handheld computer, etc. The server can be a cloud server, etc.

[0064] The electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and so on. Among them, the processor 1100 may be a central processing unit CPU, a microprocessor MCU, etc. The memory 1200 includes, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 1300 includes, for example, a USB interface, a headphone interface, etc. The communication device 1400 can perform wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display screen, a touch display screen, etc. The input device 1600 may include, for example, a touch screen, a keyboard, etc. The user can input / output voice information through the speaker 1700 and the microphone 1800.

[0065] Although multiple devices are shown for the electronic device 1000 in Figure 1 however, this application may only relate to some of the devices, for example, the electronic device 1000 only relates to the memory 1200 and the processor 1100.

[0066] In the embodiments applied to this application, the memory 1200 of the electronic device 1000 is used to store instructions for controlling the processor 1100 to execute the data decomposition method provided in the embodiments of this application.

[0067] In the above description, those skilled in the art can design instructions according to the solutions disclosed in this application. How the instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0068] This application provides a data decomposition method, which is applied to an electronic device as shown in Figure 1 As shown in Figure 2 As shown, the data decomposition method includes the following steps S2100 to step S2600.

[0069] Step S2100, obtain the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration termination condition of the hippopotamus population.

[0070] In the embodiments of this application, the data to be decomposed is usually a sequence. In one example, the data to be decomposed is a pose sequence X = (x1, x2,... x n ) composed of n pose data of a head-mounted device arranged in time series.

[0071] The number of hippopotamus individuals is the number of candidate solutions in the search space. In this embodiment, the number of hippopotamus individuals is the number of groups of parameter values in the data decomposition algorithm. In one example, the data decomposition algorithm is the VMD algorithm. Based on this, the number of hippopotamus individuals is the number of groups of the k value and the alpha value in the VMD algorithm.

[0072] The iteration termination condition of the hippopotamus population represents the convergence condition for updating the positions of hippopotamus individuals.

[0073] Step S2200: For any hippopotamus individual, perform the fitness value determination step to obtain the current individual fitness value of the hippopotamus individual.

[0074] Among them, the fitness value determination step includes the following steps S2210 to S2212.

[0075] Step S2210: Determine the data decomposition algorithm according to the initial position of the hippopotamus individual, where the initial position of the hippopotamus individual is used to represent the parameter values of the data decomposition algorithm.

[0076] Taking the VMD algorithm as an example of the data decomposition algorithm, the initial position of the hippopotamus individual is the value of k and alpha in the VMD algorithm in the initial state. The specific implementation of the above step S2210 is: set both k and alpha in the VMD algorithm to the values in the initial state to obtain the VMD algorithm.

[0077] It should be noted that when the above step S2210 is executed for the first time, the parameter values of the data decomposition algorithm represented by the initial position of the hippopotamus individual are specifically the parameter values of the data decomposition algorithm in the initial state, that is, an initial parameter value.

[0078] In an embodiment of the present application, the initial position of the hippopotamus individual can be determined by means of random mapping as in the traditional hippopotamus optimization algorithm.

[0079] Due to the large randomness of random mapping, the random values determined by random mapping are unevenly distributed. This further leads to the problem of unstable optimization convergence speed in the subsequent traditional hippopotamus optimization algorithm. Therefore, in an embodiment of the present application, the data decomposition method provided in the present application further includes the steps of determining the initial position of the hippopotamus individual shown in the following steps S2210-1 to S2210-3 before the above step S2210.

[0080] Step S2210-1: Determine the random mapping value of the hippopotamus individual according to random mapping.

[0081] Step S2210-2: Determine the chaotic mapping value of the hippopotamus individual according to the random mapping value and chaotic mapping.

[0082] In this embodiment, for any hippopotamus individual, first determine the random mapping value corresponding to the hippopotamus individual through random mapping. Further, through chaotic mapping, map out the chaotic mapping value corresponding to the random mapping value.

[0083] In one embodiment of the present application, the chaotic mapping can specifically be a Cubic chaotic mapping or a Tent chaotic mapping.

[0084] Taking the chaotic mapping in step S2210-2 as the Tent mapping as an example, the chaotic mapping can be represented by the following formula one.

[0085]

[0086] Wherein, represents the random mapping value of the i-th hippopotamus individual, represents the chaotic mapping value of the i-th hippopotamus individual, μ represents the control parameter in the Tent mapping, and 0 < μ < 1. When μ = 1 / 2, the result of the Tent mapping is uniformly distributed.

[0087] Step S2210-3: Determine the initial position of the hippopotamus individual according to the chaotic mapping value of the hippopotamus individual and the initial position constraint condition of the hippopotamus individual.

[0088] Among them, the initial position constraint condition of the hippopotamus individual is used to represent the upper limit value and the lower limit value of the initial position of the hippopotamus individual.

[0089] Taking the data decomposition algorithm as the VMD algorithm and the initial position of the hippopotamus individual as the initial values of k and alpha in the VMD algorithm as an example, the initial position constraint condition of the hippopotamus individual is used to represent the upper limit value and the lower limit value of k, and the upper limit value and the lower limit value of alpha.

[0090] In the traditional hippopotamus optimization algorithm, the initial position of the hippopotamus individual is determined according to the following formula two.

[0091] X(:,i) = X min (i) + r1 × (X max (i) - X min (i)) (Formula two)

[0092] Wherein, X(:,i) represents the initial position of the i-th hippopotamus individual, r1 is the random mapping value of the i-th hippopotamus individual, X max (i) represents the upper limit value of the i-th hippopotamus individual, X min (i) represents the lower limit value of the i-th hippopotamus individual, X max (i) and X min (i) can be set according to experience.

[0093] In this embodiment, the specific implementation of the above step S2210-3 is to replace r1 in the above formula two with the chaotic mapping value of the i-th hippopotamus individual.

[0094] Taking the chaotic mapping as the Tent mapping as an example, as Figure 3As shown, the chaotic mapping values obtained based on chaotic mapping are more uniformly distributed than the random mapping values obtained by random mapping. On this basis, the initial position distribution of the hippopotamus individuals obtained through the above steps S2210-1 to S2210-3 is more uniform, which can increase the exploration difficulty and reduce the convergence speed.

[0095] Step S2211: Determine the decomposed result data for the data to be decomposed according to the data decomposition algorithm.

[0096] In this embodiment, after obtaining the data decomposition algorithm based on the above step S2210, the data to be decomposed is input into the data decomposition algorithm, and the data decomposition result is output by the data decomposition algorithm. The data decomposition result specifically includes k sub-modal components.

[0097] Step S2212: Determine the current individual fitness value of the hippopotamus individual according to the decomposed result data.

[0098] In this embodiment, the above step S2210 can be specifically implemented in one of the following two ways.

[0099] Method 1: The above step S2212 is specifically implemented through the following steps S2212-1 and S2212-2.

[0100] Step S2212-1: Determine the reconstructed data according to the decomposed result data.

[0101] In this embodiment, by adding the k sub-modal components included in the decomposed result data, the reconstructed data can be obtained. Taking the data to be decomposed as X=(x1, x2,...x n ) as an example, the reconstructed data can be expressed as

[0102]

[0103] Step S2212-2: Determine the Euclidean distance between the reconstructed data and the data to be decomposed as the current individual fitness value of the hippopotamus individual.

[0104] In this embodiment, the above step S2212-2 can be specifically implemented through the following formula three.

[0105]

[0106] Among them, represents the Euclidean distance between the reconstructed data and the data to be decomposed.

[0107] Method 2: The above step S2212 is specifically implemented through the following step S2212-3.

[0108] Step S2212-3: Determine the sum of the fuzzy entropies corresponding to the decomposition result data based on the decomposition result data, and determine the sum of the fuzzy entropies as the current individual fitness value of the hippopotamus individual.

[0109] In this embodiment, before determining the sum of the fuzzy entropies corresponding to the decomposition result data, first calculate the fuzzy entropy of each sub-modal component in the decomposition result data. Then, sum the fuzzy entropies of all sub-modal components to obtain the sum of the fuzzy entropies.

[0110] Among them, the steps of calculating the fuzzy entropy of each sub-modal component in the decomposition result data successively include the steps of phase space reconstruction, distance calculation, probability calculation, and fuzzy entropy calculation, which are specifically as follows.

[0111] Perform phase space reconstruction of each sub-modal component through the following formula four.

[0112]

[0113] Among them, I is the index value, I,j = 1,2,…,N-(m-1)τ; N is the length of the corresponding sub-modal component u k (t); τ represents the delay time, the time interval used to construct the phase space vector; m identifies the embedding dimension, which is used to represent the dimension of the reconstructed phase space, and m generally takes a value of 2.

[0114] The above distance calculation can be performed by calculating the Euclidean distance or Manhattan distance, etc. Taking the Euclidean distance as an example, perform distance calculation through the following formula five.

[0115]

[0116] Among them, represents the phase space reconstruction result of other sub-modal components, can also be expressed as can also be expressed as

[0117] Combined with the above content, count the number of the phase space reconstruction results of the sub-modal components with distance and then calculate the probability through the following formula six

[0118]

[0119] Among them, r represents the width of the boundary of the fuzzy function. When r is too large, a lot of statistical information will be lost. When r is too small, the effect of the estimated statistical characteristics is not ideal, and the sensitivity to the result noise will increase. r usually takes a certain proportion of the standard deviation of the signal (such as 0.1-0.25 times).

[0120] The fuzzy entropy H of the sub-modal component is calculated by the following formula seven. k Calculate.

[0121]

[0122] Of course, other methods can also be used to calculate the fuzzy entropy of each sub-modal component. This application does not make any limitations in this regard.

[0123] Step S2300: Determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeating the fitness value determination step.

[0124] In an embodiment of the present application, the above step S2300 is specifically implemented by the following steps S2310 to S2314.

[0125] Step S2310: When the number of times of repeating the fitness value determination step is 0, determine that the current relative speed change rate of the hippopotamus individual is infinite.

[0126] In this embodiment, when the above step S2200 is executed for the first time, the number of times of repeating the fitness value determination step is 0. When the above step S2200 is executed for the second time, the number of times of repeating the fitness value determination step is 1. And so on, the number of times of repeating the fitness value determination step can be determined.

[0127] When the number of times of repeating the fitness value determination step is 0, the current relative speed change rate of the hippopotamus individual is determined based on the above step S2310. When the number of times of repeating the fitness value determination step is greater than 0, the current relative change rate of the hippopotamus individual is determined based on the following steps S2311 to S2314.

[0128] Step S2311: When the number of times of repeating the fitness value determination step is greater than 0, determine the current optimal fitness value according to the current individual fitness value of each hippopotamus individual.

[0129] In this embodiment, the current optimal fitness value is selected from the current individual fitness values of all hippopotamus individuals obtained after the above step S2200 is currently executed for any hippopotamus individual.

[0130] In an example, when the Euclidean distance between the reconstructed data and the data to be decomposed is determined as the current individual fitness value of the hippopotamus individual in Method 1, the smallest current individual fitness value is the current optimal fitness value. And, when the sum of the fuzzy entropies is determined as the current individual fitness value of the hippopotamus individual in Method 2, the smallest current individual fitness value is the current optimal fitness value.

[0131] Step S2312: Determine the historical optimal fitness value according to the historical fitness values obtained for each hippopotamus individual in the previous execution of the fitness value determination step.

[0132] Select the historical optimal fitness value from the current individual fitness values of all hippopotamus individuals obtained after the previous execution of the above Step S2200 for any one hippopotamus individual.

[0133] Step S2313: Obtain the historical individual fitness value obtained for the hippopotamus individual in the previous execution of the fitness value determination step.

[0134] For any one hippopotamus individual, determine the current individual fitness value obtained after the previous execution of the above Step S2200 for this hippopotamus individual as the historical individual fitness value of this hippopotamus individual.

[0135] Step S2314: Determine the current relative speed change rate of the hippopotamus individual according to the historical individual fitness value, the current individual fitness value, the historical optimal fitness value, and the current optimal fitness value.

[0136] Based on the above Steps S2310 to S2314, in this embodiment, the above Step S2300 can be specifically implemented by the following Formula VIII.

[0137]

[0138] Where t represents the current number of repetitions of the above Step S2200, and the maximum value of t is the number of iterations of the hippopotamus population; represents the current relative speed change rate of the i-th hippopotamus individual; f(·) represents the preset fitness function; B t represents the current optimal fitness value; B t-1 represents the historical optimal fitness value; represents the current individual fitness value of the i-th hippopotamus individual, represents the historical individual fitness value of the i-th hippopotamus individual.

[0139] Based on the above Steps S2310 to S2314, the present application provides a method for determining the current relative speed change rate of a hippopotamus individual.

[0140] Step S2400: Update the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual.

[0141] In one embodiment of the present application, the initial position of the hippopotamus individual can be updated according to the specific value of the current relative speed change rate. For example, when the current relative speed change rate indicates a large change amplitude, the initial position of the hippopotamus individual is updated by means of a large-scale position adjustment. When the current relative speed change rate indicates a small change amplitude, the initial position of the hippopotamus individual is updated by means of a small-scale position adjustment.

[0142] Compared with the traditional hippopotamus optimization algorithm, in the data decomposition method provided by the present application, the positions are not updated in groups according to the order of hippopotamus individuals as in the traditional hippopotamus algorithm, but the positions are updated depending on the current relative speed change rate of each hippopotamus individual. On this basis, the problem that it is difficult to find the optimal solution when using the traditional hippopotamus optimization algorithm can be avoided.

[0143] Therefore, the present application improves the traditional hippopotamus optimization algorithm, and determines the optimal solution of the data decomposition algorithm based on the improved hippopotamus optimization algorithm, thereby avoiding the problem that it is difficult to find the optimal solution when using the traditional hippopotamus optimization algorithm.

[0144] Step S2500: Update the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeat the fitness value determination step until the iteration cut-off condition of the hippopotamus population is reached.

[0145] In one embodiment of the present application, the iteration cut-off condition of the hippopotamus population includes: repeating the fitness value determination step until the iteration times of the hippopotamus population are reached.

[0146] Alternatively, the iteration cut-off condition of the hippopotamus population includes: the error between the reconstructed data corresponding to the decomposed result data and the data to be decomposed is less than a preset error.

[0147] In one embodiment of the present application, the error between the reconstructed data corresponding to the decomposed result data and the data to be decomposed can be represented by their Euclidean distance. Among them, the preset error can be set according to experience.

[0148] In this embodiment, after updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, the above step S2200 is repeated until the iteration cut-off condition of the hippopotamus population is reached after repeating the above step S2200.

[0149] After updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, at this time, when repeating the above step S2200, after step S2210, the parameter value of the data decomposition algorithm is specifically the parameter value of the data decomposition algorithm represented by the updated position.

[0150] It should be noted that the above steps S2200 to S2500 are executed for any hippopotamus individual.

[0151] Step S2600: Output the decomposition result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individuals.

[0152] In this embodiment, among the currently obtained current individual fitness values of the hippopotamus individuals, the optimal current individual fitness value, and the position of the corresponding hippopotamus individual, which is also the parameter value of the data decomposition algorithm, is the optimal solution. When the data decomposition algorithm is the VMD algorithm and the position of the hippopotamus individual is the k value and the alpha value in the VMD algorithm, the aforementioned optimal solution is the optimal k value and alpha value in the VMD algorithm. That is, the present application determines the k value and the alpha value in the VMD algorithm through the improved hippopotamus algorithm, which is different from the traditional technology of setting according to experience.

[0153] Therefore, among the currently obtained current individual fitness values of the hippopotamus individuals, the decomposition result data corresponding to the optimal current individual fitness value is the optimal decomposition result corresponding to the data to be decomposed.

[0154] Based on the above, in the data decomposition method provided by the present application, for the data to be decomposed, the improved hippopotamus optimization algorithm can be used to determine the decomposition result data corresponding to the optimal current individual fitness value when the iteration cutoff condition of the hippopotamus population is reached. Since the position of the hippopotamus individual corresponding to the optimal current individual fitness value when the iteration cutoff condition of the hippopotamus population is reached is the optimal solution of the parameter value of the data decomposition algorithm, the decomposition result data corresponding to the optimal current individual fitness value when the iteration cutoff condition of the hippopotamus population is reached is the accurate result of the data to be decomposed. That is, when the data decomposition algorithm is the VMD algorithm, the data decomposition method provided by the present application can determine the optimal k value and alpha value in the VMD algorithm through the improved hippopotamus algorithm, thereby realizing the accurate decomposition of the data to be decomposed.

[0155] Based on the above, taking the data to be decomposed as a pose sequence X = (x1, x2,... x n ) composed of n pose data of a head-mounted device arranged in time series, and the optimal k value being 3 as an example, then based on the above steps S2100 to S2600, the accurate decomposition of X = (x1, x2,... x n ) can be realized, and 3 IMFs corresponding to the accurate pose sequence X = (x1, x2,... x n ) are obtained as the decomposition result data. On this basis, if the pose sequence X = (x1, x2,... x n ) is used as a historical pose sequence, and the pose prediction algorithm is an algorithm that predicts the predicted pose sequence based on the historical pose sequence X = (x1, x2,... x n ), and the predicted pose sequence is [x n+i , xn+i+1 ,…, x n+i+k In the case of the algorithm, three IMFs corresponding to the historical pose sequence and the pose prediction algorithm can be utilized to perform pose prediction to obtain three IMFs corresponding to the predicted pose sequence. The reconstructed data of the three IMFs corresponding to the predicted pose sequence can be used as the final accurate pose prediction result [x n+i , x n+i+1 ,…, x n+i+k . Wherein, i is an integer greater than or equal to 1, and k is the number of prediction steps.

[0156] The present application provides a data decomposition method, including: obtaining the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cutoff condition of the hippopotamus population; for any hippopotamus individual, performing a fitness value determination step to obtain the current individual fitness value of the hippopotamus individual. The fitness value determination step includes: determining a data decomposition algorithm according to the initial position of the hippopotamus individual, determining the decomposed result data for the data to be decomposed according to the data decomposition algorithm, determining the current individual fitness value of the hippopotamus individual according to the decomposed result data, and the initial position of the hippopotamus individual is used to represent the parameter value of the data decomposition algorithm; determining the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeatedly performing the fitness value determination step; updating the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual; updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeatedly performing the fitness value determination step until the iteration cutoff condition of the hippopotamus population is reached; outputting the decomposed result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individual. When the data decomposition algorithm is the VMD algorithm, the data decomposition method provided by the present application can determine the optimal k value and alpha value in the VMD algorithm through the improved hippopotamus algorithm, thereby realizing the accurate decomposition of the data to be decomposed.

[0157] In an embodiment of the present application, the above step S2400 is specifically implemented by the following steps S2410 to S2412.

[0158] Step S2410, in the case where the current relative speed change rate of the hippopotamus individual is greater than the preset change rate, updating the initial position of the hippopotamus individual according to the position update method in the first exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual.

[0159] In this embodiment, the preset change rate Rate1 represents the critical value of the current relative speed change rate in the first exploration stage and the second exploration stage, and the preset change rate can be set according to experience. In one example, the value of the preset change rate can be: 1.

[0160] Moreover, the updated intermediate position of the hippopotamus individual is an intermediate quantity in a population iteration process and needs to be continuously updated through the following step S2430.

[0161] The traditional hippopotamus optimization algorithm includes two exploration phases, namely exploration phase 1 and exploration phase 2. In this embodiment, exploration phase 1 is denoted as the first exploration phase, and exploration phase 2 is denoted as the second exploration phase.

[0162] In an embodiment of the present application, the position update method in the first exploration phase of the traditional hippopotamus optimization algorithm is specifically as shown in the following formula nine. That is, when the current relative speed change rate Rate is greater than the preset change rate Rate1, the initial position of the hippopotamus individual is updated through the following formula nine.

[0163] X_P1(i,:) = X(i,:) + r2 × (D hippo - I1X(i,:)) (Formula nine)

[0164] Where X_P1(i,:) represents the updated intermediate position of the i-th hippopotamus individual when the current relative speed change rate of the hippopotamus individual is greater than the preset change rate; X(i,:) represents the current initial position to be updated of the i-th hippopotamus individual, r2 represents a random number in the interval [0,1]; D hippo represents the initial position of the current optimal hippopotamus individual, that is, the initial position of the hippopotamus individual corresponding to the currently obtained optimal current individual fitness value; I1 represents a random number in the interval [1,2].

[0165] In an embodiment of the present application, the above step S2410 can also be specifically implemented through the following steps S2410-1 to S2410-3.

[0166] Step S2410-1, determine the adaptive weight of the hippopotamus individual according to the current individual fitness value of each hippopotamus individual.

[0167] In an embodiment of the present application, the adaptive weight of any hippopotamus individual can be specifically determined through the following formula ten.

[0168]

[0169] Where f represents the current individual fitness value of the hippopotamus individual; f avg represents the average value of the current individual fitness values of all hippopotamus individuals; f min represents the minimum value of the current individual fitness values of all hippopotamus individuals; f max represents the maximum value of the current individual fitness values of all hippopotamus individuals; ω min represents the minimum weight; ωmax It represents the maximum weight value; a represents an adjustable parameter; ω represents the adaptive weight of the hippopotamus individual.

[0170] It should be noted that when the value of a is different, the value range of tanh is different. For example, in this embodiment, when a takes different values, the values of the tanh function are as Figure 4 shown. Based on Figure 4 it can be known that the larger the value of a, the slower the change speed of tanh. Therefore, in this embodiment, the value rule of a is when taking the maximum value, tanh converges, that is, tanh approaches 1.

[0171] Based on the above formula ten, it can be realized that when the adaptive weight is relatively large, the global search ability of the algorithm is strong, and when the adaptive weight is relatively small, the algorithm can perform fine search around the optimal solution to accelerate the convergence speed. In this embodiment, when the current relative speed change rate of the hippopotamus individual is greater than the preset change rate, the adaptive weight is determined according to the current individual fitness value of the hippopotamus individual to adjust the global search and local exploration capabilities. If the current individual adaptive fitness value of the hippopotamus individual is lower than the average value of the current individual fitness values of all hippopotamus individuals, the adaptive fitness weight value increases to enhance the global exploration ability. If the current individual fitness value of the hippopotamus individual is higher than the average value of the current individual fitness values of all hippopotamus individuals, the adaptive fitness weight decreases to enhance the local exploration ability.

[0172] Step S2410-2: According to the adaptive weight, update the position update formula in the first exploration stage of the hippopotamus optimization algorithm to obtain the updated position update formula in the first exploration stage.

[0173] In this embodiment, the above step S2412 can be specifically implemented by the following formula eleven.

[0174] X_P1(i,:) = ω(t) × X(i,:) + r2 × (D hippo -I1X(i,:)) (Formula eleven)

[0175] where w(t) represents the adaptive weight when repeating the above step S2200 for the current number of times.

[0176] Step S2410-3: According to the updated position update formula in the first exploration stage, update the initial position of the hippopotamus individual and the updated intermediate position of the hippopotamus individual.

[0177] Step S2411: When the current relative speed change rate of the hippopotamus individual is less than or equal to the preset change rate, update the initial position of the hippopotamus individual according to the position update formula in the second exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual.

[0178] In this embodiment, the position update formula in the second exploration stage of the hippopotamus optimization algorithm is as shown in Formula XII below. That is, when the current relative speed change rate Rate is less than or equal to the preset change rate Rate1, the initial position of the hippopotamus individual is updated by Formula XII below.

[0179]

[0180] Among them, X_P2(i,:) represents the updated intermediate position of the i-th hippopotamus individual when the current relative speed change rate of the hippopotamus individual is less than or equal to the preset change rate; r3 represents a random number in the interval [-1, 1]; b, c, d, and g are all uniformly distributed random numbers, the value range of b is [2, 4], the value range of c is [1, 1.5], the value range of d is [2, 3], and the value range of g is [2, 4]; Predator j represents the position of the predator in the search space, which is a random position. The specific value-taking method can refer to the traditional hippopotamus optimization algorithm; represents the factor for the hippopotamus to take defensive behavior to protect itself from predator attacks. The specific value-taking method can refer to the traditional hippopotamus optimization algorithm; F i represents the objective function value. The specific value-taking method can refer to the traditional hippopotamus optimization algorithm; represents a random vector with Levy distribution, which is used to describe the position mutation of the predator when attacking the hippopotamus. The specific value-taking method can refer to the traditional hippopotamus optimization algorithm.

[0181] Step S2412: For any hippopotamus individual, update the updated intermediate position of the hippopotamus individual according to the position update formula in the exploitation stage of the hippopotamus optimization algorithm to obtain the updated position of the hippopotamus individual.

[0182] The position update formula in the exploitation stage of the hippopotamus optimization algorithm is as shown in Formula XIII below. That is, on the basis of the above steps S2310 and S2320, for the updated intermediate position of each hippopotamus individual, it is further updated by Formula XIII below.

[0183]

[0184] Among them, X_P3(i,:) represents the updated position of the i-th hippopotamus individual; r4 represents a random number in the interval [0, 1]; r5 represents a random number conforming to the normal distribution.

[0185] It should be noted that the above steps S2410 to S2412 belong to the complete update of the position of the hippopotamus individual, that is, it belongs to a complete iteration of the hippopotamus population.

[0186] Regarding the position update method of the hippopotamus individuals shown in the above steps S2410 to S2412, compared with the traditional hippopotamus optimization algorithm, in this embodiment, instead of grouping and updating positions according to the order of hippopotamus individuals as in the traditional hippopotamus algorithm, the position is updated depending on the current relative speed change rate of each hippopotamus individual. On this basis, the problem that it is difficult to find the optimal k value and alpha value of the VMD algorithm using the traditional hippopotamus optimization algorithm can be avoided.

[0187] This application also provides a data decomposition device 500, as Figure 5 shown, including:

[0188] An acquisition module 510, configured to acquire the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cut-off condition of the hippopotamus population;

[0189] An execution module 520, configured to, for any hippopotamus individual, execute a fitness value determination step to obtain the current individual fitness value of the hippopotamus individual. The fitness value determination step includes: determining a data decomposition algorithm according to the initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, and determining the current individual fitness value of the hippopotamus individual according to the decomposition result data. The initial position of the hippopotamus individual is used to represent the parameter value of the data decomposition algorithm;

[0190] A determination module 530, configured to determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times of repeating the execution of the fitness value determination step;

[0191] An update module 540, configured to update the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual;

[0192] An iteration module 550, configured to update the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeat the execution of the fitness value determination step until the iteration cut-off condition of the hippopotamus population is reached;

[0193] An output module 570, configured to output the decomposition result data corresponding to the optimal current individual fitness value among the currently obtained current individual fitness values of the hippopotamus individual.

[0194] In an embodiment of this application, the iteration cut-off condition of the hippopotamus population includes:

[0195] Repeating the execution of the fitness value determination step until the iteration times of the hippopotamus population are reached;

[0196] Alternatively, the error between the reconstructed data corresponding to the decomposition result data and the data to be decomposed is less than a preset error.

[0197] In an embodiment of the present application, the execution module 520 is specifically configured to determine reconstructed data according to the decomposition result data;

[0198] Determine the Euclidean distance between the reconstructed data and the data to be decomposed as the current individual fitness value of the hippopotamus individual;

[0199] Alternatively, according to the decomposition result data, determine the sum of the fuzzy entropies corresponding to the decomposition result data, and determine the sum of the fuzzy entropies as the current individual fitness value of the hippopotamus individual.

[0200] In an embodiment of the present application, the determination module 530 is further configured to determine the random mapping value of the hippopotamus individual according to a random mapping;

[0201] Determine the chaotic mapping value of the hippopotamus individual according to the random mapping value and a chaotic mapping;

[0202] Determine the initial position of the hippopotamus individual according to the chaotic mapping value of the hippopotamus individual and the initial position constraint condition of the hippopotamus individual.

[0203] In an embodiment of the present application, the determination module 530 is specifically configured to determine that the current relative speed change rate of the hippopotamus individual is infinite when the number of times of repeating the fitness value determination step is 0;

[0204] When the number of times of repeating the fitness value determination step is greater than 0, determine the current optimal fitness value according to the current individual fitness value of each hippopotamus individual;

[0205] Determine the historical optimal fitness value according to the historical fitness value obtained by each hippopotamus individual in the previous execution of the fitness value determination step;

[0206] Obtain the historical individual fitness value obtained by the hippopotamus individual in the previous execution of the fitness value determination step;

[0207] Determine the current relative speed change rate of the hippopotamus individual according to the historical individual fitness value, the current individual fitness value, the historical optimal fitness value, and the current optimal fitness value.

[0208] In an embodiment of the present application, the update module 540 is specifically configured to: when the current relative speed change rate of the hippopotamus individual is greater than a preset change rate, update the initial position of the hippopotamus individual according to the first exploration stage position update method in the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual;

[0209] When the current relative speed change rate of the hippopotamus individual is less than or equal to the preset change rate, update the initial position of the hippopotamus individual according to the position update method in the second exploration stage of the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual;

[0210] For any one of the hippopotamus individuals, update the updated intermediate position of the hippopotamus individual according to the position update method in the exploitation stage of the hippopotamus optimization algorithm to obtain the updated position of the hippopotamus individual.

[0211] In an embodiment of the present application, the update module 540 is specifically configured to: determine the adaptive weight of each hippopotamus individual according to the current individual fitness value of each hippopotamus individual;

[0212] Update the position update formula in the first exploration stage of the hippopotamus optimization algorithm according to the adaptive weight to obtain the updated position update method in the first exploration stage;

[0213] Update the initial position of the hippopotamus individual and the updated intermediate position of the hippopotamus individual according to the updated position update method in the first exploration stage.

[0214] The present application also provides an electronic device 600, and the electronic device 600 includes any one of the data decomposition devices 500 provided in the above device embodiments;

[0215] Or, as Figure 6 shown, the electronic device 600 includes a memory 610 and a processor 620. The memory 610 is used to store computer instructions, and the processor 620 is used to call the computer instructions from the memory 610 to execute any one of the data decomposition methods provided in the above method embodiments.

[0216] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the data decomposition methods provided in the above method embodiments.

[0217] The present application may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0218] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0219] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded to an external computer or an external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0220] The computer program instructions for performing the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of this application.

[0221] Aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0222] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data - processing device, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, and these instructions cause the computer, programmable data - processing device, and / or other devices to work in a specific manner. Thus, the computer - readable medium storing the instructions includes a manufactured article that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0223] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0224] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the boxes may occur in a different order than noted in the figures. For example, two consecutive boxes may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions. As is well known to those skilled in the art, implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.

[0225] The embodiments of the present application have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein. The scope of the present application is defined by the appended claims.

Claims

1. A data decomposition method, characterized in that: include: Obtain the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cutoff condition of the hippopotamus population; For any hippopotamus individual, a fitness value determination step is performed to obtain the current individual fitness value of the hippopotamus individual, wherein the fitness value determination step comprises: determining a data decomposition algorithm according to an initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, and determining the current individual fitness value of the hippopotamus individual according to the decomposition result data, wherein the initial position of the hippopotamus individual is used to characterize a parameter value of the data decomposition algorithm; Determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times the fitness value determination step is repeated; According to the current relative speed change rate of the hippopotamus individual, the initial position of the hippopotamus individual is updated to obtain the updated position of the hippopotamus individual; Updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeating the fitness value determination step until the hippopotamus population iteration cutoff condition is reached; Output the decomposition result data corresponding to the best current individual fitness value among the latest obtained current individual fitness values ​​of the hippopotamus individuals.

2. The method according to claim 1, characterized in that The hippo population iteration cutoff conditions include: Repeat the fitness value determination step until the number of hippo population iterations is reached; Alternatively, an error between the reconstructed data corresponding to the decomposition result data and the data to be decomposed is less than a preset error.

3. The method according to claim 1, characterized in that Determining the current individual fitness value of the hippopotamus individual according to the decomposition result data includes: Determining reconstructed data according to the decomposition result data; Determine the Euclidean distance between the reconstructed data and the data to be decomposed as the current individual fitness value of the hippopotamus individual; Alternatively, based on the decomposition result data, the sum of the fuzzy entropies corresponding to the decomposition result data is determined, and the sum of the fuzzy entropies is determined as the current individual fitness value of the hippopotamus individual.

4. The method according to claim 1, characterized in that Before determining the data decomposition algorithm according to the initial position of the hippopotamus individual, the method further comprises: According to the random mapping, determining the random mapping value of the hippopotamus individual; Determining the chaotic mapping value of the hippopotamus individual according to the random mapping value and the chaotic mapping; The initial position of the hippopotamus individual is determined according to the chaotic mapping value of the hippopotamus individual and the initial position constraint condition of the hippopotamus individual.

5. The method according to claim 1, characterized in that Determining the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times the fitness value determination step is repeated includes: When the number of times the fitness value determination step is repeated is 0, determining that the current relative speed change rate of the hippopotamus individual is infinite; When the number of times the fitness value determination step is repeated is greater than 0, determining the current optimal fitness value according to the current individual fitness value of each of the hippopotamus individuals; Determine the historical optimal fitness value according to the historical fitness value obtained by performing the fitness value determination step for each hippopotamus individual last time; Obtaining a historical individual fitness value obtained by performing the fitness value determination step for the hippopotamus individual last time; The current relative speed change rate of the hippopotamus individual is determined according to the historical individual fitness value, the current individual fitness value, the historical optimal fitness value and the current optimal fitness value.

6. The method according to claim 1, characterized in that The updating of the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual to obtain the updated position of the hippopotamus individual includes: When the current relative speed change rate of the hippopotamus individual is greater than the preset change rate, the initial position of the hippopotamus individual is updated according to the position update method of the first exploration phase in the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual; When the current relative speed change rate of the hippopotamus individual is less than or equal to the preset change rate, the initial position of the hippopotamus individual is updated according to the position update method of the second exploration phase in the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual; For any of the hippopotamus individuals, the updated intermediate position of the hippopotamus individual is updated according to the position update method in the development phase of the hippopotamus optimization algorithm to obtain the updated position of the hippopotamus individual.

7. The method according to claim 6, characterized in that The method of updating the initial position of the hippopotamus individual according to the position update formula of the first exploration phase in the hippopotamus optimization algorithm to obtain the updated intermediate position of the hippopotamus individual includes: Determine the adaptive weight of each hippopotamus individual according to the current individual fitness value of the hippopotamus individual; According to the adaptive weight, updating the position update formula of the first exploration phase in the Hippo optimization algorithm to obtain an updated position update method of the first exploration phase; According to the updated position updating method of the first exploration phase, the initial position of the hippopotamus individual and the updated intermediate position of the hippopotamus individual are updated.

8. A data decomposition device, characterized in that: include: An acquisition module is used to obtain the data to be decomposed, the number of hippopotamus individuals in the hippopotamus population, and the iteration cutoff condition of the hippopotamus population; An execution module is used to execute a fitness value determination step for any hippopotamus individual to obtain a current individual fitness value of the hippopotamus individual, wherein the fitness value determination step comprises: determining a data decomposition algorithm according to an initial position of the hippopotamus individual, determining decomposition result data for the data to be decomposed according to the data decomposition algorithm, and determining the current individual fitness value of the hippopotamus individual according to the decomposition result data, wherein the initial position of the hippopotamus individual is used to characterize a parameter value of the data decomposition algorithm; A determination module, used to determine the current relative speed change rate of the hippopotamus individual according to the current individual fitness value of the hippopotamus individual and the number of times the fitness value determination step is repeated; An updating module, used for updating the initial position of the hippopotamus individual according to the current relative speed change rate of the hippopotamus individual, so as to obtain the updated position of the hippopotamus individual; An iteration module, used for updating the initial position of the hippopotamus individual to the updated position of the hippopotamus individual, and repeating the fitness value determination step until the hippopotamus population iteration cutoff condition is reached; The output module is used to output the decomposition result data corresponding to the best current individual fitness value among the latest current individual fitness values ​​of the hippopotamus individuals.

9. An electronic device, characterized in that: The electronic device comprises the apparatus as claimed in claim 8; Alternatively, the method comprises a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method according to any one of claims 1 to 7.

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

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