Animal husbandry equipment cooperative control method and system based on wireless communication

Abstract

The invention discloses a livestock raising equipment cooperative control method and system based on wireless communication, relates to the technical field of wireless communication, breaks through the limitation of traditional pure data driven blind separation, and provides a structure constraint type projection method based on RIS asymmetric partitioning. RIS is divided into an auxiliary detection and symbiotic transmission array plane, a known physical reflection coefficient is used as a prior structure and is subjected to forced orthogonal fusion with an SVD decomposition result, and a structure constraint type projection operator is constructed. According to the bypass detection and main path stripping mechanism, channel estimation in advance is not needed, the calculation complexity is low, and strong direct wave interference can be accurately stripped under the conditions of extremely low signal-to-noise ratio and few snapshots. According to the method, the signal to interference plus noise ratio of the weak symbiotic signals is greatly improved, dependence on the number of snapshots is remarkably reduced, the environment subspace can still be stably extracted under the extremely low signal to noise ratio, the error rate platform phenomenon of a traditional method is avoided, the secondary signals are highly reliably recovered, and accurate cooperative control over livestock raising equipment is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method and system for collaborative control of livestock equipment based on wireless communication. Background Technology

[0002] With the development of smart animal husbandry, a large number of low-power IoT devices have been deployed in farms. Symbiotic wireless communication utilizes the backscattering principle, enabling secondary devices to transmit information by reflecting the main system's radio frequency signals, eliminating the need for self-generated carriers and significantly reducing power consumption. Reconfigurable smart surfaces (RIS) enhance signals or modulate information by adjusting the reflection coefficient, making them an ideal technology for symbiotic communication. However, RIS-based symbiotic communication faces the challenge of weak signal detection under strong direct wave interference. In the received signal, the power of the main system's direct wave is much higher than the symbiotic signal reflected by the RIS. Furthermore, the presence of non-information-carrying structural scattering causes weak signals to be submerged, making it difficult for traditional detection algorithms to effectively demodulate them. Existing interference suppression methods based on the covariance matrix (SVD) are susceptible to noise in low signal-to-noise ratio environments, causing bias in interference subspace estimation. This not only fails to eliminate strong interference but may also damage weak symbiotic signals, affecting reliable system communication.

[0003] Currently, Chinese invention patent application number 202511650438.9 discloses a method for real-time monitoring of livestock farming environment, including: collecting multimodal sensing signals from edge computing nodes deployed in livestock farms and performing edge preprocessing; analyzing the preprocessed signals using a lightweight feature extraction model and constructing a comprehensive health stress index; performing edge-cloud collaborative training based on federated learning, with the cloud server performing global model aggregation; generating updated environmental control strategies and synchronizing them to edge computing nodes through the edge network; and combining the environmental control strategies with the comprehensive health stress index to generate and execute control commands for environmental control devices in the edge IoT. This invention can overcome the limitations of one-sided information from a single data source, ensuring low latency and high reliability of environmental control, and realizing refined management and intelligent decision-making in livestock farming. However, existing symbiotic wireless communication technologies are difficult to effectively detect weak symbiotic signals under strong direct wave background interference, resulting in low reliability of collaborative control of livestock equipment. Summary of the Invention

[0004] The technical problem solved by this invention is that existing symbiotic wireless communication technologies have difficulty effectively detecting weak symbiotic signals under strong direct wave background interference, resulting in low reliability of collaborative control of livestock equipment.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a collaborative control method for livestock equipment based on wireless communication, comprising the following steps: Step S1: Within the preset training time slot, control the symbiotic transmission array to enter a silent state, and cause the auxiliary detection array to reflect the main signal to obtain the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; Step S2: Construct a first Hankel matrix for the first observation signal, and extract the data-driven subspace basis matrix by performing singular value decomposition on the first Hankel matrix. Obtain the known physical reflection coefficients of the auxiliary detection array to construct a physical prior structure matrix. Perform joint orthogonalization processing on the data-driven subspace basis matrix and the physical prior structure matrix to generate an environmental subspace projection matrix. Step S3: Construct an orthogonal complementary projection operator based on the environmental subspace projection matrix, construct a second Hankel matrix with the same dimension as the first Hankel matrix based on the second observation signal, and calculate the second Hankel matrix using the orthogonal complementary projection operator to obtain the co-occurrence signal; Step S4: Detect the symbiotic signal, recover the secondary signal, and perform coordinated control of the livestock equipment based on the secondary signal.

[0006] Preferably, step S1 specifically includes: The first observation signal is the superposition of the main signal reflected by the auxiliary probe array and the direct link signal. The second observation signal is the superposition of the main signal reflected by the co-occurring transmission array and modulated with the secondary signal, and the direct link signal. The mathematical expression for the first observation signal is: ; The mathematical expression for the second observed signal is: ; in, This is the first observation signal. This is the second observation signal. For symbolic periodic index, The direct link signal is the response signal for the direct link. Main signal, To assist in the detection array's reflection coefficient matrix, The reflection coefficient matrix of the co-existing transmission array. To assist in detecting the channel vector from the detector array to the receiver, Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the auxiliary detection array. This is the channel vector from the co-existing transport array to the receiver. Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the co-existing transport array. and These are the noise levels for their respective channels.

[0007] Preferably, step S2 specifically includes: The first observation signals from L consecutive time-time sampling points are combined into a first Hankel matrix, the dimension of which is... ,in is the row number of the first Hankel matrix; Perform singular value decomposition on the first Hankel matrix, and select the left singular vectors corresponding to the first r singular values ​​in descending order to form the data-driven subspace basis matrix. The dimension of the data-driven subspace basis matrix is... ; Based on the reflection coefficient matrix of the auxiliary detection array and the spatial array manifold, a physical prior structure matrix is ​​constructed. The construction logic of the physical prior structure matrix includes: multiplying the spatial array manifold by a fixed reflection coefficient matrix to obtain a spatial steering vector; and then performing Q-time translation expansion on this spatial steering vector according to the time delay structure of the first Hankel matrix to generate a dimension of... The matrix, where The number of reflective elements to assist in the detection array; The data-driven subspace basis matrix and the physical prior structure matrix are concatenated column-wise to obtain a matrix with dimension [missing information]. augmented matrix ,in For augmented matrix, For data-driven subspace basis matrices, Given the physical prior structure matrix, perform orthogonalization on the augmented matrix to obtain an orthogonal basis matrix; The mathematical expression for the projection matrix of the environment subspace is as follows: ; in, The projection matrix of the environment subspace. It is an orthogonal basis matrix.

[0008] Preferably, step S3 specifically includes: The mathematical expression for constructing the orthogonal complement projection operator is: ; in, For orthogonal complementary projection operators, For dimension The identity matrix; A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed by splicing the second observed signal, and a projection operation is performed to calculate the co-occurrence signal matrix, the mathematical expression of which is: ; in, For the co-occurrence signal matrix, This is the second Hankel matrix.

[0009] Preferably, step S4 specifically includes: Step S41: Perform singular value decomposition on the co-occurrence signal matrix to extract the co-occurrence signal subspace basis matrix, where the dimension of the co-occurrence signal subspace basis matrix is... , The effective multipath quantity of the co-occurring signal; Step S42: Based on the co-occurring signal subspace, estimate the equivalent steering matrix of the co-occurring reflection link using the rotation-invariant subspace technique ESPRIT; Step S43: Recover the secondary signal from the co-occurring signal matrix using the least squares method and the symbol decision rule.

[0010] Preferably, the rotation-invariant subspace technique ESPRIT specifically includes: The co-occurrence signal subspace basis matrix is ​​divided into blocks, which includes: extracting the first Q-1 rows of the co-occurrence signal subspace basis matrix to form the first submatrix, and extracting the last Q-1 rows of the co-occurrence signal subspace basis matrix to form the second submatrix; Based on the principle of rotation invariance, one dimension is extracted. The rotation matrix, wherein the mathematical expression of the rotation matrix satisfies the following conditions includes: ; in, This is the first submatrix. Let be a rotation matrix. This is the second submatrix; The rotation matrix is ​​solved using the least squares method. The rotation matrix is ​​then decomposed into eigenvalues ​​to obtain K eigenvalues. The equivalent steering matrix of the symbiotic link is reconstructed using these eigenvalues.

[0011] Preferably, step S43 specifically includes: The co-occurrence signal matrix processed in step S3 is restored to the corresponding co-occurrence signal vector. The specific restoration process includes: extracting each column of the co-occurrence signal matrix to form a column vector of dimension Q×1; using each column vector as the co-occurrence signal vector under the corresponding virtual snapshot; and modeling the co-occurrence signal vector, the mathematical expression of which is: ; in, Let n be the co-occurrence signal vector under the nth virtual snapshot. For the equivalent orientation matrix, For the equivalent emission symbol vector, Let n be the residual noise vector, and n be the column index of the co-occurring signal matrix. The value of n is in the range of [1, L-Q+1]. The equivalent emission symbol vector is solved using least squares estimation, and its mathematical expression is as follows: ; in, This is the equivalent emission symbol vector obtained from the least squares estimation; The influence of the main signal s(n) and the channel fading coefficient is eliminated from the equivalent transmitted symbol vector, and a soft decision value containing the secondary signal features is extracted. Based on the preset modulation constellation diagram of the subsystem, the soft decision value is hard-determined using the minimum Euclidean distance decision rule. The hard decision includes: if the real part of the soft decision value is greater than or equal to 0, then the sub-signal is determined to be... , corresponding to bit 1; If the real part of the soft decision value is less than 0, then the secondary signal is determined. , corresponding to bit 0; Output the complete sub-signal bit stream sequence after the judgment is completed.

[0012] A collaborative control system for livestock equipment based on wireless communication, the system being used to execute a collaborative control method for livestock equipment based on wireless communication, includes livestock equipment, a main system transmitter, a reconfigurable smart surface RIS module, a receiving and processing module, and a control module: The livestock equipment includes environmental monitoring sensors, automatic feeders, and temperature control equipment; The main system transmitter is used to send the main signal; The reconfigurable smart surface RIS module is a subsystem transmitter, which is physically divided into an auxiliary detection array and a co-transmission array. The auxiliary detection array is initialized with a fixed reflection coefficient matrix to reflect the main signal to form an environmental background signal. The co-occurring transmission array is configured with a reflection coefficient matrix that varies with the secondary signal, used to reflect the main signal and modulate the secondary signal; The receiving and processing module is used to control the co-transmission array to enter a silent state within a preset training time slot, so that the auxiliary detection array reflects the main signal and obtains the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; A first Hankel matrix is ​​constructed for the first observation signal, and the first Hankel matrix is ​​subjected to singular value decomposition to extract the data-driven subspace basis matrix. The known physical reflection coefficients of the auxiliary detection array are obtained to construct the physical prior structure matrix. The data-driven subspace basis matrix and the physical prior structure matrix are jointly orthogonalized to generate the environmental subspace projection matrix. An orthogonal complementary projection operator is constructed based on the environmental subspace projection matrix. A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed based on the second observation signal. The second Hankel matrix is ​​calculated using the orthogonal complementary projection operator to obtain the co-occurrence signal. The co-occurring signal is detected, and the secondary signal is recovered; The control module is used to generate control commands based on the recovered secondary signals to coordinate the control of livestock equipment.

[0013] Preferably, the receiving and processing module performs protocol layer parsing on the recovered sub-signal bit stream sequence. The parsing process includes: identifying the data frame header, performing cyclic redundancy check, removing erroneous frames and extracting the payload, converting the payload into physical quantity values, and obtaining livestock environment data. The livestock environment data includes ammonia concentration values, ambient temperature values, ambient humidity values ​​collected by sensors deployed in the pens, and livestock activity values ​​collected by accelerometers deployed on livestock collars. The control module generates control commands based on the parsed data and coordinates the control of livestock equipment. The coordinated control includes generating control commands based on livestock environment data and sending them to automatic feeders and temperature control equipment. These signals include data collected by environmental monitoring sensors. The generated control commands include temperature coordination control and feeding coordination control: Temperature coordination control includes: when the resolved ambient temperature value is greater than the preset high temperature threshold, generating an activation command and sending it to the temperature control device to drive the temperature control device to operate and reduce the temperature of the enclosure; When the ambient temperature drops to a preset suitable range, a shutdown command is generated; Feeding coordination control includes: when the analyzed livestock activity level is within a preset time window and continues to be greater than a preset activity threshold per unit time, and the current system time is within a preset feeding time period, the livestock is determined to be in an active feeding period, a feeding instruction including feeding weight parameters is generated, and sent to the automatic feeder, driving the stepper motor of the automatic feeder to feed according to the weight parameters.

[0014] Preferably, the physical partitioning ratio of the reconfigurable smart surface RIS module is configured with a dynamic adjustment mechanism, the total number of reflective units in the reconfigurable smart surface RIS module is N, and the number of reflective units in the auxiliary detection array is [missing information]. The number of reflection elements in the co-existing transmission array is And satisfy N= + ; The dynamic adjustment mechanism specifically includes: Obtain the signal-to-noise ratio of the main signal received by the main system receiver and the bit error rate of the secondary signal received by the secondary system receiver; When the signal-to-noise ratio (SNR) of the main signal received is less than a preset SNR safety threshold, the adjustment is performed according to a preset step size. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; When the signal-to-noise ratio (SNR) of the main signal received is greater than or equal to the SNR safety threshold, and the bit error rate (BER) of the secondary signal is greater than the preset BER tolerance threshold, the adjustment is performed according to the preset adjustment step size. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; and The adjustment is limited by the preset minimum number of array elements.

[0015] The beneficial effects of this invention are as follows: The RIS physical layer is divided into auxiliary detection and co-occurring transmission arrays. An innovative mechanism of bypass detection and main path stripping is proposed, utilizing the auxiliary array to obtain a clean background. Orthogonal projection is used to accurately strip strong direct waves and static interference, significantly improving the signal-to-interference-plus-noise ratio (SNR) of weak co-occurring signals. It overcomes the limitations of purely data-driven blind separation by using known RIS physical reflection coefficients as a priori structures and forcibly orthogonally fusing them with SVD decomposition results. Even at extremely low SNR, it can firmly anchor the true background interference direction. Based on the Hankel matrix, features are directly extracted from observation data without prior acquisition of Channel State Information (CSI), avoiding CSI inaccuracy issues at low SNR. The entire process involves only matrix decomposition and projection, without complex iterations, perfectly adapting to low-cost, low-power IoT nodes. Thanks to the introduction of deterministic physical priors, the system's dependence on the number of snapshots is drastically reduced. It can still reliably recover secondary signals even in harsh environments, completely eliminating the error rate plateau phenomenon of traditional methods and effectively ensuring precise and coordinated control of livestock equipment. Attached Figure Description

[0016] Figure 1 This is a basic flowchart illustrating a collaborative control method for livestock equipment based on wireless communication, provided as an embodiment of the present invention.

[0017] Figure 2 This is a basic flowchart of a collaborative control system for livestock equipment based on wireless communication, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Reference Figure 1 As an embodiment of the present invention, a collaborative control method for livestock equipment based on wireless communication is provided, comprising the following steps: Step S1: Within the preset training time slot, control the symbiotic transmission array to enter a silent state, and only allow the auxiliary detection array to reflect the main signal to obtain the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; Step S2: Construct a first Hankel matrix for the first observation signal, and extract the data-driven subspace basis matrix by performing singular value decomposition on the first Hankel matrix. Obtain the known physical reflection coefficients of the auxiliary detection array to construct the physical prior structure matrix. Perform joint orthogonalization processing on the data-driven subspace basis matrix and the physical prior structure matrix to generate the environmental subspace projection matrix. Step S3: Construct an orthogonal complementary projection operator based on the environmental subspace projection matrix, construct a second Hankel matrix with the same dimension as the first Hankel matrix based on the second observation signal, calculate the second Hankel matrix using the orthogonal complementary projection operator, and obtain the co-occurrence signal after removing strong background interference; Step S4: Detect the symbiotic signal, recover the secondary signal, and coordinate the control of livestock equipment based on the secondary signal.

[0020] This invention provides a collaborative control method and system for livestock equipment based on wireless communication. This scheme achieves effective removal of strong background interference and high-sensitivity extraction of weak symbiotic signals by physically dividing the RIS and performing cross-track orthogonal supplementary projection.

[0021] A collaborative control method for livestock equipment based on wireless communication is applied to a symbiotic wireless communication system based on a reconfigurable smart surface (RIS), where the RIS is physically divided into an auxiliary detection array and a symbiotic transmission array.

[0022] Step S1 specifically includes: The first observation signal is the superposition of the main signal reflected by the auxiliary probe array and the direct link signal. The second observation signal is the superposition of the main signal reflected by the co-occurring transmission array and modulated with the secondary signal, and the direct link signal. The mathematical expression for the first observation signal is: ; The mathematical expression for the second observed signal is: ; in, This is the first observation signal. This is the second observation signal. For symbolic periodic index, The direct link signal is the response signal for the direct link. Main signal, To assist in the detection array's reflection coefficient matrix, The reflection coefficient matrix of the co-existing transmission array. To assist in detecting the channel vector from the detector array to the receiver, Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the auxiliary detection array. This is the channel vector from the co-existing transport array to the receiver. Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the co-existing transport array. and These are the noise levels for their respective channels.

[0023] The reflection coefficient matrix of the auxiliary detector array is a diagonal matrix. Each element on the diagonal corresponds to the reflection coefficient of a reflection unit in the auxiliary detector array. The reflection coefficient includes amplitude attenuation and phase shift. It is directly configured by the RIS controller during initialization. The controller sends a command to the RIS to set all units of the auxiliary array to the reflection state.

[0024] The reflection coefficient matrix of the co-occurring transmission array is also a diagonal matrix, corresponding to the reflection unit of the co-occurring transmission array. It changes rapidly with the symbol period n and is driven by the secondary signal. For example, when the secondary signal is binary data 1, the controller sets the matrix to the first state with phase 0. When the signal is binary data 0, the controller sets the matrix to state A with phase 0, which is the second state with phase 180 degrees, which is the modulation process of backscatter communication.

[0025] The symbol period index n is a discrete-time counter. Communication is performed in steps, with each step representing a symbol number. The receiver and transmitter agree on the duration of each symbol (e.g., 1 microsecond). The symbol period index n represents the nth time step.

[0026] The static background signal reflected by the auxiliary array is the signal that the main signal hits the auxiliary array and is reflected to the receiver. The static background signal is the signal received by the receiver in the training time slot (when the co-occurring array is silent) minus the direct wave. Because the reflection coefficient matrix of the auxiliary detector array is fixed, the channel characteristics of the static background signal are static, and it constitutes the background noise in the received signal.

[0027] The secondary signal is the data that IoT devices want to send, which is collected by sensors and converted into machine code.

[0028] This invention employs a time-slot division mechanism to physically isolate the characteristics of two signal paths, thereby acquiring dual-path observation signals. During the training time slot, the control co-occurrence transmission array is in a silent state, which includes no reflection and no absorption. The auxiliary detection array reflects normally, and the receiving and processing module acquires the first observation signal. At this time, the signal includes the direct wave. The static background signal reflected by the auxiliary array, excluding secondary signals; During the data transmission time slot, both arrays operate simultaneously. The receiving and processing module acquires the second observation signal, which is superimposed with the direct wave, the auxiliary detection array, and the co-occurring transmission array modulated by the co-occurring signal.

[0029] The training time slot and the data transmission time slot are automatically switched by the RIS controller according to the factory-fixed timing sequence.

[0030] Step S2 specifically includes: The first observation signals from L consecutive time-time sampling points are combined into a first Hankel matrix, the dimension of which is... ,in is the row number of the first Hankel matrix; Perform singular value decomposition on the first Hankel matrix, and select the left singular vectors corresponding to the first r singular values ​​in descending order to form the data-driven subspace basis matrix. The dimension of the data-driven subspace basis matrix is... ; Based on the reflection coefficient matrix of the auxiliary detection array and the spatial array manifold, a physical prior structure matrix is ​​constructed. The construction logic of the physical prior structure matrix includes: multiplying the spatial array manifold by a fixed reflection coefficient matrix to obtain a spatial steering vector; and then performing Q-time translation expansion on this spatial steering vector according to the time delay structure of the first Hankel matrix to generate a dimension of... The matrix, where The number of reflective elements to assist in the detection array; By concatenating the data-driven subspace basis matrix with the physical prior structure matrix column by column, we obtain a matrix with dimension [missing information]. augmented matrix ,in For augmented matrices, For data-driven subspace basis matrices, Given the physical prior structure matrix, perform orthonormalization on the augmented matrix to obtain the orthogonal basis matrix; The mathematical expression for the projection matrix of the environment subspace is as follows: ; in, The projection matrix of the environment subspace. It is an orthogonal basis matrix.

[0031] In this embodiment, L is calculated by dividing the training time slot duration carried in the control signaling issued by the master system transmitter by the duration of a single symbol period. To ensure that the first Hankel matrix has sufficient rank to extract the subspace and does not exceed the coherence time for the channel to remain static, the value range of L is set to [20, 200] symbol periods, and the number of matrix rows Q satisfies Q ≤ L / 2.

[0032] The mathematical expression for singular value decomposition is: ; in, This is the first Hankel matrix. It is a left singular matrix. It is a singular value matrix. It is a right singular matrix. This represents the conjugate transpose of a matrix; The purpose of performing singular value decomposition on the first Hankel matrix is ​​to decompose the collected complex signal matrix and determine the spatial orientation of the signal. Energy level and changes over time By separating the elements and focusing only on the spatial directions with the highest energy, we can accurately locate and eliminate strong background interference.

[0033] The left singular matrix represents the spatial distribution direction of the signal. The direction with the highest energy is selected and assembled into a data-driven subspace basis matrix, which serves as the target for subtracting strong interference. The singular value matrix represents the signal energy contained in each spatial direction of the left singular matrix. The larger the singular value, the stronger the signal in that direction. The first few huge singular values ​​represent deafening direct wave interference, while the later tiny singular values ​​close to 0 represent only weak ambient noise. r represents the number of strong interference sources. The logic for determining strong interference sources is that signals with a strength greater than a preset signal strength threshold are considered strong interference sources. The preset signal strength threshold is obtained by calculating the receiver's noise floor power, and its mathematical expression is: ; in, To preset the signal strength threshold, For receiver thermal noise variance, As a margin for signal-to-noise ratio, in this embodiment, the range of the preset signal strength threshold is set to be between 10dB and 20dB higher than the receiver's noise floor power.

[0034] The right singular matrix represents the variation pattern of the signal in the time dimension; Choose a diagonal matrix The left singular vectors corresponding to the first r singular values ​​arranged in descending order constitute the basis matrix of the data-driven subspace, which represents the main interference direction observed from the current noisy data; Obtain the fixed reflection coefficient matrix pre-configured by the RIS controller for the auxiliary detection array, and construct the physical prior structure matrix in combination with the known spatial array manifold. This matrix represents the background reflection direction that the physical hardware will theoretically produce. The spatial array manifold is used to describe the physical phase difference vector of electromagnetic waves arriving at each reflector of the RIS. It is obtained by calculating the three-dimensional physical coordinates of each reflector in the RIS auxiliary detection array, the spacing between antenna elements, and the communication carrier wavelength using the geometric spatial projection formula.

[0035] The row representation of the physical prior structure matrix represents the Q time delay dimensions generated during the construction of the first Hankel matrix, i.e., the virtual antenna dimensions; The column represents the auxiliary detection array surface. One independent physical reflection unit; The physical prior structure matrix represents the background reflection direction that the physical hardware will theoretically produce.

[0036] The structurally constrained environmental subspace projection matrix not only contains the data characteristics of the real-time channel, but is also forced to be on the correct spatial trajectory by physical hardware parameters, thus possessing extremely strong noise resistance.

[0037] Step S2 uses the first observed signal to extract the feature subspace of strong background interference and depicts the noise profile. The environmental subspace projection matrix represents the signal space direction where the strong background interference signal is located. The core of this step is that it does not simply rely on the statistical characteristics of noisy data, but forcibly injects the known physical hardware parameters issued by the RIS controller into the mathematical space.

[0038] Step S3 specifically includes: The mathematical expression for constructing the orthogonal complement projection operator is: ; in, For orthogonal complementary projection operators, For dimension The identity matrix; A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed by splicing the second observed signal, and a projection operation is performed to calculate the co-occurrence signal matrix, the mathematical expression of which is: ; in, For the co-occurrence signal matrix, This is the second Hankel matrix.

[0039] Step S3 is used for cross-orbit orthogonal supplementary projection to deduct strong interference. After this step, the direct link signal and the background signal component reflected by the auxiliary detection array included in the co-occurrence signal matrix are located in the space spanned by the environmental subspace projection matrix and will be significantly suppressed by the orthogonal supplementary projection operator. However, the co-occurrence signal term is completely preserved because its signal subspace is different from the background subspace due to the modulation of the secondary signal.

[0040] Step S4 specifically includes: Step S41: Perform singular value decomposition on the co-occurrence signal matrix to extract the co-occurrence signal subspace basis matrix, where the dimension of the co-occurrence signal subspace basis matrix is... , The effective multipath quantity of the co-occurring signal; Step S42: Based on the co-occurring signal subspace, estimate the equivalent steering matrix of the co-occurring reflection link using the rotation-invariant subspace technique ESPRIT; Step S43: Recover the secondary signal from the co-occurring signal matrix using the least squares method and the symbol decision rule.

[0041] After orthogonal projection, strong direct waves and static background interference have been filtered out in the co-occurring signal matrix, mainly retaining the signal characteristics reflected from the co-occurring transmission array.

[0042] Following the process of performing singular value decomposition on the first Hankel matrix and obtaining the environment subspace projection matrix in step S2, singular value decomposition is performed on the co-occurrence signal matrix to extract the co-occurrence signal subspace basis matrix, wherein the dimension of the co-occurrence signal subspace basis matrix is... , The effective multipath quantity of the co-occurring signal is determined by selecting non-zero singular values ​​that are greater than a preset energy threshold and combining the left singular vectors corresponding to these non-zero singular values ​​to form the basis matrix of the co-occurring signal subspace.

[0043] The rotation-invariant subspace technique ESPRIT specifically includes: Based on the inherent shift-invariant structure of the Hankel matrix, the co-occurrence signal subspace basis matrix is ​​divided into blocks. The block division includes: extracting the first Q-1 rows of the co-occurrence signal subspace basis matrix to form the first submatrix, and extracting the last Q-1 rows of the co-occurrence signal subspace basis matrix to form the second submatrix. Based on the principle of rotation invariance, one dimension is extracted. The rotation matrix, and the mathematical expressions for the rotation matrix that satisfy the conditions include: ; in, This is the first submatrix. Let be a rotation matrix. This is the second submatrix; The rotation matrix is ​​solved using the least squares method. The rotation matrix is ​​then decomposed into eigenvalues ​​to obtain K eigenvalues. The equivalent steering matrix of the symbiotic link is reconstructed using these eigenvalues.

[0044] The mathematical expression for solving the rotation matrix using the least squares method is: ; Eigenvalues ​​are obtained by performing eigenvalue decomposition on the rotation matrix. ,in The eigenvalues ​​contain the angle and delay information of the co-occurring reflection link. Using the eigenvalues... The equivalent steering matrix of the symbiotic link is reconstructed, and the k-th column vector of the equivalent steering matrix is ​​represented as follows: .

[0045] Step S43 specifically includes: The co-occurrence signal matrix processed in step S3 is restored to the corresponding co-occurrence signal vector. The specific restoration process includes: extracting each column of the co-occurrence signal matrix to form a column vector of dimension Q×1; using each column vector as the co-occurrence signal vector under the corresponding virtual snapshot; and modeling the co-occurrence signal vector, the mathematical expression of which is: ; in, Let n be the co-occurrence signal vector under the nth virtual snapshot. For the equivalent orientation matrix, For the equivalent emission symbol vector, Let n be the residual noise vector, and n be the column index of the co-occurring signal matrix. The value of n is in the range of [1, L-Q+1]. The co-occurrence signal vector under the nth virtual snapshot is obtained by extracting the nth column of the processed co-occurrence signal matrix, representing the pure co-occurrence signal sample observed by the receiver at the nth time after removing strong background interference; Each column (steering vector) of the equivalent steering matrix represents the spatial response characteristics of a co-occurring signal multipath on the virtual array, i.e., from which direction or with what phase difference does the signal arrive at the receiver; The equivalent transmitted symbol vector has a dimension of K×1, which includes the complex signal amplitudes on all K effective multipaths at the nth time. The elements of this vector are the combination of the main signal, the secondary signal, and the channel fading coefficients of the corresponding multipath, that is, it includes the secondary signal information that needs to be recovered. The residual noise vector has a dimension of Q×1, representing the Gaussian white noise and minor interferences that remain in the signal after orthogonal projection processing.

[0046] The equivalent emission symbol vector is solved using least squares estimation, and its mathematical expression is as follows: ; in, This is the equivalent emission symbol vector obtained from the least squares estimation; The influence of the main signal s(n) and the channel fading coefficient is eliminated from the equivalent transmitted symbol vector, and a soft decision value containing the secondary signal features is extracted. Based on the preset modulation constellation diagram of the subsystem, the minimum Euclidean distance decision rule is used to perform hard decision on the soft decision value. The hard decision includes: if the real part of the soft decision value is greater than or equal to 0, then the sub-signal is determined to be... , corresponding to bit 1; If the real part of the soft decision value is less than 0, then the secondary signal is determined. , corresponding to bit 0; Output the complete sub-signal bit stream sequence after the judgment is completed.

[0047] Since the main signal s(n) is known to the receiver, the soft decision value containing the secondary signal characteristics can be extracted directly by eliminating the influence of the main signal s(n) and the channel fading coefficient.

[0048] The modulation constellation diagram includes binary phase shift keying (BPSK) modulation, with standard constellation points {+1, -1}.

[0049] A collaborative control system for livestock equipment based on wireless communication, the system being used to execute a collaborative control method for livestock equipment based on wireless communication, includes livestock equipment, a main system transmitter, a reconfigurable smart surface RIS module, a receiving and processing module, and a control module: Livestock equipment includes environmental monitoring sensors, automatic feeders, and temperature control equipment; The main system transmitter is used to send the main signal; The reconfigurable smart surface RIS module is the subsystem transmitter, which is physically divided into an auxiliary detection array and a co-transmission array. The auxiliary detection array is initialized with a fixed reflection coefficient matrix to reflect the main signal and form the environmental background signal. The co-occurring transmission array is configured with a reflection coefficient matrix that varies with the secondary signal, used to reflect the main signal and modulate the secondary signal; The receiving and processing module is used to control the co-existing transmission array to enter a silent state within a preset training time slot, so that only the auxiliary detection array reflects the main signal and obtains the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; A first Hankel matrix is ​​constructed for the first observation signal, and the first Hankel matrix is ​​subjected to singular value decomposition to extract the data-driven subspace basis matrix. The known physical reflection coefficients of the auxiliary detection array are obtained to construct the physical prior structure matrix. The data-driven subspace basis matrix and the physical prior structure matrix are jointly orthogonalized to generate the environmental subspace projection matrix. An orthogonal complementary projection operator is constructed based on the environmental subspace projection matrix. A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed based on the second observation signal. The second Hankel matrix is ​​calculated using the orthogonal complementary projection operator to obtain the co-occurrence signal after removing strong background interference. The symbiotic signal is detected, and the secondary signal is recovered; The control module is used to generate control commands based on the recovered secondary signals to coordinate the control of livestock equipment.

[0050] The receiving and processing module performs protocol layer parsing on the recovered sub-signal bit stream sequence. The parsing process includes: identifying the data frame header, performing cyclic redundancy check, removing erroneous frames and extracting the payload, converting the payload into physical quantity values, and acquiring livestock environment data. The livestock environment data includes ammonia concentration values, ambient temperature values, ambient humidity values ​​collected by sensors deployed in the pens, and livestock activity values ​​collected by accelerometers deployed on livestock collars. The control module generates control commands based on the parsed data and coordinates the control of livestock equipment. The coordinated control includes generating control commands based on livestock environment data and sending them to automatic feeders and temperature control equipment. These signals include data collected by environmental monitoring sensors. The generated control commands include temperature coordination control and feeding coordination control: Temperature coordination control includes: when the resolved ambient temperature value is greater than the preset high temperature threshold (28 degrees Celsius), generating an activation command and sending it to the temperature control equipment (exhaust fan and water curtain) to drive the temperature control equipment to operate and reduce the temperature of the enclosure; When the ambient temperature drops to a preset suitable range, a shutdown command is generated; Feeding coordination control includes: when the analyzed livestock activity level is within a preset time window and continues to be greater than a preset activity threshold per unit time, and the current system time is within a preset feeding time period, the livestock is determined to be in an active feeding period, a feeding instruction including feeding weight parameters is generated, and sent to the automatic feeder, driving the stepper motor of the automatic feeder to feed according to the weight parameters.

[0051] In this embodiment, the preset time window is obtained by the system administrator through the control terminal and stored in the memory of the control module. Its value range is set to [5, 30] minutes (for example, set to 10 minutes). The preset time window is used to statistically smooth the time interval of livestock activity data. Its function is to filter out the interference caused by occasional momentary movements of livestock (such as occasionally shaking their heads or swatting away mosquitoes) and prevent accidental triggering of feeding. The preset activity threshold is determined by pre-collecting accelerometer data from target livestock (such as cattle and sheep) under standard feeding conditions, calculating the variance of their triaxial acceleration vector sum, and taking the lower boundary of the statistical average as the threshold. For example, when activity levels are provided by a collar accelerometer, the activity threshold is set to an acceleration variance greater than 0.5g, where g is the standard gravitational acceleration. The preset feeding time periods are set by the farm administrator in the system clock task of the control module according to the feeding strategy. Specific examples are: set to the morning interval (06:00 to 08:00) and the evening interval (17:00 to 19:00) every day. Within a preset time window (10 minutes), the control module samples the analyzed livestock activity values ​​at a fixed frequency (e.g., once per minute). If more than 80% of the sampled values ​​are greater than the preset activity threshold, it is determined that the activity level is continuously greater than the preset activity threshold, thereby triggering the feeding command.

[0052] The physical partitioning ratio of the reconfigurable smart surface RIS module is configured with a dynamic adjustment mechanism. The total number of reflective elements in the reconfigurable smart surface RIS module is N, and the number of reflective elements in the auxiliary detection array is [missing information]. The number of reflection elements in the co-existing transmission array is And satisfy N= + ; The dynamic adjustment mechanism specifically includes: Obtain the signal-to-noise ratio of the main signal received by the main system receiver and the bit error rate of the secondary signal received by the secondary system receiver; When the signal-to-noise ratio (SNR) of the main signal received is less than the preset SNR safety threshold, the adjustment step size is set according to the preset value. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; When the signal-to-noise ratio (SNR) of the main signal received is greater than or equal to the SNR safety threshold, and the bit error rate (BER) of the secondary signal is greater than the preset BER tolerance threshold, the adjustment is performed according to the preset step size. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; and The adjustment is limited by the preset minimum number of array elements.

[0053] The dynamic adjustment mechanism is executed periodically by the control module according to a preset adjustment cycle (every 1000 symbol cycles). The specific execution logic includes: Obtain the signal-to-noise ratio (SNR) of the main signal fed back by the main system receiver and the bit error rate (BER) of the secondary signal as statistically obtained by the secondary system receiver within the current adjustment period; The received signal-to-noise ratio (SNR) of the main signal is compared with a preset SNR safety threshold. If the received SNR is less than the preset SNR safety threshold, the main system communication quality is deemed compromised. The control module then sends a reconfiguration command to the reconfigurable smart surface (RIS) module. The quantity is increased by a preset adjustment step. (For example =4), and at the same time The number decreased This operation aims to increase the area of ​​the auxiliary detection array, providing stronger direct wave suppression and main link reflection gain.

[0054] If the signal-to-noise ratio of the main signal received is greater than or equal to the preset signal-to-noise ratio safety threshold, that is, the communication quality of the main system has met the requirements, then the acquired secondary signal bit error rate will be compared with the preset bit error rate tolerance threshold. The system performs a comparison. If the bit error rate of the secondary signal exceeds the preset bit error rate tolerance threshold, the communication reliability of the secondary system is deemed insufficient. The control module then issues a reconfiguration command to the reconfigurable intelligent surface (RIS) module. Increase the number of steps to adjust At the same time The number decreased This operation aims to increase the area of ​​the symbiotic transmission array and enhance the reflection modulation energy of the sub-signal.

[0055] To prevent any array face from malfunctioning due to insufficient element count, a minimum array face element count constraint is forcibly introduced when performing the above addition and subtraction operations. This minimum array face element count constraint includes: the adjusted... It must be greater than or equal to the preset minimum number of auxiliary array elements, and the adjusted number of elements must be greater than or equal to the preset minimum number of auxiliary array elements. The number of elements must be greater than or equal to the preset minimum number of elements in the co-occurrence array, if calculated by step size. If the adjustment would exceed the minimum unit number constraint, then only adjust to the minimum unit number boundary value.

[0056] This invention overcomes the limitations of traditional pure data-driven blind separation methods by introducing the known and fixed reflection coefficient matrix of the RIS auxiliary detector array into the signal processing, constructing a structure-constrained projection operator. By forcibly orthogonally fusing the physical prior structure with the SVD subspace decomposition results, deterministic physical constraints are embedded in the mathematical projection. Even at extremely low signal-to-noise ratios (SNR), it can stably anchor the direction of real background interference and accurately separate strong direct waves and static reflection interference. The RIS array is divided into an auxiliary detector array and a co-occurring transmission array. The auxiliary array acquires clean environmental background information (direct wave + background reflection) to construct a structure-constrained environmental subspace; the main path signal achieves interference removal through orthogonal complementary projection. This bypass detection + main path removal mechanism significantly suppresses strong direct wave interference and greatly improves the SNR of weak co-occurring signals. Based on the Hankel matrix and subspace decomposition, environmental and signal features are directly extracted from the observation data without prior channel estimation, avoiding CSI inaccuracy at low SNR and improving system robustness. It mainly involves matrix factorization and projection operations, requiring no complex iterative optimization or high-dimensional channel estimation, resulting in low computational cost and suitability for low-cost, low-power IoT node deployment. By introducing deterministic physical structure priors, the dependence on the number of snapshots is significantly reduced. It can still stably extract the environmental subspace even at extremely low signal-to-noise ratios, avoiding the error rate plateau phenomenon of traditional methods, thus enabling highly reliable recovery of secondary signals and ensuring precise coordinated control of livestock equipment.

[0057] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A collaborative control method for livestock equipment based on wireless communication, characterized in that, Includes the following steps: Step S1: Within the preset training time slot, control the symbiotic transmission array to enter a silent state, and cause the auxiliary detection array to reflect the main signal to obtain the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; Step S2: Construct a first Hankel matrix for the first observation signal, and extract the data-driven subspace basis matrix by performing singular value decomposition on the first Hankel matrix. Obtain the known physical reflection coefficients of the auxiliary detection array to construct a physical prior structure matrix. Perform joint orthogonalization processing on the data-driven subspace basis matrix and the physical prior structure matrix to generate an environmental subspace projection matrix. Step S3: Construct an orthogonal complementary projection operator based on the environmental subspace projection matrix, construct a second Hankel matrix with the same dimension as the first Hankel matrix based on the second observation signal, and calculate the second Hankel matrix using the orthogonal complementary projection operator to obtain the co-occurrence signal; Step S4: Detect the symbiotic signal, recover the secondary signal, and perform coordinated control of the livestock equipment based on the secondary signal.

2. The collaborative control method for livestock equipment based on wireless communication as described in claim 1, characterized in that, Step S1 specifically includes: The first observation signal is the superposition of the main signal reflected by the auxiliary probe array and the direct link signal. The second observation signal is the superposition of the main signal reflected by the co-occurring transmission array and modulated with the secondary signal, and the direct link signal. The mathematical expression for the first observation signal is: ； The mathematical expression for the second observed signal is: ； in, This is the first observation signal. This is the second observation signal. For symbolic periodic index, The direct link signal is the response signal for the direct link. Main signal, To assist in the detection array's reflection coefficient matrix, The reflection coefficient matrix of the co-existing transmission array. To assist in detecting the channel vector from the detector array to the receiver, Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the auxiliary detection array. This is the channel vector from the co-existing transport array to the receiver. Indicates to Perform conjugate transpose. This is the channel vector from the transmitter to the co-existing transport array. and These are the noise levels for their respective channels.

3. The collaborative control method for livestock equipment based on wireless communication as described in claim 2, characterized in that, Step S2 specifically includes: The first observation signals from L consecutive time-time sampling points are combined into a first Hankel matrix, the dimension of which is... ,in is the row number of the first Hankel matrix; Perform singular value decomposition on the first Hankel matrix, and select the left singular vectors corresponding to the first r singular values ​​in descending order to form the data-driven subspace basis matrix. The dimension of the data-driven subspace basis matrix is... ; Based on the reflection coefficient matrix of the auxiliary detection array and the spatial array manifold, a physical prior structure matrix is ​​constructed. The construction logic of the physical prior structure matrix includes: multiplying the spatial array manifold by a fixed reflection coefficient matrix to obtain a spatial steering vector; and then extending this spatial steering vector by Q translations according to the time delay structure of the first Hankel matrix to generate a dimension of... The matrix, where The number of reflective elements to assist in the detection array; The data-driven subspace basis matrix and the physical prior structure matrix are concatenated column-wise to obtain a matrix with dimension [missing information]. augmented matrix ,in For augmented matrix, For data-driven subspace basis matrices, Given the physical prior structure matrix, perform orthogonalization on the augmented matrix to obtain an orthogonal basis matrix; The mathematical expression for the projection matrix of the environment subspace is as follows: ； in, The projection matrix of the environment subspace. It is an orthogonal basis matrix.

4. The collaborative control method for livestock equipment based on wireless communication as described in claim 3, characterized in that, Step S3 specifically includes: The mathematical expression for constructing the orthogonal complement projection operator is: ； in, For orthogonal complementary projection operators, For dimension The identity matrix; A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed by splicing the second observed signal, and a projection operation is performed to calculate the co-occurrence signal matrix, the mathematical expression of which is: ； in, For the co-occurrence signal matrix, This is the second Hankel matrix.

5. The collaborative control method for livestock equipment based on wireless communication as described in claim 4, characterized in that, Step S4 specifically includes: Step S41: Perform singular value decomposition on the co-occurrence signal matrix to extract the co-occurrence signal subspace basis matrix, where the dimension of the co-occurrence signal subspace basis matrix is... , The effective multipath quantity of the co-occurring signal; Step S42: Based on the co-occurring signal subspace, estimate the equivalent steering matrix of the co-occurring reflection link using the rotation-invariant subspace technique ESPRIT; Step S43: Recover the secondary signal from the co-occurring signal matrix using the least squares method and the symbol decision rule.

6. The collaborative control method for livestock equipment based on wireless communication as described in claim 5, characterized in that, The rotation-invariant subspace technique ESPRIT specifically includes: The co-occurrence signal subspace basis matrix is ​​divided into blocks, which includes: extracting the first Q-1 rows of the co-occurrence signal subspace basis matrix to form the first submatrix, and extracting the last Q-1 rows of the co-occurrence signal subspace basis matrix to form the second submatrix; Based on the principle of rotation invariance, one dimension is extracted. The rotation matrix, wherein the mathematical expression of the rotation matrix satisfies the following conditions includes: ; in, This is the first submatrix. Let be a rotation matrix. This is the second submatrix; The rotation matrix is ​​solved using the least squares method. The rotation matrix is ​​then decomposed into eigenvalues ​​to obtain K eigenvalues. The equivalent steering matrix of the symbiotic link is reconstructed using these eigenvalues.

7. The collaborative control method for livestock equipment based on wireless communication as described in claim 6, characterized in that, Step S43 specifically includes: The co-occurrence signal matrix processed in step S3 is restored to the corresponding co-occurrence signal vector. The specific restoration process includes: extracting each column of the co-occurrence signal matrix to form a column vector of dimension Q×1; using each column vector as the co-occurrence signal vector under the corresponding virtual snapshot; and modeling the co-occurrence signal vector, the mathematical expression of which is: ； in, Let n be the co-occurrence signal vector under the nth virtual snapshot. For the equivalent orientation matrix, For the equivalent emission symbol vector, Let n be the residual noise vector, and n be the column index of the co-occurring signal matrix. The value of n is in the range of [1, L-Q+1]. The equivalent emission symbol vector is solved using least squares estimation, and its mathematical expression is as follows: ； in, This is the equivalent emission symbol vector obtained from the least squares estimation; The influence of the main signal s(n) and the channel fading coefficient is eliminated from the equivalent transmitted symbol vector, and a soft decision value containing the secondary signal features is extracted. Based on the preset modulation constellation diagram of the subsystem, the soft decision value is hard-determined using the minimum Euclidean distance decision rule. The hard decision includes: if the real part of the soft decision value is greater than or equal to 0, then the sub-signal is determined to be... , corresponding to bit 1; If the real part of the soft decision value is less than 0, then the secondary signal is determined. , corresponding to bit 0; Output the complete sub-signal bit stream sequence after the judgment is completed.

8. A collaborative control system for livestock equipment based on wireless communication, the system being used to execute a collaborative control method for livestock equipment based on wireless communication, characterized in that, Includes livestock equipment, main system transmitter, reconfigurable smart surface RIS module, receiving and processing module, and control module: The livestock equipment includes environmental monitoring sensors, automatic feeders, and temperature control equipment; The main system transmitter is used to send the main signal; The reconfigurable smart surface RIS module is a subsystem transmitter, which is physically divided into an auxiliary detection array and a co-transmission array. The auxiliary detection array is initialized with a fixed reflection coefficient matrix to reflect the main signal to form an environmental background signal. The co-occurring transmission array is configured with a reflection coefficient matrix that varies with the secondary signal, used to reflect the main signal and modulate the secondary signal; The receiving and processing module is used to control the co-transmission array to enter a silent state within a preset training time slot, so that the auxiliary detection array reflects the main signal and obtains the first observation signal; During the data transmission time slot, the auxiliary detection array and the co-existing transmission array are operated simultaneously to acquire the second observation signal; A first Hankel matrix is ​​constructed for the first observation signal, and the first Hankel matrix is ​​subjected to singular value decomposition to extract the data-driven subspace basis matrix. The known physical reflection coefficients of the auxiliary detection array are obtained to construct the physical prior structure matrix. The data-driven subspace basis matrix and the physical prior structure matrix are jointly orthogonalized to generate the environmental subspace projection matrix. An orthogonal complementary projection operator is constructed based on the environmental subspace projection matrix. A second Hankel matrix with the same dimension as the first Hankel matrix is ​​constructed based on the second observation signal. The second Hankel matrix is ​​calculated using the orthogonal complementary projection operator to obtain the co-occurrence signal. The co-occurring signal is detected, and the secondary signal is recovered; The control module is used to generate control commands based on the recovered secondary signals to coordinate the control of livestock equipment.

9. The livestock equipment collaborative control system based on wireless communication as described in claim 8, characterized in that: The receiving and processing module performs protocol layer parsing on the recovered sub-signal bit stream sequence. The parsing process includes: identifying the data frame header, performing cyclic redundancy check, removing erroneous frames and extracting the payload, converting the payload into physical quantity values, and obtaining livestock environment data. The livestock environment data includes ammonia concentration values, ambient temperature values, ambient humidity values ​​collected by sensors deployed in the pens, and livestock activity values ​​collected by accelerometers deployed on livestock collars. The control module generates control commands based on the parsed data and coordinates the control of livestock equipment. The coordinated control includes generating control commands based on livestock environment data and sending them to automatic feeders and temperature control equipment. These signals include data collected by environmental monitoring sensors. The generated control commands include temperature coordination control and feeding coordination control: Temperature coordination control includes: when the resolved ambient temperature value is greater than the preset high temperature threshold, generating an activation command and sending it to the temperature control device to drive the temperature control device to operate and reduce the temperature of the enclosure; When the ambient temperature drops to a preset suitable range, a shutdown command is generated; Feeding coordination control includes: when the analyzed livestock activity level is within a preset time window and continues to be greater than a preset activity threshold per unit time, and the current system time is within a preset feeding time period, the livestock is determined to be in an active feeding period, a feeding instruction including feeding weight parameters is generated, and sent to the automatic feeder, driving the stepper motor of the automatic feeder to feed according to the weight parameters.

10. The livestock equipment collaborative control system based on wireless communication as described in claim 9, characterized in that: The physical partitioning ratio of the reconfigurable smart surface RIS module is configured with a dynamic adjustment mechanism. The total number of reflective elements in the reconfigurable smart surface RIS module is N, and the number of reflective elements in the auxiliary detection array is [missing information]. The number of reflection elements in the co-existing transmission array is And satisfy N= + ; The dynamic adjustment mechanism specifically includes: Obtain the signal-to-noise ratio of the main signal received by the main system receiver and the bit error rate of the secondary signal received by the secondary system receiver; When the signal-to-noise ratio (SNR) of the main signal received is less than a preset SNR safety threshold, the adjustment is performed according to a preset step size. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; When the signal-to-noise ratio (SNR) of the main signal received is greater than or equal to the SNR safety threshold, and the bit error rate (BER) of the secondary signal is greater than the preset BER tolerance threshold, the adjustment is performed according to the preset adjustment step size. Increase The quantity, and simultaneously reduce the amount by the same amount. Quantity; and The adjustment is limited by the preset minimum number of array elements.

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