Intelligent measurement and control terminal for multi-channel sensing signal synchronous acquisition

Abstract

The application discloses an intelligent measurement and control terminal for multi-channel sensing signal synchronous acquisition and relates to the technical field of data acquisition control. Inherent data of each sensor is acquired, and an ant colony algorithm and a genetic algorithm are initialized to obtain a blank ant colony population and a blank genetic population respectively; an adaptive threshold is calculated based on the inherent data and a filtering operation is performed to obtain an effective sensing data set; a local optimal acquisition scheme is updated based on the effective sensing data set, and a global optimal acquisition scheme is updated based on the local optimal acquisition scheme; and the global optimal acquisition scheme is sent to each sensor, so that each sensor receives and executes the global optimal acquisition scheme. The adaptive threshold eliminates repeated redundant sampling, reduces invalid data transmission and storage, releases cache bandwidth and shortens acquisition time delay; the double-algorithm optimization reduces channel time sequence deviation and improves synchronous acquisition accuracy; and the scheduling balances the acquisition load of each sensor, reduces data redundancy and improves acquisition efficiency.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and control technology, specifically to an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals. Background Technology

[0002] As the scale of monitoring in fields such as industrial monitoring, vehicle sensing, and drone inspection continues to expand, multi-channel sensor signal acquisition technology has gradually evolved from acquiring a small number of single-type channels to a collaborative acquisition mode with multiple measurement points and heterogeneous sensors. The early simple time-division acquisition architecture and single intelligent optimization algorithm have gradually exposed multiple inherent limitations.

[0003] Publication No. CN122085069A discloses a multi-channel synchronous signal acquisition system. The system includes a signal acquisition and storage module, a data polling module, a data processing module, and a data fusion module. Each signal acquisition channel alternately samples acoustic and temperature signals, and adds timestamps and channel identifiers. The central aggregation node of the data polling module polls and reads data from each channel, combining the timestamp-aligned samples into multi-dimensional array snapshots. A sound field energy distribution map is obtained by beamforming the acoustic array snapshot, and a temperature field distribution map is obtained by spatial interpolation of the temperature array snapshot. The two maps are then spatially aligned and fused to generate a GIS device status feature map that simultaneously contains acoustic and temperature information.

[0004] Existing traditional multi-channel measurement and control terminals suffer from data redundancy and repeated acquisition during the acquisition process. Multiple similar and adjacent sensors can easily generate a large amount of duplicate sampling data. The terminal lacks a redundancy discrimination mechanism, and the transmission and storage of all data will occupy the buffer and communication bandwidth, increase the power consumption of the device and lengthen the acquisition latency, resulting in low acquisition efficiency. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of data redundancy and repeated acquisition of multi-channel sensor signals mentioned in the background art, and to propose an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals.

[0006] In one aspect, the present invention provides an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals, the terminal comprising: The parameter initialization module is used to acquire the inherent data of each sensor and initialize the ant colony algorithm and the genetic algorithm to obtain the blank ant colony population and the blank genetic population, respectively. The threshold filtering module is used to calculate an adaptive threshold from the inherent data using the ITD intelligent threshold detector and perform filtering operations to obtain an effective sensor dataset. The scheme optimization module is used to update the blank ant colony population based on the effective sensor dataset to obtain the local optimal collection scheme, and to update the blank genetic population based on the local optimal collection scheme to obtain the global optimal collection scheme. The scheme sending module is used to send the globally optimal acquisition scheme to each sensor, so that each sensor can receive and execute the globally optimal acquisition scheme.

[0007] Adaptive thresholding filters redundant sampling beforehand, reducing invalid data transmission and storage, freeing up terminal cache and communication bandwidth, and compressing acquisition latency. Then, dual-algorithm collaborative optimization reduces channel timing differences, significantly improving the accuracy of multi-channel synchronous acquisition. Finally, dynamic scheduling balances the acquisition load of each sensor, reducing data redundancy from multiple dimensions and improving overall acquisition efficiency.

[0008] Optionally, the threshold filtering module includes: The data sampling module is used to determine the target sensor based on the inherent data, send a collection command to the target sensor so that the target sensor can receive the collection command, perform sampling to obtain the sampled data, and feed back the receiving time to the intelligent measurement and control terminal as the sampling request time. The sampling request time of each sensor is combined to obtain the total time of a complete multi-channel acquisition. The threshold calculation module is used to calculate an adaptive threshold based on the sampling request time and the total time of a complete multi-channel acquisition round, and to filter each sampled data according to the adaptive threshold to obtain an effective sensor dataset. Adaptive threshold calculation formula:

[0009] in, For adaptive threshold, The time taken for the sensor sampling request is represented by x, where x is the time sensitivity coefficient. This represents the total duration of a complete multi-channel data acquisition cycle.

[0010] The adaptive threshold formula generates dynamic judgment criteria in real time, synchronously calculates the proportion of repeated and redundant sampling, compares the sampling time of each channel to distinguish smooth channels from irregular channels with time drift and data jumps, directly discards invalid samples and marks abnormal measurement points, and can also dynamically adjust the sensor sleep wake-up cycle to reduce the volume of redundant data, release terminal cache and transmission resources, reduce the computing power overhead of back-end algorithms, and avoid interference from time-series abnormal data in subsequent acquisition time-series optimization work from the source.

[0011] Optionally, the threshold calculation module is further used to determine the total sampling time of a single channel of the target sensor. If the total sampling time of a single channel is less than or equal to the adaptive threshold, the target sensor is determined to be a smooth channel; otherwise, the target sensor is determined to be an irregular channel. After traversing each sensor, the sampling data corresponding to each smooth channel are merged to obtain an effective sensor dataset.

[0012] Optionally, the scheme optimization module is used to perform the following steps: Step 1: Take the effective sensor dataset as the collection object, determine the number of ants according to the total number of channels in the effective sensor dataset, initialize the pheromone matrix and assign pheromone to each channel; Step 2: Read the sampling frequency and spatial distance of each channel, calculate the sampling interval of each group of channels based on the sampling frequency and spatial distance of the measurement points, and calculate the attraction coefficient based on the sampling interval; Step 3: Establish a target acquisition cluster. Calculate the selection probability of each channel group using a probability formula. Randomly select a channel group based on the selection probability and add it to the target acquisition cluster until the target acquisition cluster reaches its upper limit. Close the target acquisition cluster and create a new blank acquisition cluster to continue allocating channels. When the effective sensor dataset is allocated, a single candidate solution is obtained. Combine the candidate solutions corresponding to each ant to obtain the initial candidate solution set. Step 4: Score each candidate solution in the initial candidate solution set, filter according to the score to obtain multiple high-quality solutions, calculate the pheromone increment according to each high-quality solution, update the initial pheromone matrix with the pheromone increment to obtain the global pheromone matrix, and output the local optimal acquisition solution when the preset condition is met.

[0013] The channel selection probability formula is used to optimally match sensor combinations. Combined with the acquisition frequency and the distance between measurement points, the data is re-clustered and parallelly acquired to reduce polling latency. The pheromone iterative update mechanism is used to enhance high-quality timing and attenuation degradation schemes. Multiple sets of locally optimal clustered timing solutions are continuously iterated and screened as the initial population of the genetic algorithm, which effectively reduces the synchronization time difference within the cluster, avoids the defect of being trapped in local optima by single optimization, and reduces the iterative pressure of subsequent global optimization.

[0014] Optionally, the selection probability of each channel group can be calculated using a probability formula, which includes:

[0015] in, To select the probability, Let α be the average pheromone concentration in channels i and j, α be the pheromone weight, and β be the channel distance weight. Let be the pheromone concentration in the channel at time t. Let be the pheromone concentration in the channel at time t. This is the attraction coefficient.

[0016] Optionally, the pheromone increment is used to update the initial pheromone matrix to obtain a global pheromone matrix, wherein the pheromone matrix is ​​updated in the following ways:

[0017] in, The pheromone concentration in generation t+1. This is the increment of the pheromone. The proportion of residual information, Let be the pheromone concentration in generation t.

[0018] Optionally, the scheme optimization module is further configured to replace the blank genetic population with the local optimal acquisition scheme to obtain an initial chromosome population, calculate the fitness score of each chromosome in the initial chromosome population using a fitness function, perform screening on each chromosome based on the fitness score to obtain multiple elite chromosomes, combine the elite individuals to obtain an elite population, perform an update operation on the elite population to obtain an optimal population, determine the optimal chromosome based on the fitness score of the optimal population, and determine the global optimal acquisition scheme based on the optimal chromosome.

[0019] The beneficial effects of this invention are: This invention proposes an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals. It eliminates redundant sampling through adaptive thresholding, reduces invalid data transmission and storage, releases buffer bandwidth, and shortens acquisition latency. Dual-algorithm optimization reduces channel timing deviation and improves synchronous acquisition accuracy. Scheduling balances the acquisition load of each sensor, reduces data redundancy, and improves acquisition efficiency. Attached Figure Description

[0020] Figure 1 A framework diagram of an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals provided in an embodiment of the present invention; Figure 2 This is a flowchart of an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals provided in an embodiment of the present invention. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] This invention provides an intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals. See also... Figure 1 The terminal includes: The parameter initialization module is used to acquire the inherent data of each sensor and initialize the ant colony algorithm and the genetic algorithm to obtain the blank ant colony population and the blank genetic population, respectively. The threshold filtering module is used to calculate an adaptive threshold from the inherent data using the ITD intelligent threshold detector and perform filtering operations to obtain an effective sensor dataset. The scheme optimization module is used to update the blank ant colony population based on the effective sensor dataset to obtain the local optimal collection scheme, and to update the blank genetic population based on the local optimal collection scheme to obtain the global optimal collection scheme. The scheme sending module is used to send the globally optimal acquisition scheme to each sensor, so that each sensor can receive and execute the globally optimal acquisition scheme.

[0023] In one implementation, the inherent parameters of the sensor are first collected to complete the dual-algorithm initialization. Then, the original sensor data is pre-filtered by the ITD intelligent threshold detector to remove redundant and time-series abnormal samples, and only the effective dataset is retained to participate in the optimization operation. This greatly reduces the data volume of the ant colony and genetic algorithm iterative calculations, reduces the computing power load of the embedded chip of the intelligent measurement and control terminal, avoids invalid data from interfering with the clustering and time-series optimization process, and ensures the iterative convergence speed of the two meta-heuristic algorithms from the source.

[0024] In one implementation, a two-stage progressive optimization architecture of ant colony local optimization and genetic global calibration is adopted. The local optimal clustering acquisition scheme is updated by updating the ant colony population output with an effective dataset. This scheme is then used as a high-quality initial solution to fill the genetic population to iteratively solve the global synchronous acquisition strategy. This approach overcomes the shortcomings of a single ant colony algorithm, which is prone to getting trapped in local optima, and the poor quality of the initial solution of a single genetic algorithm. It fully combines the advantages of local parallel clustering of ant colonies with the global temporal smoothing capability of genetic algorithms, thereby reducing the timing difference of multi-channel acquisition and improving the synchronous acquisition accuracy of multi-channel sensor signals.

[0025] In one implementation, the globally optimal acquisition scheme, solved by a two-level algorithm, is directly distributed to all sensors for execution. This scheme can adapt to heterogeneous sensor points with different sampling frequencies and spatial distributions. It can dynamically adjust the clustering and acquisition timing in real time based on the effective channels after front-end ITD filtering, and adaptively cope with complex field conditions such as measurement point movement and load fluctuations. It can stably achieve multi-channel synchronous acquisition, while balancing the acquisition load of each sensor and reducing the overall power consumption of the terminal.

[0026] In one implementation, the inherent data (hardware inherent parameters) includes: sampling frequency of each channel, acquisition interval, sensor power consumption, and channel buffer limit.

[0027] In one embodiment, the threshold filtering module includes: The data sampling module is used to determine the target sensor based on the inherent data, send a collection command to the target sensor so that the target sensor can receive the collection command and perform sampling to obtain the sampled data, and feed back the reception time to the intelligent measurement and control terminal as the sampling request time. The sampling request time of each sensor is combined to obtain the total time of a complete multi-channel acquisition. The threshold calculation module is used to calculate an adaptive threshold based on the sampling request time and the total time of a complete multi-channel acquisition round, and then filter each sampled data according to the adaptive threshold to obtain an effective sensor dataset. Adaptive threshold calculation formula:

[0028] in, For adaptive threshold, The time taken for the sensor sampling request is represented by x, where x is the time sensitivity coefficient. This represents the total duration of a complete multi-channel data acquisition cycle.

[0029] In one implementation, the real-time acquisition time parameter of the channel is substituted, and the judgment threshold for the current round is calculated in real time; the single-channel sampling time is compared with... : Time consumed ≤ : If determined to be a smooth channel, the sampling is valid and will be retained for subsequent timing planning; Time consumed > If a channel is identified as irregular (time drift, jump, or redundancy), redundant samples are discarded locally, and only the abnormal channel number is marked. Synchronously and dynamically adjust the sensor sleep-to-wake cycle: reduce the total amount of sampling input under low-demand conditions and reduce terminal computing power consumption.

[0030] In one implementation, the total acquisition time for a single round of full-channel acquisition is obtained by issuing acquisition commands, calculating the time consumed by each sensor's sampling request, and summarizing the results. An adaptive judgment threshold is then dynamically calculated using a time sensitivity coefficient. By comparing the sampling time of each channel, the system distinguishes between smooth and effective channels and irregular channels with timing drift and redundancy issues. Invalid sampling data is directly eliminated and abnormal channels are marked. At the same time, the sensor sleep-wake cycle is dynamically adjusted according to the working conditions. This can reduce redundant data from the source, release terminal cache and transmission bandwidth, avoid timing misalignment problems of multi-channel acquisition in advance, and reduce the computational pressure of back-end cluster timing optimization.

[0031] In one embodiment, the threshold calculation module is further used to determine the total sampling time of a single channel of the target sensor. If the total sampling time of a single channel is less than or equal to the adaptive threshold, the target sensor is determined to be a smooth channel; otherwise, the target sensor is determined to be an irregular channel. After traversing each sensor, the sampling data corresponding to each smooth channel are merged to obtain an effective sensor dataset.

[0032] In one implementation, the channel-by-channel comparison filtering mechanism uses an adaptive threshold as the criterion to distinguish between smooth channels and irregular channels. It retains the time-stable and non-redundant sampled data completely for subsequent clustering and time-series planning operations. Invalid samples with time-series drift, data jumps, or duplication are directly discarded locally. Only the abnormal channel number is recorded and it does not participate in subsequent optimization calculations. This can reduce the amount of computational data for backend ant colony and genetic algorithms, avoid abnormal sampling from interfering with the multi-channel time-series optimization process, reduce the sources of time-series errors in multi-channel synchronous acquisition from the front end, and save terminal cache and bus transmission resources, thereby reducing the overall computing power and power consumption.

[0033] In one embodiment, the scheme optimization module is used to perform the following steps: Step 1: Use the effective sensor dataset as the data collection object, determine the number of ants based on the total number of channels in the effective sensor dataset, initialize the pheromone matrix and assign pheromone to each channel. Step 2: Read the sampling frequency and spatial distance of each channel, calculate the sampling interval of each group of channels based on the sampling frequency and spatial distance of the measurement points, and calculate the attraction coefficient based on the sampling interval; Step 3: Establish a target acquisition cluster. Calculate the selection probability of each channel group using the probability formula. Randomly select a channel group based on the selection probability and add it to the target acquisition cluster until the target acquisition cluster reaches its upper limit. Close the target acquisition cluster and create a new blank acquisition cluster to continue allocating channels. When the effective sensor dataset is allocated, a single candidate solution is obtained. Combine the candidate solutions corresponding to each ant to obtain the initial candidate solution set. Step 4: Score each candidate solution in the initial candidate solution set, filter according to the score to obtain multiple high-quality solutions, calculate the pheromone increment according to each high-quality solution, update the initial pheromone matrix with the pheromone increment to obtain the global pheromone matrix, and output the local optimal acquisition solution when the preset condition is met.

[0034] In one implementation, see [link to implementation details]. Figure 2 The effective sensor dataset is used as the acquisition object: the smooth and effective channels after filtering are selected as the acquisition objects, and irregular and abnormal channels are directly excluded and do not participate in clustering; the number of ants is determined according to the total number of channels in the effective sensor dataset: after initializing the ant colony, the number of ants is set according to the total number of channels; the pheromone matrix is ​​initialized and pheromone is assigned to each channel: the global pheromone set is initialized, and the initial pheromone of all effective channels is uniformly assigned a base value.

[0035] In one implementation, the sampling frequency of each channel and the spatial distance between measurement points are read: the sampling frequency of each channel and the spatial distance between measurement points are read, and the acquisition interval of each group of channels is calculated. , ,in, f is the sampling interval. i f is the sampling frequency of the i-th channel sensor (in Hz, samples per second). j The sampling frequency (in Hz) of the j-th channel sensor is given; and the attraction coefficient is calculated. ,in, The sampling interval between channel i and channel j. This is the attraction coefficient.

[0036] In one implementation, a target acquisition cluster is established (the target acquisition cluster is a blank acquisition cluster, an acquisition cluster without assigned channels). The selection probability of each group of channels is calculated using a probability formula. Substituting into the probability formula, the selection probability of each group of channels to be paired in the current remaining channels is calculated. Based on the selection probability, a group of channels is randomly selected and added to the target acquisition cluster. Based on the probability, a group of channels with matching acquisition characteristics is randomly selected and added to the current acquisition cluster (target acquisition cluster), with similar sampling frequencies, small spatial distance between measurement points, and similar single-channel power consumption levels. In one implementation, the target acquisition cluster is closed and a new blank acquisition cluster is created to continue allocating channels until the target acquisition cluster reaches its upper limit: it is determined whether the number of channels in the current cluster has reached the cluster capacity limit. 1. If it has not exceeded the limit, new channels are added to the cluster. 2. If it has reached the upper limit, the current cluster is closed and a new blank acquisition cluster is created to continue allocating channels.

[0037] In one implementation, after the effective sensor dataset is allocated, a single candidate solution is obtained. The candidate solutions corresponding to each ant are combined to obtain an initial candidate solution set: all ants in the population traverse the allocation channel to generate multiple sets of differentiated clustering acquisition schemes.

[0038] In one implementation, each candidate solution in the initial candidate solution set is scored: the performance of each ant clustering scheme is scored, and the evaluation indicators are: intra-cluster synchronization time difference, cluster load balance, and total power consumption for data acquisition. , The overall score for generating clusters for a single ant. As the intra-cluster synchronization time difference weight, As cluster load balancing weight, To collect the total power consumption weight, The normalized intra-cluster synchronization time difference score (a positive indicator; the smaller the original time difference, the higher the score). This is the normalized load balancing score (a positive indicator; the more evenly the load is distributed, the higher the score). The normalized power consumption of the entire machine is a negative indicator; the higher the power consumption, the greater the deduction.

[0039] Multiple high-quality solutions are obtained by filtering based on scores: solutions with scores greater than the threshold in this round are selected; pheromone increments are calculated based on each high-quality solution: all channels in the high-quality solutions are paired, pheromone increments are calculated, and the pheromone increments are used to update the initial pheromone matrix to obtain the global pheromone matrix: the global pheromone matrix is ​​obtained by substituting into the pheromone update formula.

[0040] In one implementation, the local optimal acquisition scheme is output when a preset condition is met. The preset condition is: when the iteration limit is reached or the optimal scheme remains unchanged for multiple consecutive rounds, the iteration stops.

[0041] In one embodiment, the selection probability of each group of channels is calculated using a probability formula, which includes:

[0042] in, To select the probability, Let α be the average pheromone concentration in channels i and j, α be the pheromone weight, and β be the channel distance weight. Let be the pheromone concentration in the channel at time t. Let be the pheromone concentration in the channel at time t. This is the attraction coefficient.

[0043] In one implementation, The selection probability is the probability that the current ant will choose the combination of channels i and j; the pheromone weight is larger, and the higher the priority is given to historically high-quality collection combinations; the channel distance weight is larger, and the higher the priority is given to parallel collection of sensors with small collection intervals.

[0044] In one embodiment, the global pheromone matrix is ​​obtained by updating the initial pheromone matrix with pheromone increments. The updating methods for the pheromone matrix include:

[0045] in, The pheromone concentration in generation t+1. This is the increment of the pheromone. The proportion of residual information, Let be the pheromone concentration in generation t.

[0046] In one implementation, a high-quality channel combination is achieved by adding new pheromones, making it easier to be selected in the next round; a normal / low-quality channel combination is achieved by adding new pheromones to existing pheromones. Proportional evaporation decays, reducing the probability of subsequent selection; refreshes the initial pheromone matrix, completing one round of ant colony iteration; output: the updated global pheromone matrix.

[0047] In one embodiment, the scheme optimization module is further configured to replace the blank genetic population with the local optimal acquisition scheme to obtain an initial chromosome population, calculate the fitness score of each chromosome in the initial chromosome population using a fitness function, perform screening on each chromosome based on the fitness score to obtain multiple elite chromosomes, combine the elite individuals to obtain an elite population, perform an update operation on the elite population to obtain an optimal population, determine the optimal chromosome based on the fitness score of the optimal population, and determine the globally optimal acquisition scheme based on the optimal chromosome.

[0048] In one implementation, the fitness function is: Where F is the fitness score, W is the total baseline weight, W1 is the smooth channel weight, and W2 is the irregular channel weight.

[0049] In one implementation, the fitness function scores each chromosome and performs elite retention: the optimal sequence is directly inherited to the next generation; an update operation is performed on the elite population to obtain the optimal population, the update operation being adaptive crossover and mutation.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should still fall within the scope of the claims of the present invention.

Claims

1. An intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals, characterized in that, The terminal includes: The parameter initialization module is used to acquire the inherent data of each sensor and initialize the ant colony algorithm and the genetic algorithm to obtain the blank ant colony population and the blank genetic population, respectively. The threshold filtering module is used to calculate an adaptive threshold from the inherent data using the ITD intelligent threshold detector and perform filtering operations to obtain an effective sensor dataset. The scheme optimization module is used to update the blank ant colony population based on the effective sensor dataset to obtain the local optimal collection scheme, and to update the blank genetic population based on the local optimal collection scheme to obtain the global optimal collection scheme. The scheme sending module is used to send the globally optimal acquisition scheme to each sensor, so that each sensor can receive and execute the globally optimal acquisition scheme.

2. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 1, characterized in that, The threshold filtering module includes: The data sampling module is used to determine the target sensor based on the inherent data, send a collection command to the target sensor so that the target sensor can receive the collection command, perform sampling to obtain the sampled data, and feed back the receiving time to the intelligent measurement and control terminal as the sampling request time. The sampling request time of each sensor is combined to obtain the total time of a complete multi-channel acquisition. The threshold calculation module is used to calculate an adaptive threshold based on the sampling request time and the total time of a complete multi-channel acquisition round, and to filter each sampled data according to the adaptive threshold to obtain an effective sensor dataset. Adaptive threshold calculation formula: in, For adaptive threshold, The time taken for the sensor sampling request is represented by x, where x is the time sensitivity coefficient. This represents the total duration of a complete multi-channel data acquisition cycle.

3. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 2, characterized in that, The threshold calculation module is also used to determine the total sampling time of a single channel of the target sensor. If the total sampling time of a single channel is less than or equal to the adaptive threshold, the target sensor is determined to be a smooth channel; otherwise, the target sensor is determined to be an irregular channel. After traversing each sensor, the sampling data corresponding to each smooth channel are merged to obtain an effective sensor dataset.

4. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 1, characterized in that, The scheme optimization module is used to perform the following steps: Step 1: Take the effective sensor dataset as the collection object, determine the number of ants according to the total number of channels in the effective sensor dataset, initialize the pheromone matrix and assign pheromone to each channel; Step 2: Read the sampling frequency and spatial distance of each channel, calculate the sampling interval of each group of channels based on the sampling frequency and spatial distance of the measurement points, and calculate the attraction coefficient based on the sampling interval; Step 3: Establish a target acquisition cluster. Calculate the selection probability of each channel group using a probability formula. Randomly select a channel group based on the selection probability and add it to the target acquisition cluster until the target acquisition cluster reaches its upper limit. Close the target acquisition cluster and create a new blank acquisition cluster to continue allocating channels. When the effective sensor dataset is allocated, a single candidate solution is obtained. Combine the candidate solutions corresponding to each ant to obtain the initial candidate solution set. Step 4: Score each candidate solution in the initial candidate solution set, filter according to the score to obtain multiple high-quality solutions, calculate the pheromone increment according to each high-quality solution, update the initial pheromone matrix with the pheromone increment to obtain the global pheromone matrix, and output the local optimal acquisition solution when the preset condition is met.

5. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 4, characterized in that, The selection probability of each channel group is calculated using a probability formula, which includes: in, To select the probability, Let α be the average pheromone concentration in channels i and j, α be the pheromone weight, and β be the channel distance weight. Let be the pheromone concentration of the channel at time t. Let be the pheromone concentration in the channel at time t. This is the attraction coefficient.

6. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 4, characterized in that, The global pheromone matrix is ​​obtained by updating the initial pheromone matrix with the pheromone increment. The updating method of the pheromone matrix includes: in, The pheromone concentration in generation t+1. This is the increment of the pheromone. The proportion of residual information, Let be the pheromone concentration in generation t.

7. The intelligent measurement and control terminal for synchronous acquisition of multi-channel sensor signals according to claim 1, characterized in that, The scheme optimization module is further configured to replace the blank genetic population with the local optimal acquisition scheme to obtain an initial chromosome population, calculate the fitness score of each chromosome in the initial chromosome population using a fitness function, perform screening on each chromosome based on the fitness score to obtain multiple elite chromosomes, combine the elite individuals to obtain an elite population, perform an update operation on the elite population to obtain an optimal population, determine the optimal chromosome based on the fitness score of the optimal population, and determine the global optimal acquisition scheme based on the optimal chromosome.

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