A method and system for efficiently processing photoelectric sensor signal data

Abstract

The present invention discloses an efficient processing method and system for photoelectric sensor signal data, comprising the following steps: obtaining the optical signal collected by the photoelectric sensor and converting the optical signal into an electrical signal; performing a primary noise filtering on the electrical signal by using a distribution map method, performing a secondary noise filtering by using morphological filtering, and amplifying the filtered electrical signal to obtain a preprocessed electrical signal; performing analog-to-digital conversion on the preprocessed electrical signal to obtain a digital signal; performing lightweight processing on the digital signal by using a complex Fourier transform, and quickly storing and transmitting the lightweight digital signal based on data block and priority scheduling mechanism; performing signal feature extraction after receiving the lightweight digital signal, performing automatic anti-interference detection according to the extracted signal features, eliminating the interfered signal, and outputting a high-quality signal. The present invention realizes efficient and flexible processing of signal data.

Description

Technical Field

[0001] The present invention relates to the technical field of signal data processing, and in particular to a method and system for efficiently processing signal data of a photoelectric sensor. Background Art

[0002] Displacement refers to the offset of an object's position from a reference point. Displacement measurement includes linear displacement or angular displacement. Linear displacement refers to the change in the distance of an object relative to a point in a reference coordinate system. It is a physical quantity that describes the change in the spatial position of an object. If the displacement of an object moving in a straight line is called linear displacement, it is a physical quantity that describes the change in the linear position of an object in a plane. Linear displacement is basically the same as length measurement. People usually call a fixed measurement such as the length and diameter of an object length measurement, and the measurement of dimensions that change during work is called displacement measurement. Therefore, instruments for measuring length and displacement can often be used in common under certain conditions.

[0003] In recent years, high-precision and high-speed measurement of object displacement has received more and more attention, especially the measurement of small displacement in a narrow space. Therefore, there are more and more methods for displacement measurement using optical methods, and they are applied to photoelectric sensors.

[0004] However, no matter which optical method is used, the collected signal data needs to be processed, and the existing technologies mostly directly receive the analog signals sent from the sensor, and implement a series of processing such as filtering, amplification, phase locking, and interference elimination through hardware methods. As time goes by, the shortcomings of hardware in some aspects are gradually exposed. For example, once the circuit is fixed, it is difficult to change, it cannot be fine-tuned, and it is expensive. Therefore, the flexibility is poor and it is difficult to meet the efficient processing of signal data in different application scenarios. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides an efficient processing method and system for photoelectric sensor signal data, which is used to solve the technical problems of poor flexibility and low efficiency of existing signal data processing methods, and realize efficient and flexible processing of signal data.

[0006] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0007] An efficient method for processing photoelectric sensor signal data comprises the following steps:

[0008] Acquire the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal;

[0009] Performing a primary noise filtering on the electrical signal by using a distribution map method, performing a secondary noise filtering by using morphological filtering, and amplifying the filtered electrical signal to obtain a preprocessed electrical signal;

[0010] Performing analog-to-digital conversion on the preprocessed electrical signal to obtain a digital signal;

[0011] The digital signal is lightweight processed by complex Fourier transform, and the lightweight digital signal is quickly stored and transmitted based on data block and priority scheduling mechanism;

[0012] After receiving the lightweight digital signal, the signal feature is extracted, and automatic anti-interference detection is performed based on the extracted signal features to eliminate the interfered signal and output a high-quality signal.

[0013] As a preferred embodiment of the present invention, when a noise filtering is performed using a distribution graph method, it includes:

[0014] Converting the electrical signal into an ordered measurement sequence and obtaining a median value of the sequence;

[0015] Based on the median of the sequence, get the upper quartile of the sequence , the lower quartile of the sequence ;

[0016] According to the upper quartile of the sequence , the lower quartile of the sequence Get the quartile dispersion of the sequence , and determine the effective signal interval ;

[0017] Eliminate signals that are not in the valid signal range The electrical signal inside is obtained to obtain the initial filtered electrical signal.

[0018] As a preferred embodiment of the present invention, when morphological filtering is used for secondary noise filtering, it includes:

[0019] Obtaining a discrete function of the initial filtered electrical signal and structural elements ;

[0020] Get discrete function About Structural Elements Dilation and erosion, morphological opening and closing, morphological opening and closing, and morphological closing and opening operations;

[0021] The morphological opening and closing and the morphological closing and opening are cascaded to obtain a morphological filter, and the initial filtered electrical signal is secondary filtered using the morphological filter to obtain a filtered electrical signal.

[0022] As a preferred embodiment of the present invention, when the complex Fourier transform is used for lightweight processing, it includes:

[0023] Convert the digital signal into point real number sequence, and the Point real number sequences are divided into even number sequences and odd number sequences;

[0024] Taking the even and odd number sequences as the real and imaginary parts respectively, a complex number sequence of digital signals is obtained;

[0025] The complex sequence of digital signals is subjected to complex Fourier transform to obtain the lightweight digital signal.

[0026] As a preferred embodiment of the present invention, when fast storage is performed based on data blocks, it includes:

[0027] The secondary tag storage, access tag arbiter, composite controller, access storage arbiter, and secondary storage are built from top to bottom into a fast storage framework;

[0028] Storing the auxiliary information of the cache line in the fast storage framework through the secondary tag;

[0029] All access tag requests in the fast storage framework are arbitrated by the access tag arbitrator.

[0030] As a preferred embodiment of the present invention, when fast storage is performed based on data blocks, it also includes:

[0031] The composite controller performs consistency processing, missing processing, primary request processing and external memory request processing;

[0032] Complete bandwidth management memory access arbitration through the memory access arbitrator;

[0033] Storing the secondary data through the secondary storage;

[0034] Wherein, the secondary storage is a multi-block storage structure.

[0035] As a preferred embodiment of the present invention, when storing the secondary data through the secondary storage, it includes:

[0036] The first n and last n storage units of each row in the multi-block storage body are used as the even half row and odd half row in the cache line respectively;

[0037] For the even and odd half rows in each row, one access storage arbitrator is configured through an independent access storage interface, thereby obtaining 2m access storage arbitrators;

[0038] The lightweight digital signal is quickly stored by using the 2m access storage arbiters to simultaneously satisfy 2m non-conflicting access memory requests;

[0039] The multi-block storage body has m rows, and each row has 2n storage units.

[0040] As a preferred implementation mode of the present invention, when transmission is performed based on a priority scheduling mechanism, it includes:

[0041] Initialize parameters and locate at the backoff start boundary;

[0042] Determine whether it is within the preset backoff time slot interval. If so, randomly delay a number of complete backoff cycles;

[0043] When at the backoff period boundary, the node performs a clear channel assessment and evaluates whether the channel is idle;

[0044] If the channel is busy, determine whether a specific parameter meets a preset value. If so, reset the value of the specific parameter, and after the node is backed off for a number of backoff time slots, determine whether there is high-priority optical signal data. If so, the delivery is successful;

[0045] If the channel is idle, high priority nodes are allowed to access the channel.

[0046] As a preferred embodiment of the present invention, when performing automatic anti-interference detection, it includes:

[0047] Extract the mean, variance, standard deviation, peak value and skewness of the lightweight digital signal and integrate them to form the corresponding feature vector ;

[0048] The classifier is trained using training samples of the non-interfering signal and the interfered signal, and the feature vector Generate a decision function as shown in Formula 19:

[0049] (19);

[0050] In the formula, is the number of training samples, is the coefficient of the training sample, is the label of the training sample, is the sum function used to obtain the eigenvector and training samples The similarity between is the bias term;

[0051] A classifier based on the decision function is used to determine whether the lightweight digital signal is interfered with.

[0052] An efficient processing system for photoelectric sensor signal data, comprising:

[0053] Signal preprocessing module: used to obtain the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal; and to perform a primary noise filtering on the electrical signal using a distribution map method, perform a secondary noise filtering using morphological filtering, and amplify the filtered electrical signal to obtain a preprocessed electrical signal;

[0054] Data lightweight processing module: used for performing analog-to-digital conversion on the pre-processed electrical signal to obtain a digital signal, and performing lightweight processing on the digital signal using complex Fourier transform;

[0055] Data storage and transmission module: used to quickly store and transmit lightweight digital signals based on data block and priority scheduling mechanism;

[0056] Data anti-interference detection module: It is used to extract signal features after receiving the lightweight digital signal, perform automatic anti-interference detection based on the extracted signal features, eliminate the interfered signals, and output high-quality signals.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) The present invention shares some of the functions of the hardware through software methods to make up for the deficiencies caused by completely adopting hardware processing, improve the flexibility of signal data processing, and reduce costs;

[0059] (2) The present invention performs double filtering on the collected electrical signals, thereby fully filtering out large and small noises to obtain more effective digital signals, and then uses complex Fourier transform to achieve data lightweighting, optimize the speed of storage, transmission, feature extraction and anti-interference detection, and finally accurately detect and eliminate interfered signals based on multi-signal features, output high-quality signal data for subsequent measurement and analysis, thereby obtaining accurate measurement results;

[0060] (3) The present invention can eliminate large error data in real time and accurately, effectively avoiding the influence of large error data on measurement accuracy, and can also effectively suppress small noise, while having real-time performance;

[0061] (4) The present invention is based on data blocking and priority scheduling mechanisms, which can simultaneously satisfy multiple non-conflicting memory access requests, optimize the transmission efficiency and delay of signal data, and realize rapid storage and transmission of signal data.

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a step diagram of the efficient processing method of photoelectric sensor signal data provided by the present invention;

[0064] Figure 2 It is a schematic diagram of the composition of the fast storage framework provided by the present invention.

[0065] Explanation of the accompanying figures: 1. Secondary Tag storage; 2. Access Tag arbitrator; 3. Composite controller; 4. Access storage arbitrator; 5. Secondary storage. DETAILED DESCRIPTION

[0066] The present invention provides a highly efficient method for processing photoelectric sensor signal data, such as Figure 1 As shown, the following steps are included:

[0067] Step S1: Obtain the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal;

[0068] Step S2: performing a primary noise filtering on the electrical signal using a distribution map method, performing a secondary noise filtering using a morphological filter, and amplifying the filtered electrical signal to obtain a preprocessed electrical signal;

[0069] Step S3: converting the preprocessed electrical signal into digital form to obtain a digital signal;

[0070] Step S4: using complex Fourier transform to perform lightweight processing on the digital signal, and quickly storing and transmitting the lightweight digital signal based on data block and priority scheduling mechanism;

[0071] Step S5: After receiving the lightweight digital signal, signal feature extraction is performed, and automatic anti-interference detection is performed based on the extracted signal features to eliminate the interfered signal and output a high-quality signal.

[0072] Specifically, the present invention performs double filtering on the collected electrical signals to fully filter out large and small noises and obtain a more effective digital signal, and then uses complex Fourier transform to achieve data lightweighting, optimize the speed of storage, transmission, feature extraction and anti-interference detection, and finally achieve accurate detection and elimination of interfered signals based on multi-signal features, output high-quality signal data for subsequent measurement and analysis, thereby obtaining accurate measurement results.

[0073] In the above step S2, when the distribution graph method is used to perform a noise filtering, it includes:

[0074] Converts electrical signals into an ordered measurement sequence , and obtain the median value of the sequence from the measurement sequence, as shown in Formula 1:

[0075] (1);

[0076] Based on the median of the sequence, get the upper quartile of the sequence , the lower quartile of the sequence , as shown in Formula 2:

[0077] (2);

[0078] According to the upper quartile of the sequence , the lower quartile of the sequence Get the quartile dispersion of the sequence , as shown in Formula 3:

[0079] (3);

[0080] Based on the quartile dispersion of the series , the upper quartile of the sequence , the lower quartile of the sequence Determine the effective signal range , as shown in Formula 4:

[0081] (4);

[0082] In the formula, is a preset constant;

[0083] Eliminate signals that are not in the valid signal range The electrical signal inside is obtained to obtain the initial filtered electrical signal;

[0084] The data in the measurement sequence are arranged from small to large.

[0085] Specifically, the preset constant The size of depends on the measurement accuracy, and preferably, the value is 0.7 or 1.2.

[0086] The present invention converts the electrical signal into an ordered measurement sequence, and extracts the sequence median, the sequence upper quartile, and the sequence lower quartile therefrom, thereby obtaining the sequence quartile dispersion, and then determines the effective signal interval through the sequence quartile dispersion, filters the electrical signal, and removes large error data in real time and accurately, effectively avoiding the influence of large error data on measurement accuracy.

[0087] In the above step S2, when the morphological filtering is used for secondary noise filtering, it includes:

[0088] Get the discrete function of the initial filtered electrical signal and structural elements ;

[0089] Get discrete function About Structural Elements The expansion and erosion of are shown in Formula 5 and Formula 6:

[0090] (5);

[0091] (6);

[0092] In the formula, is the dilation operation, is the corrosion operation, For defining discrete functions The number of electrical signal data, To define structural elements The amount of electrical signal data;

[0093] Get discrete function About Structural Elements The morphological opening and closing operations are shown in Formula 7 and Formula 8:

[0094] (7);

[0095] (8);

[0096] In the formula, is the morphological opening operation, is the morphological closing operation;

[0097] Get discrete function About Structural Elements The morphological opening and closing operations and morphological closing and opening operations are shown in Formula 9 and Formula 10:

[0098] (9);

[0099] (10);

[0100] In the formula, is a discrete function About Structural Elements The morphological opening and closing operations, is a discrete function About Structural Elements The morphological closing and opening operation of ;

[0101] The morphological opening and closing and the morphological closing and opening are cascaded to obtain the morphological filter, as shown in Formula 11:

[0102] (11);

[0103] In the formula, is the morphological filter;

[0104] The initial filtered electrical signal is filtered twice using a morphological filter to obtain a filtered electrical signal;

[0105] Among them, the discrete function For definition in Discrete functions on , structural elements To define the interval range is a discrete function on , and .

[0106] Specifically, the present invention obtains the discrete function and structural element of the initial filtered electrical signal, performs dilation and erosion, morphological opening and closing operations, and morphological closing operations to obtain the morphological opening and closing, morphological closing and opening of the discrete function with respect to the structural element, and cascades them to obtain a morphological filter. Using the morphological filter for secondary filtering can not only effectively suppress small noise, but also has real-time performance.

[0107] In the above step S4, when the complex Fourier transform is used for lightweight processing, it includes:

[0108] Convert digital signals into point real number sequence, and Point real number sequences are divided into even number sequences and odd number sequences;

[0109] Taking the even number sequence as the real part and the odd number sequence as the imaginary part, we get the complex number sequence of the digital signal, as shown in Formula 12:

[0110] (12);

[0111] In the formula, is an imaginary unit, for point real number sequence, is a complex sequence of digital signals, The lengths of the point real number sequence and the digital signal complex number sequence are and , is an even-numbered sequence, is an odd sequence;

[0112] The complex sequence of the digital signal is subjected to a complex Fourier transform to obtain a lightweight digital signal, as shown in Formula 13:

[0113] (13);

[0114] In the formula, is an imaginary unit, is a natural constant, , It is a lightweight digital signal.

[0115] Specifically, the present invention converts the digital signal into a specific digital signal complex sequence (with an even sequence as the real part and an odd sequence as the imaginary part) and performs a complex Fourier transform on the digital signal complex sequence, thereby not only improving the data storage and transmission speed, but also greatly reducing the amount of calculation for subsequent automatic anti-interference detection.

[0116] In the above step S4, when fast storage is performed based on data blocks, Figure 2 As shown, including:

[0117] The secondary tag storage 1, the access tag arbiter 2, the composite controller 3, the access storage arbiter 4, and the secondary storage 5 are constructed from top to bottom into a fast storage framework;

[0118] The secondary tag storage 1 quickly stores the attached information of the cache line in the framework;

[0119] All access tag requests in the fast storage framework are arbitrated by access tag arbitrator 2;

[0120] The composite controller 3 performs consistency processing, missing processing, primary request processing and external memory request processing;

[0121] Bandwidth management memory access arbitration is completed by accessing the memory arbitrator 4;

[0122] Storing the secondary data via the secondary storage 5;

[0123] Among them, the secondary storage 5 is a multi-block storage structure.

[0124] Furthermore, when the secondary data is stored in the secondary storage 5, it includes:

[0125] The first n storage units in each row of the multi-block storage body are used as the even half of the cache line, and the last n storage units are used as the odd half of the cache line;

[0126] For the even half row and the odd half row in each row, an access storage arbiter 4 is configured through an independent access storage interface, thereby obtaining 2m access storage arbiters 4;

[0127] 2m access storage arbiters 4 are used to simultaneously satisfy 2m non-conflicting access storage requests, so as to quickly store the lightweight digital signal;

[0128] The multi-block storage body has m rows, and each row has 2n storage cells.

[0129] Specifically, the present invention realizes fast request processing and fast arbitration by building a fast storage framework, and realizes fast data storage by satisfying multiple non-conflicting memory access requests based on the multi-block storage structure of the secondary storage 5.

[0130] In the above step S4, when transmission is performed based on the priority scheduling mechanism, it includes:

[0131] Initialize the parameters and set the first parameter 0, the second parameter 2. The third parameter is 1 and is positioned at the backoff start boundary;

[0132] Determine whether it is within the preset backoff time slot interval If yes, then randomly delay A complete backoff cycle;

[0133] When the backoff slot is reduced to 0, the node performs a clear channel assessment and evaluates whether the channel is idle;

[0134] If the channel is busy, determine the second parameter and the third parameter Are they both 1? If so, set the third parameter Set to 0, the second parameter Set to 2, and after the node retreats a number of backoff time slots, determine whether there is high-priority optical signal data. If so, the delivery is successful;

[0135] If the channel is idle, high-priority nodes are allowed to access the channel;

[0136] in, Preset according to the actual required transmission efficiency, the first parameter The second parameter is the number of times the priority scheduling mechanism needs to back off when executing the current sending task. The third parameter is the contention window length. for Type parameter, backoff time slot is the backoff index.

[0137] In the above step S5, when performing signal feature extraction, it includes:

[0138] The mean, variance, standard deviation, peak value and skewness of the lightweight digital signal are extracted, as shown in formula 14-18:

[0139] (14);

[0140] (15);

[0141] (16);

[0142] (17);

[0143] (18);

[0144] In the formula, is the mean value of the digital signal, is the variance of the digital signal, is the standard deviation of the digital signal, is the peak value of the digital signal, is the skewness of the digital signal, is the number of digital signals after lightweighting, for The lightweight digital signal at the moment.

[0145] Specifically, the present invention optimizes the transmission efficiency and delay of signal data through a priority scheduling mechanism.

[0146] In the above step S5, when performing automatic anti-interference detection, it includes:

[0147] The mean, variance, standard deviation, peak value and skewness of the lightweight digital signal are integrated to form the corresponding feature vector ;

[0148] The classifier is trained using training samples of the non-interfering signal and the interfered signal, and the feature vector Generate a decision function as shown in Formula 19:

[0149] (19);

[0150] In the formula, is the number of training samples, is the coefficient of the training sample, is the label of the training sample, is the sum function used to obtain the eigenvector and training samples The similarity between is the bias term;

[0151] A classifier based on a decision function is used to determine whether the lightweight digital signal is interfered with.

[0152] Specifically, the present invention integrates the extracted multiple signal features to form a specific feature vector, and uses the feature vector to construct a classifier based on a decision function, so that accurate detection and elimination of interfered signals can be achieved based on the classifier, fully ensuring the accuracy of subsequent measurement results.

[0153] The efficient processing system of photoelectric sensor signal data provided by the present invention comprises: a signal preprocessing module, a data lightweight processing module, a data storage and transmission module, and a data anti-interference detection module;

[0154] Signal preprocessing module: used to obtain the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal; and to perform a primary noise filter on the electrical signal using a distribution map method, perform a secondary noise filter using morphological filtering, and amplify the filtered electrical signal to obtain a preprocessed electrical signal;

[0155] Data lightweight processing module: used to perform analog-to-digital conversion on the pre-processed electrical signal to obtain a digital signal, and perform lightweight processing on the digital signal using complex Fourier transform;

[0156] Data storage and transmission module: used to quickly store and transmit lightweight digital signals based on data block and priority scheduling mechanism;

[0157] Data anti-interference detection module: It is used to extract signal features after receiving the lightweight digital signal, perform automatic anti-interference detection based on the extracted signal features, eliminate the interfered signals, and output high-quality signals.

[0158] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. An efficient processing method for photoelectric sensor signal data, characterized in that: The following steps are involved: Acquire the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal; Performing a primary noise filtering on the electrical signal by using a distribution map method, performing a secondary noise filtering by using morphological filtering, and amplifying the filtered electrical signal to obtain a preprocessed electrical signal; Performing analog-to-digital conversion on the preprocessed electrical signal to obtain a digital signal; The digital signal is lightweight processed by complex Fourier transform, and the lightweight digital signal is quickly stored and transmitted based on data block and priority scheduling mechanism; After receiving the lightweight digital signal, the signal feature is extracted, and automatic anti-interference detection is performed based on the extracted signal features to remove the interfered signal and output a high-quality signal; Among them, when performing automatic anti-interference detection, it includes: Extract the mean, variance, standard deviation, peak value and skewness of the lightweight digital signal and integrate them to form the corresponding feature vector ; The classifier is trained using training samples of the non-interfering signal and the interfered signal, and the feature vector Generate a decision function as shown in Formula 19: （19）； In the formula, is the number of training samples, For the The coefficients of the training samples, For the The labels of the training samples, is the sum function used to obtain the eigenvector and training samples The similarity between is the bias term; A classifier based on the decision function is used to determine whether the lightweight digital signal is interfered with.

2. The efficient processing method of photoelectric sensor signal data according to claim 1, characterized in that: When using the distribution map method to perform a noise filtering, it includes: Converting the electrical signal into an ordered measurement sequence and obtaining a median value of the sequence; Based on the median of the sequence, get the upper quartile of the sequence , the lower quartile of the sequence ; According to the upper quartile of the sequence , the lower quartile of the sequence Get the quartile dispersion of the sequence , and determine the effective signal interval ; Eliminate signals that are not in the valid signal range The electrical signal inside is obtained to obtain the initial filtered electrical signal.

3. The efficient processing method of photoelectric sensor signal data according to claim 2, characterized in that: When using morphological filtering for secondary noise filtering, it includes: Obtaining a discrete function of the initial filtered electrical signal and structural elements ; Get discrete function About Structural Elements Dilation and erosion, morphological opening and closing, morphological opening and closing, and morphological closing and opening operations; The morphological opening and closing and the morphological closing and opening are cascaded to obtain a morphological filter, and the initial filtered electrical signal is secondary filtered using the morphological filter to obtain a filtered electrical signal.

4. The efficient processing method for photoelectric sensor signal data according to any one of claims 1 to 3, characterized in that: When using complex Fourier transform for lightweight processing, it includes: Convert the digital signal into point real number sequence, and the Point real number sequences are divided into even number sequences and odd number sequences; Taking the even and odd number sequences as the real and imaginary parts respectively, a complex number sequence of digital signals is obtained; The complex sequence of digital signals is subjected to complex Fourier transform to obtain the lightweight digital signal.

5. The efficient processing method of photoelectric sensor signal data according to claim 4, characterized in that: When fast storage is performed based on data blocks, it includes: The secondary tag storage, access tag arbiter, composite controller, access storage arbiter, and secondary storage are built from top to bottom into a fast storage framework; Storing the auxiliary information of the cache line in the fast storage framework through the secondary tag; All access tag requests in the fast storage framework are arbitrated by the access tag arbitrator.

6. The efficient processing method of photoelectric sensor signal data according to claim 5, characterized in that: When fast storage is performed based on data blocks, it also includes: The composite controller performs consistency processing, missing processing, primary request processing and external memory request processing; Complete bandwidth management memory access arbitration through the memory access arbitrator; Storing the secondary data through the secondary storage; Wherein, the secondary storage is a multi-block storage structure.

7. The efficient processing method of photoelectric sensor signal data according to claim 6, characterized in that: When the secondary data is stored by the secondary storage, it includes: The first n and last n storage units of each row in the multi-block storage body are used as the even half row and odd half row in the cache line respectively; For the even and odd half rows in each row, one access storage arbitrator is configured through an independent access storage interface, thereby obtaining 2m access storage arbitrators; The lightweight digital signal is quickly stored by using the 2m access storage arbiters to simultaneously satisfy 2m non-conflicting access memory requests; The multi-block storage body has m rows, and each row has 2n storage units.

8. The efficient processing method of photoelectric sensor signal data according to claim 7, characterized in that: When transmitting based on the priority scheduling mechanism, it includes: Initialize parameters and locate at the backoff start boundary; Determine whether it is within the preset backoff time slot interval. If so, randomly delay a number of complete backoff cycles; When at the backoff period boundary, the node performs a clear channel assessment and evaluates whether the channel is idle; If the channel is busy, determine whether a specific parameter meets a preset value. If so, reset the value of the specific parameter, and after the node is backed off for a number of backoff time slots, determine whether there is high-priority optical signal data. If so, the delivery is successful; If the channel is idle, high priority nodes are allowed to access the channel.

9. An efficient processing system for photoelectric sensor signal data, characterized in that: include: Signal preprocessing module: used to obtain the optical signal collected by the photoelectric sensor and convert the optical signal into an electrical signal; and performing a primary noise filtering on the electrical signal using a distribution map method, performing a secondary noise filtering using morphological filtering, and amplifying the filtered electrical signal to obtain a preprocessed electrical signal; Data lightweight processing module: used for performing analog-to-digital conversion on the pre-processed electrical signal to obtain a digital signal, and performing lightweight processing on the digital signal using complex Fourier transform; Data storage and transmission module: used to quickly store and transmit lightweight digital signals based on data block and priority scheduling mechanism; Data anti-interference detection module: It is used to extract signal features after receiving the lightweight digital signal, perform automatic anti-interference detection based on the extracted signal features, remove the interfered signal, and output high-quality signal; Among them, when performing automatic anti-interference detection, it includes: Extract the mean, variance, standard deviation, peak value and skewness of the lightweight digital signal and integrate them to form the corresponding feature vector ; The classifier is trained using training samples of the non-interfering signal and the interfered signal, and the feature vector Generate a decision function as shown in Formula 19: （19）； In the formula, is the number of training samples, For the The coefficients of the training samples, For the The labels of the training samples, is the sum function used to obtain the eigenvector and training samples The similarity between is the bias term; A classifier based on the decision function is used to determine whether the lightweight digital signal is interfered with.

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