Flexible sensor signal decoupling method and system based on improved SSA-BP algorithm

Abstract

The invention discloses a flexible sensor signal decoupling method and system based on an improved SSA-BP algorithm, and the method comprises the steps: obtaining the electric signal data of a sensor, constructing an initial sequence of signals, obtaining an accurate weight and threshold through an improved sparrow search algorithm, and carrying out the decoupling of the signals. And constructing a network model by using the calculated weight and threshold through an improved BP neural network algorithm, training the BP neural network model, and decoupling the signal by using the trained network model. The interference of noise signals is reduced, the signal detection precision and accuracy of the sensor are improved, the practicability is enhanced, and the sensor can be applied to detection of various required signals.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensor signal processing, and in particular relates to a flexible sensor signal decoupling method and system based on an improved SSA-BP algorithm. Background Art

[0002] In modern electronics and sensor technology, flexible sensors are a new type of sensor technology with high flexibility and scalability. They can work under various bending, stretching and deformation conditions and are widely used in wearable devices, smart textiles, environmental perception, health monitoring systems and other fields. However, in practical applications, the output signals of flexible sensors are often affected by multiple interferences and noises, and may sense the influence of multiple physical quantities at the same time. These signals are usually the superposition of multiple sensor output signals and contain the mixed effects of multiple information sources. To accurately interpret the independent signal of each sensor, the signal decoupling problem must be solved. Although traditional signal decoupling methods (such as filters, statistical methods, etc.) are effective in some cases, they often show limitations when processing complex multi-channel and nonlinear signals. These methods usually require manual adjustment and optimization for specific signals, are inefficient, and difficult to adapt to changing application environments.

[0003] According to current research results, the BP neural network algorithm is an effective artificial neural network model that can be applied to the problem of sensor signal decoupling. However, the BP neural network algorithm has the following shortcomings: (1) high computational complexity; (2) easy to fall into local optimality; (3) long training time; (4) sensitive to initial weights. Therefore, the BP neural network algorithm still has some defects, which makes the algorithm often fail to achieve ideal results when performing sensor signal decoupling. Summary of the invention

[0004] The present invention provides a flexible sensor signal decoupling method and system based on an improved SSA-BP algorithm, which is used to solve at least one of the above-mentioned technical problems existing in the existing BP neural network algorithm, can reduce the computational complexity, shorten the training time and effectively decouple the signal.

[0005] In a first aspect, the present invention provides a flexible sensor signal decoupling method based on an improved SSA-BP algorithm, comprising: The improved SSA algorithm is used to determine the weights and thresholds of the pre-built network model; According to the weight and the threshold, the network model is trained using an improved BP neural network algorithm to obtain a final signal decoupling model; Acquiring electrical signal data of the flexible sensor, and constructing an initial signal sequence according to the electrical signal data; The initial signal sequence is input into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

[0006] In a second aspect, the present invention provides a flexible sensor signal decoupling system based on an improved SSA-BP algorithm, comprising: A determination module configured to determine weights and thresholds of a pre-built network model using an improved SSA algorithm; A training module is configured to train the network model using an improved BP neural network algorithm according to the weight and the threshold to obtain a final signal decoupling model; A construction module configured to obtain electrical signal data of the flexible sensor and construct an initial signal sequence according to the electrical signal data; The output module is configured to input the initial signal sequence into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

[0007] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the flexible sensor signal decoupling method based on the improved SSA-BP algorithm of any embodiment of the present invention.

[0008] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program instructions are executed by a processor, the processor executes the steps of the flexible sensor signal decoupling method based on the improved SSA-BP algorithm of any embodiment of the present invention.

[0009] The flexible sensor signal decoupling method and system based on the improved SSA-BP algorithm of the present application obtains the electrical signal data of the sensor and constructs the initial sequence of the signal; uses the improved sparrow search algorithm (SSA) to obtain accurate weights and thresholds; uses the calculated weights and thresholds to construct a network model through the improved BP neural network algorithm, and trains the BP neural network model; and uses the trained network model to decouple the signal. The present application reduces the interference of noise signals, improves the signal detection precision and accuracy of the sensor, enhances the practicality, and is conducive to the application in detecting various required signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a flexible sensor signal decoupling method based on an improved SSA-BP algorithm provided by an embodiment of the present invention; Figure 2 A structural block diagram of a flexible sensor signal decoupling system based on an improved SSA-BP algorithm provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] See also Figure 1 , which shows a flow chart of a flexible sensor signal decoupling method based on an improved SSA-BP algorithm of the present application.

[0014] like Figure 1 As shown, the flexible sensor signal decoupling method based on the improved SSA-BP algorithm specifically includes the following steps: Step S101, using an improved SSA algorithm to determine the weight and threshold of a pre-built network model.

[0015] In this step, the fitness of the discoverer provides the overall search range and direction for the sparrow population. The solutions of the discoverers with high fitness are of good quality. The solutions of these individuals can be used as search targets to guide other individuals to move closer to high-quality areas. The discoverers with high fitness can accelerate the convergence process of the algorithm. The high-quality solutions provided by the discoverers provide a good direction for the algorithm, thereby reducing invalid searches and increasing the convergence speed. The fitness values ​​of all sparrows are expressed as: , In the formula, is the fitness value, is the population of sparrows, n is the dimension of the variable to be optimized, and k is the number of sparrows; The finder with a better fitness value will get food first during the search process. In addition, because the finder is responsible for finding food for the entire sparrow population and providing foraging directions for all joiners, the finder can obtain a larger foraging search range than the joiner.

[0016] Specifically, in each iteration, the position of the finder is updated, and the expression is: , In the formula, is the position of the next finder in the global search, is the current finder's position, is the current iteration number, is a random number, is the maximum number of iterations, is a safe value belonging to [0.5,1], is the warning value belonging to [0,1], is a 1×d matrix, is a random number that follows a normal distribution, is the dimension of the signal, is the signal quantity at the same latitude; when When , it means that there are no predators around the discoverer and it is in a safe environment. The discoverer can continue to expand the search range. When the finder sees a predator, it means that there is a predator around the finder and the sparrow is in danger. The finder will then send an alarm to the sparrow population and quickly fly to other safe places to find food.

[0017] Introducing the normal folding mapping hybrid search strategy, in the standard SSA, the accuracy of the solution obtained by global search alone is not high enough, and the convergence speed is slow, and it is easy to fall into the local optimum. In order to improve the accuracy and convergence speed and avoid falling into the local optimum, the normal folding mapping hybrid search strategy is introduced. The specific content refers to: performing a normal folding mapping hybrid search, and combining the global search and local search solutions of the SSA algorithm to obtain a new solution. The normal folding mapping hybrid search includes a normal folding mapping local search, and the expression is: , In the formula, is the current finder position obtained by local search of the normal folding map, is a random quantity that follows a normal distribution, is the local worst solution, The previous finder position for the local search of the normal fold map, is the weight of the local optimal solution, is the local optimal solution, is the weight function of the random body of the population, is a random entity in the population, is the weight function of the second random entity in the population, is the random body 2 in the population; , In the formula, is the weight function, is the dynamic factor 1, is the dynamic factor 2, is a constant of one, is the constant 2, is the constant three, is the current iteration number; The normal folding mapping hybrid search includes a normal folding mapping local area search, and the expression is: , In the formula, is the adjustment factor, is the number of signals, is the position of the next finder in the global search, is the current finder position obtained by local search of the normal folding map, is the next finder position obtained by local search of the normal folding map, is the local optimal solution of the current individual, is the search precision difference factor; Combined with the new solution obtained by local search of normal folding mapping, the position of the sparrow finder is updated by weighted average, and the expression is: , , In the formula, is the position of the next finder in the normal fold map hybrid search, is the weight vector of the trend of the current individual moving toward the optimal individual, is the position of the next finder in the global search, is the search weight, is the next finder position obtained by local search of the normal folding map, is the weight vector of the trend of the current individual moving toward the optimal individual, is a constant indicating the degree to which the current individual follows the optimal individual, is the number of iterations, is the maximum number of iterations, is a random number that conforms to the normal distribution. is the lower limit of the search range; In the process of searching for food, the joiners will always monitor the status of the discoverers. Once they find that the discoverers have found better food, the joiners will fly to compete for the discoverers' food. If the joiner wins, the joiner gets the discoverers' food, otherwise it will continue to update its position. The position update expression of the joiner is: , In the formula, The position of the next joiner, is the global worst joiner position, is the position of the current joiner, The optimal position for the next discoverer to occupy, is a 1×d matrix, is the transpose of matrix A, is a 1×d row vector, where d is a positive integer; Assume that sparrows that are aware of danger account for one tenth of the total population. These sparrows are called alerters. The initial position of the alerter is randomly generated. The expression for updating the position of the alerter is: , In the formula, For the next sentinel's position, is the current position of the sentinel, is the position of the global optimal solution, is the step size control parameter, is the fitness value of the i-th sparrow, is the global optimal fitness value, is a random number, is the position of the global worst solution, is the global worst fitness value, is a constant; The position of the sparrow is set as the weight and threshold of the neural network, and the fitness function is defined as the error function of the neural network, expressed as: , In the formula, is the position set of each sparrow, is the set of hidden layer weights and hidden layer thresholds, is the weight of the hidden layer, is the threshold of the hidden layer, is the fitness function, is the fitness coefficient, is the true output value, is the output value of the network, is the sample size.

[0018] Step S102: According to the weight and the threshold, the network model is trained using an improved BP neural network algorithm to obtain a final signal decoupling model.

[0019] In this step, the input layer data is x. The input data has multi-dimensional complexity. If the acquired data is directly input into the input layer of the BP neural network, the training time will be too long and the trained model will have low stability. Therefore, adding gradient consistency standardization can increase the training rate and improve the model stability, making the gradient update more consistent during the training process. First, the input data is subjected to gradient consistency standardization. The expression is: , , In the formula, is the data value after normalization. is the original input data, is the time constant, is the input value after gradient consistency normalization, is the gradient scaling factor, is the bias term, is the maximum value of the input data. is the gradient scaling rate, is the gradient scaling step size, is the minimum value in the input data. is the preset parameter, is a natural constant, is the current iteration number; Since the initial threshold and weight of the BP neural network are randomly selected, training with a large data sample will consume a lot of time and more computing resources, and it is easy to fall into local extreme values. Therefore, the improved SSA algorithm is used to initialize the weights and bias items in the BP neural network according to the weights and thresholds of the network model, and perform weighted sum calculations, where the weights and bias items are recorded as , , as well as , the expression for weighted sum calculation is: , In the formula, For the The input of the layer, For the The neurons in layer i and The weights between neurons in layer j, For the The activation value of neuron j in layer, For the The bias term of neuron i in layer; Activate forward propagation, perform loss calculation, obtain the expected value of each layer output and loss function, and calculate the loss between the network output and the actual target value. The loss function The expression is: , In the formula, To average the total error values, is the true value, is the network prediction value; In back propagation, the weights and biases in the neural network are the parameters of the training model. The loss function is calculated using the chain rule, and the gradient value of each parameter is calculated based on the loss function. The error of the output layer reflects the difference between the predicted value of each output neuron and the actual target value. For the calculation of the output layer error, it is first necessary to determine the difference between the activation value of the network output and the true label. For each output neuron k, its output error for: , In the formula, is the activation value of the kth neuron in the output layer, is the actual target value of the kth neuron, is the derivative of the activation function at the output layer; The hidden layer error calculation requires back propagation using the output layer error to pass the error back to the previous layer. For the i-th neuron in the hidden layer, its error It is obtained by weighted sum calculation, where the weighting factor comes from the error of the next layer. The expression for the hidden layer error calculation is , In the formula, For the The error of the i-th neuron in the layer, For the The error of the jth neuron in the layer, For the The i-th neuron and the The weights between the jth neurons in the layer, For the Derivatives of layer activation functions; According to the calculated gradient, the weights and biases of the neural network are updated using the gradient descent method, and the expression is: , , In the formula, For the The updated weights of the layer, For the The weight of the layer, is the learning rate, For the The bias term after the layer update, For the The bias term of the layer; If the network does not reach the set number of iterations, the process of forward propagation, loss calculation, backpropagation, and weight update will be repeated until the loss function converges or the set number of iterations is reached.

[0020] Step S103, acquiring electrical signal data of the flexible sensor, and constructing an initial signal sequence according to the electrical signal data.

[0021] In this step, the electrical signal data of the flexible sensor is obtained according to a preset multi-dimensional complex cross-linking signal acquisition strategy to obtain a multi-dimensional signal matrix, wherein the expression of the multi-dimensional complex cross-linking signal acquisition strategy is: , In the formula, is a multidimensional signal matrix, is a set of signals of the same dimension, is the signal acquisition time constant, is the signal set of the i-th dimension, is the signal deviation coefficient, is the dimension, is the offset of the ith signal, is the signal acquisition time constant minus one, is the frequency domain signal matrix, is the i-th dimension signal vector, is the j-th dimension signal vector; The multidimensional signal matrix is ​​weighted and the processed multidimensional signal matrix is ​​Fourier transformed, and the expression is: , In the formula, is the value of the Fourier transform of the ith signal, is the kth signal value, is the k-1th signal value, is a multidimensional signal matrix, is the weighting matrix of the ith signal, is the frequency domain system matrix of the ith signal, is the frequency domain noise matrix of the ith signal, is the inverse Fourier transform; The initial signal sequence is constructed according to the signal data after Fourier transformation. The expression of the initial signal sequence is: , In the formula, is the initial signal sequence of the signal data, is the first collected data of the first dimension, is the second collected data of the first dimension, is the jth collected data of the first dimension, is the first collected data of the second dimension, is the second collected data of the second dimension, is the jth collected data of the second dimension, is the first collected data of the i-th dimension, is the second collected data of the i-th dimension, Collect data for the jth item in the i-th dimension.

[0022] Step S104: input the initial signal sequence into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

[0023] In summary, the method of the present application obtains the electrical signal data of the sensor, constructs the initial sequence of the signal, uses the improved sparrow search algorithm (SSA) to obtain accurate weights and thresholds, uses the calculated weights and thresholds to construct a network model through the improved BP neural network algorithm, trains the BP neural network model, and uses the trained network model to decouple the signal. It reduces the interference of noise signals, improves the signal detection precision and accuracy of the sensor, enhances practicality, and is conducive to the detection of various required signals.

[0024] See also Figure 2 , which shows a structural block diagram of a flexible sensor signal decoupling system based on an improved SSA-BP algorithm of the present application.

[0025] like Figure 2 As shown, the flexible sensor signal decoupling system 200 includes a determination module 210 , a training module 220 , a construction module 230 and an output module 240 .

[0026] Among them, the determination module 210 is configured to use the improved SSA algorithm to determine the weights and thresholds of the pre-constructed network model; the training module 220 is configured to use the improved BP neural network algorithm to train the network model according to the weights and the thresholds to obtain the final signal decoupling model; the construction module 230 is configured to obtain the electrical signal data of the flexible sensor and construct an initial signal sequence according to the electrical signal data; the output module 240 is configured to input the initial signal sequence into the signal decoupling model, and the signal decoupling model outputs the decoupling result.

[0027] It should be understood that Figure 2Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0028] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the flexible sensor signal decoupling method based on the improved SSA-BP algorithm in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: The improved SSA algorithm is used to determine the weights and thresholds of the pre-built network model; According to the weight and the threshold, the network model is trained using an improved BP neural network algorithm to obtain a final signal decoupling model; Acquiring electrical signal data of the flexible sensor, and constructing an initial signal sequence according to the electrical signal data; The initial signal sequence is input into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

[0029] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the flexible sensor signal decoupling system based on the improved SSA-BP algorithm, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the flexible sensor signal decoupling system based on the improved SSA-BP algorithm via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3In the example, the connection through the bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the flexible sensor signal decoupling method based on the improved SSA-BP algorithm of the above-mentioned method embodiment is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the flexible sensor signal decoupling system based on the improved SSA-BP algorithm. The output device 340 may include a display device such as a display screen.

[0031] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0032] As an implementation mode, the electronic device is applied to a flexible sensor signal decoupling system based on an improved SSA-BP algorithm, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: The improved SSA algorithm is used to determine the weights and thresholds of the pre-built network model; According to the weight and the threshold, the network model is trained using an improved BP neural network algorithm to obtain a final signal decoupling model; Acquiring electrical signal data of the flexible sensor, and constructing an initial signal sequence according to the electrical signal data; The initial signal sequence is input into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

[0033] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexible sensor signal decoupling method based on an improved SSA-BP algorithm, characterized in that: include: The improved SSA algorithm is used to determine the weights and thresholds of the pre-built network model; According to the weight and the threshold, the network model is trained using an improved BP neural network algorithm to obtain a final signal decoupling model; Acquiring electrical signal data of the flexible sensor, and constructing an initial signal sequence according to the electrical signal data; The initial signal sequence is input into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

2. According to claim 1, a flexible sensor signal decoupling method based on an improved SSA-BP algorithm is characterized in that: The method of using the improved SSA algorithm to determine the weight and threshold of the pre-built network model includes: In each iteration, the position of the finder is updated, and the expression is: ， In the formula, is the position of the next finder in the global search, is the current finder's position, is the current iteration number, is a random number, is the maximum number of iterations, is a safe value belonging to [0.5,1], is the warning value belonging to [0,1], is a 1×d matrix, is a random number that follows a normal distribution, is the dimension of the signal, is the signal quantity at the same latitude; A normal folding mapping hybrid search is performed, and a new solution is obtained by combining the global search and local search solutions of the SSA algorithm. The normal folding mapping hybrid search includes a normal folding mapping local search, and the expression is: ， In the formula, is the current finder position obtained by local search of the normal folding map, is a random quantity that follows a normal distribution, is the local worst solution, The previous finder position for the local search of the normal fold map, is the weight of the local optimal solution, is the local optimal solution, is the weight function of the random body of the population, is a random entity in the population, is the weight function of the second random entity in the population, is the random body 2 in the population; ， In the formula, is the weight function, is the dynamic factor 1, is the dynamic factor 2, is a constant of one, is the constant 2, is the constant three, is the current iteration number; The normal folding mapping hybrid search includes a normal folding mapping local area search, and the expression is: ， In the formula, is the adjustment factor, is the number of signals, is the position of the next finder in the global search, is the current finder position obtained by local search of the normal folding map, is the next finder position obtained by local search of the normal folding map, is the local optimal solution of the current individual, is the search precision difference factor; Combined with the new solution obtained by local search of normal folding mapping, the position of the sparrow finder is updated by weighted average, and the expression is: ， ， In the formula, is the position of the next finder in the normal fold map hybrid search, is the weight vector of the trend of the current individual moving toward the optimal individual, is the position of the next finder in the global search, is the search weight, is the next finder position obtained by local search of the normal folding map, is the weight vector of the trend of the current individual moving toward the optimal individual, is a constant indicating the degree to which the current individual follows the optimal individual, is the number of iterations, is the maximum number of iterations, is a random number that conforms to the normal distribution. is the lower limit of the search range; In the process of searching for food, the joiners will always monitor the status of the discoverers. Once they find that the discoverers have found better food, the joiners will fly to compete for the discoverers' food. If the joiner wins, the joiner gets the discoverers' food, otherwise it will continue to update its position. The position update expression of the joiner is: ， In the formula, The position of the next joiner, is the global worst joiner position, is the position of the current joiner, The optimal position for the next discoverer to occupy, is a 1×d matrix, is the transpose of matrix A, is a 1×d row vector, where d is a positive integer; Assume that sparrows that are aware of danger account for one tenth of the total population. These sparrows are called alerters. The initial position of the alerter is randomly generated. The expression for updating the position of the alerter is: ， In the formula, For the next sentinel's position, is the current position of the sentinel, is the position of the global optimal solution, is the step size control parameter, is the fitness value of the i-th sparrow, is the global optimal fitness value, is a random number, is the position of the global worst solution, is the global worst fitness value, is a constant; The position of the sparrow is set as the weight and threshold of the neural network, and the fitness function is defined as the error function of the neural network, expressed as: ， In the formula, is the position set of each sparrow, is the set of hidden layer weights and hidden layer thresholds, is the weight of the hidden layer, is the threshold of the hidden layer, is the fitness function, is the fitness coefficient, is the true output value, is the output value of the network, is the sample size.

3. According to claim 1, a flexible sensor signal decoupling method based on an improved SSA-BP algorithm is characterized in that: The method of training the network model using an improved BP neural network algorithm according to the weight and the threshold to obtain a final signal decoupling model includes: The input data is normalized for gradient consistency, and the expression is: ， ， In the formula, is the data value after normalization. is the original input data, is the time constant, is the input value after gradient consistency normalization, is the gradient scaling factor, is the bias term, is the maximum value of the input data. is the gradient scaling rate, is the gradient scaling step size, is the minimum value in the input data. is the preset parameter, is a natural constant, is the current iteration number; The improved SSA algorithm is used to initialize the weights and bias terms in the BP neural network according to the weights and thresholds of the network model, and perform weighted sum calculations, where the weights and bias terms are recorded as , , the expression for weighted sum calculation is: ， In the formula, For the The input of the layer, For the The neurons in layer i and The weights between neurons in layer j, For the The activation value of neuron j in layer, For the The bias term of neuron i in layer; Activate forward propagation, perform loss calculation, obtain the expected value of each layer output and loss function, and calculate the loss between the network output and the actual target value. The loss function The expression is: ， In the formula, To average the total error values, is the true value, is the network prediction value; In back propagation, the weights and biases in the neural network are the parameters of the training model. The loss function is calculated using the chain rule, and the gradient value of each parameter is calculated based on the loss function. According to the calculated gradient, the weights and biases of the neural network are updated using the gradient descent method, and the expression is: ， ， In the formula, For the The updated weights of the layer, For the The weight of the layer, is the learning rate, For the The bias term after the layer update, For the The bias term of the layer; If the network does not reach the set number of iterations, the process of forward propagation, loss calculation, backpropagation, and weight update will be repeated until the loss function converges or the set number of iterations is reached.

4. According to claim 1, a flexible sensor signal decoupling method based on an improved SSA-BP algorithm is characterized in that: The step of acquiring electrical signal data of the flexible sensor and constructing an initial signal sequence according to the electrical signal data includes: The electrical signal data of the flexible sensor is obtained according to a preset multi-dimensional complex cross-linking signal acquisition strategy to obtain a multi-dimensional signal matrix, wherein the expression of the multi-dimensional complex cross-linking signal acquisition strategy is: ， In the formula, is a multidimensional signal matrix, is a set of signals of the same dimension, is the signal acquisition time constant, is the signal set of the i-th dimension, is the signal deviation coefficient, is the dimension, is the offset of the ith signal, is the signal acquisition time constant minus one, is the frequency domain signal matrix, is the i-th dimension signal vector, is the j-th dimension signal vector; The multidimensional signal matrix is ​​weighted and the processed multidimensional signal matrix is ​​Fourier transformed, and the expression is: ， In the formula, is the value of the Fourier transform of the ith signal, is the kth signal value, is the k-1th signal value, is a multidimensional signal matrix, is the weighting matrix of the ith signal, is the frequency domain system matrix of the ith signal, is the frequency domain noise matrix of the ith signal, is the inverse Fourier transform; The initial signal sequence is constructed according to the signal data after Fourier transformation. The expression of the initial signal sequence is: ， In the formula, is the initial signal sequence of the signal data, Collect data for the jth item in the i-th dimension.

5. The flexible sensor signal decoupling method based on the improved SSA-BP algorithm according to claim 1 is characterized in that: The electrical signal data includes compression deformation signal data, tension deformation signal data and twist deformation signal data.

6. A flexible sensor signal decoupling system based on an improved SSA-BP algorithm, characterized in that: include: A determination module configured to determine weights and thresholds of a pre-built network model using an improved SSA algorithm; A training module is configured to train the network model using an improved BP neural network algorithm according to the weight and the threshold to obtain a final signal decoupling model; A construction module configured to obtain electrical signal data of the flexible sensor and construct an initial signal sequence according to the electrical signal data; The output module is configured to input the initial signal sequence into the signal decoupling model, and the signal decoupling model outputs a decoupling result.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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