Parameter processing device and method of ultrasonic bird repelling device

Abstract

The application discloses a parameter processing device and method of an ultrasonic bird repelling device, which calculates a clutter probability reference quantity of a parameter to be processed, a parameter difference quantity between the parameter to be processed and an adjacent group of parameters, a coherence quantity between the parameter to be processed and an extended parameter, and a coherence quantity between the parameter to be processed and an adjacent group of parameters, so as to accurately identify the probability that the parameter to be processed is clutter, identify reasonable parameters and clutter parameters according to the clutter probability reference quantity, and mark the clutter parameters with an identification code, which is used as a processing quantity of the group of parameters, so as to efficiently perform parameter selection on the group of parameters and identify the clutter parameters.

Description

Technical Field

[0001] This invention belongs to the field of ultrasonic bird deterrence technology, specifically relating to a parameter processing device and method for ultrasonic bird deterrence equipment. Background Technology

[0002] Ultrasonic bird deterrent devices utilize ultrasonic pulses from an ultrasonic generator to interfere with and disrupt the nervous and physiological systems of birds, causing physiological disturbances to achieve the ultimate goal of repelling or eliminating birds. The ultrasonic waves used in these devices possess characteristics such as inability to penetrate obstacles, strong directionality, and rapid attenuation. Therefore, a high-powered ultrasonic generator is used to propagate the waves through numerous reflections, forming an ultrasonic protective net covering the entire bird-repelling area to achieve the best deterrent effect. Research has shown that within a given space and with a certain "feeding capacity," killing one bird will result in the birth of another; therefore, "bird deterrence" is more beneficial than "bird killing." Furthermore, birds easily congregate on the crossarms of power poles, causing phase-to-phase short circuits and other faults, leading to large-scale power outages. Therefore, the advantages of ultrasonic generators in ultrasonic bird deterrent devices are currently being used to disperse bird flocks on the crossarms of power poles.

[0003] On the other hand, to achieve real-time sampling of the operating parameters of the ultrasonic generator of ultrasonic bird deterrence equipment, the current method is to sample the ultrasonic frequency, ultrasonic intensity and output power of the ultrasonic generator when it is working, and then transmit them to the back-end terminal for storage, so as to be backed up and viewed later.

[0004] Because the sensors that sample the ultrasonic frequency, ultrasonic intensity, and output power of the ultrasonic generator are different, these operating parameters are divided into several groups. These groups of parameters are the format and group of parameters sampled by three different sampling devices. By performing centralized parameter storage, processing, reading, and editing through these groups of parameters, it is easier to coordinate and process them. However, wireless transmission is often used during the transmission of these groups of parameters, which is often subject to noise interference, resulting in a lot of noise parameters in these groups of parameters and making the query workload of noise parameters too high.

[0005] Currently, clutter parameters are identified by determining the coherence between several groups of parameters and previous parameters using the Jaccard exponent method. However, this method often results in hypercoherence due to the inability to select an appropriate number of previous parameters, which increases the workload and reduces the accuracy of clutter parameter acquisition, thus making the processing of several groups of parameters less reliable. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a parameter processing device for ultrasonic bird deterrence equipment. By dividing several groups of parameters into several sets of parameters, analysis can be performed based on different groups of parameters. This prevents increased errors caused by analyzing parameters within multiple groups of parameters. By identifying the parameter to be processed and determining the parameter difference between the parameter to be processed and its adjacent set of parameters, the reasonableness of the parameter to be processed within its adjacent set of parameters can be efficiently determined. This facilitates efficient analysis of the parameter to be processed based on the parameter difference. The probability of wave parameters; by identifying the parameter to be processed and comparing it with the sample parameter items composed of previous parameters at the same time point, the parameter fluctuation between the parameter to be processed and the sample parameter items can be determined, thereby effectively preventing continuous clutter parameters from hindering clutter confirmation and improving the reliability of parameter processing; through the parameter coordinate system, and by performing dynamic time-point correction processing on the point to be processed and the sample point within the parameter coordinate system, the movement direction between the point to be processed and the sample point can be determined through the dynamic time-point correction processing. Then, based on the movement direction, the parameter fluctuation is added. This method efficiently identifies extended parameter items from the parameters to be executed, ensuring accurate identification and preventing excessive workload due to too many extended parameter items, while also preventing inefficient association due to too few elements. This ensures the rationality of the extended parameter items and the accuracy of the correlation between them and the parameter to be processed. Because it calculates the clutter probability benchmark of the parameter to be processed based on parameter differentiation and correlation, the parameter differentiation is the difference between the parameter to be processed and its adjacent set of parameters, representing the anomaly of the parameter itself. The state, coherence quantity, is the coherence quantity of the parameter to be processed and its extension parameter, which can be used to represent the unusual state of the parameter to be processed. Thus, the clutter probability benchmark quantity is obtained by calculation, which can accurately determine the probability that the parameter to be processed is clutter. Based on the clutter probability benchmark quantity, reasonable parameters and clutter parameters are identified, and the clutter parameters are marked with an identification code. The identification code is used as the processing quantity of several groups of parameters, thereby efficiently performing parameter selection on a group of parameters and identifying the clutter parameters here. In summary, this invention can efficiently improve the accuracy of clutter parameter acquisition and enhance the reliability of processing several groups of parameters.

[0007] The present invention employs the following technical solution.

[0008] A parameter processing method for an ultrasonic bird deterrent device includes:

[0009] An ultrasonic generator for bird deterrence, installed on the crossarm of a power pole, is activated to perform bird deterrence. Simultaneously, a frequency sampling device, an ultrasonic intensity meter, and a power sensor sample several groups of parameters during the operation of the ultrasonic generator, including ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator, and transmit these parameters to the controller. The ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator are each three parameters in the same group. The controller then transmits these parameters to the backend terminal for processing and storage.

[0010] The methods for handling actions performed by the background terminal specifically include:

[0011] Step 1: The backend terminal receives several groups of parameters, groups the parameters according to the parameter groups, treats parameters of the same group as a group of parameters, randomly selects a parameter from a group of parameters as the parameter to be processed, and determines the parameter difference between the parameter to be processed and the adjacent group of parameters.

[0012] Preferably, determining the parameter difference between the parameter to be processed and a set of adjacent parameters includes: determining a pre-defined set of two parameters adjacent to the parameter to be processed as adjacent parameters; calculating the modulus obtained by subtracting each adjacent parameter from the parameter to be processed as the adjacent difference; and calculating the standardized value of the sum obtained by adding all adjacent differences as the parameter difference.

[0013] Alternatively, it can be determined that the parameter to be processed and the mean of two pre-set adjacent parameters are used as the modulus of the parameter to be processed and the mean, and then the modulus of the parameter to be processed and the mean are calculated as the adjacent difference quantity.

[0014] Preferably, the equation for the parameter differentiation operation is:

[0015]

[0016] In the equation, g is the parameter distinguishing quantity, c is the pre-defined number of parameters, j is the sequence code of adjacent parameters, and E j Let E be the adjacency difference between the j-th adjacent parameter and the parameter to be processed. j =e0-e j e0 is the parameter to be processed, e j Let H be the j-th adjacent parameter, and H be the parameter within the parentheses following it. Implement standardization.

[0017] Preferably, after step 1, the procedure further includes:

[0018] Step 2: Take a set of adjacent parameters with the parameters to be processed, which is set in advance, as the parameter item to be processed. Obtain the previous parameters and identify the previous parameters at the same point in time as the parameters in the parameter item to be processed as sample parameters. Combine the sample parameters as sample parameter items and calculate the difference between the parameter item to be processed and the sample parameter items as the parameter fluctuation.

[0019] Step 3: Construct a parameter coordinate system, identify the points to be processed and sample points within the parameter coordinate system, perform dynamic time-point correction processing on the points to be processed and sample points, obtain the movement direction of the parameter items to be processed, extend the sample parameter items based on the movement direction and parameter fluctuation, obtain the extended parameter items, and determine the correlation between the extended parameter items and the parameter items to be processed.

[0020] Step 4: Based on the parameter differentiation quantity and coherence quantity, calculate the clutter probability benchmark quantity of the parameter to be processed, poll all groups of parameters, identify reasonable parameters and clutter parameters within a group of parameters based on the clutter probability benchmark quantity, note the clutter parameters to obtain identification codes, and use the identification codes as processing quantities for several groups of parameters.

[0021] Preferably, the difference between the parameter item to be processed in the operation and the sample parameter item is taken as the parameter fluctuation, including:

[0022] The modulus of the parameters in the parameter item to be processed and the parameters in the sample parameter item at the same time point are taken as the parameter difference quantity at the same time point; the standardized quantity of the sum obtained after adding all the parameter difference quantities at the same time point is taken as the parameter fluctuation quantity.

[0023] Preferably, the calculation equation for the parameter fluctuation is:

[0024]

[0025] Within the equation, Q represents the parameter fluctuation, n is a pre-defined number, k is the sequence code of the parameter in the parameter term to be processed, and V... k V represents the parameter difference between parameters within the same sample parameter at the same time point for the parameter to be processed in the k-th parameter item. k =|o k -l k |,o k For the parameter in the k-th parameter item to be processed, l k For the parameter in the sample parameter item that is the same as the parameter in the k-th parameter item to be processed, H is the parameter in the parentheses following it. Implement standardization.

[0026] Preferably, constructing a parametric coordinate system, and identifying the points to be processed and sample points within the parametric coordinate system, includes:

[0027] Construct a parameter coordinate system using the time point as the X-axis and the parameters as the Y-axis; associate the parameters in the parameter items to be processed with the parameter coordinate system in sequence to obtain the points to be processed; associate the parameters in the sample parameter items with the parameter coordinate system in sequence to obtain the sample points.

[0028] Preferably, performing a time-point correction process on the point to be processed and the sample point to obtain the movement direction of the parameter item to be processed includes:

[0029] Based on the Jaccard index calculation method, dynamic time-point correction is performed on the treatment points and sample points, and the related treatment points and sample points are identified as related parameter clusters. The difference between the corresponding time point of the treatment point on the X-axis and the corresponding time point of the sample point on the X-axis within the related parameter cluster is the X-axis subtraction. The sum of the X-axis subtractions of all related parameter clusters is taken as the shift factor. When the shift factor is not less than zero, the parameter item to be treated is shifted backward; when the shift factor is less than zero, the parameter item to be treated is shifted forward.

[0030] Preferably, based on the movement direction and parameter fluctuation amount, the sample parameter items are extended from the previous parameters to obtain extended parameter items, including: the product of the calculated parameter fluctuation amount and a pre-set number of parameters is used as the number of parameters to be extended; the parameters for which the number of parameters to be extended is obtained from the previous parameters according to the movement direction are used as the parameters to be extended; and the sample parameter items and the parameters to be extended are combined as extended parameter items.

[0031] The equation corresponding to the increased number of parameters is:

[0032] M = n * q

[0033] In the equation, M is the number of parameters to be extended, n is the number set in advance, and q is the parameter fluctuation.

[0034] Preferably, determining the correlation between the extended parameter item and the parameter item to be processed includes:

[0035] The minimum Jaccard exponent for the extended parameter term and the parameter term to be processed is determined based on the Jaccard exponent calculation method, and the standardized quantity of the minimum Jaccard exponent is taken as the coherent quantity.

[0036] Preferably, based on the parameter differentiation quantity and the coherence quantity, the clutter probability reference quantity of the parameter to be processed is calculated, including:

[0037] When the coherence quantity is higher than the pre-set coherence quantity threshold, the clutter probability reference quantity is considered to be zero; when the coherence quantity is not higher than the pre-set coherence quantity threshold, the standardized quantity of the quotient obtained by dividing the operational parameter difference quantity by the coherence quantity is used as the clutter probability reference quantity.

[0038] Preferably, the equation for calculating the clutter probability reference quantity is:

[0039]

[0040] In the equation, W is the clutter probability reference quantity, g is the parameter differentiation quantity, I is the coherence quantity, and H is the correlation coefficient within the parentheses following it. Implement standardization.

[0041] Preferably, reasonable parameters and clutter parameters are determined from a set of parameters based on a clutter probability benchmark, including:

[0042] Parameters whose clutter probability baseline is not higher than the pre-set probability threshold are considered reasonable parameters; parameters whose clutter probability baseline is higher than the pre-set probability threshold are considered clutter parameters.

[0043] A parameter processing device for an ultrasonic bird deterrent device, comprising:

[0044] The ultrasonic transmitter for ultrasonic bird deterrence, installed on the crossarm of the power pole, is equipped with a frequency sampling device, an ultrasonic intensity meter and a power sensor.

[0045] The wireless communication device, frequency sampling device, ultrasonic intensity meter, and power sensor are all connected to the controller. The controller communicates with the back-end terminal in the wireless network via the wireless communication device. The frequency sampling device, ultrasonic intensity meter, and power sensor are used to sample several groups of parameters such as ultrasonic frequency, ultrasonic intensity, and output power of the ultrasonic transmitter when it is working, and transmit them to the controller. The ultrasonic frequency, ultrasonic intensity, and output power of the ultrasonic transmitter are three parameters of the same group. The controller is used to transmit several groups of parameters to the back-end terminal for processing and storage.

[0046] Preferably, the module running on the background terminal includes:

[0047] The identification module is used to collect several groups of parameters, group the parameters according to the parameter groups, treat parameters of the same group as a group of parameters, randomly select a parameter from a group of parameters as the parameter to be processed, and identify the parameter difference between the parameter to be processed and the adjacent group of parameters.

[0048] The merging module is used to take a pre-set number of adjacent parameters with parameters to be processed as the parameter item to be processed, obtain the previous parameters, identify the previous parameters at the same point as the parameters in the parameter item to be processed as sample parameters, merge the sample parameters as sample parameter items, and calculate the difference between the parameter item to be processed and the sample parameter items as the parameter fluctuation.

[0049] The construction module is used to construct a parameter coordinate system, identify the points to be processed and sample points within the parameter coordinate system, perform dynamic time-point correction processing on the points to be processed and sample points, obtain the movement direction of the parameter item to be processed, perform extension on the sample parameter item based on the movement direction and parameter fluctuation amount, obtain the extended parameter item, and determine the correlation between the extended parameter item and the parameter item to be processed.

[0050] The annotation module is used to calculate the clutter probability benchmark quantity of the parameter to be processed based on the parameter differentiation quantity and the coherence quantity, poll all groups of parameters, identify reasonable parameters and clutter parameters within a group of parameters based on the clutter probability benchmark quantity, annotate the clutter parameters to obtain the identification code, and use the identification code as the processing quantity of several groups of parameters.

[0051] The beneficial effects of this invention are as follows: Compared with the prior art, this invention, by dividing several groups of parameters into several sets of parameters, can perform analysis based on parameters from different groups, thereby preventing the increase in parameter analysis errors caused by analyzing parameters within several groups of parameters. By identifying the parameter to be processed and determining the parameter difference between the parameter to be processed and the adjacent set of parameters, the rationality of the parameter to be processed among the adjacent set of parameters can be efficiently determined, thus facilitating the subsequent efficient analysis of the probability that the parameter to be processed is a clutter parameter based on the parameter difference. By identifying the parameter to be processed and comparing it with the sample parameter items composed of previous parameters at the same time point, the parameter fluctuation between the parameter to be processed and the sample parameter items can be determined, thereby effectively preventing continuous clutter parameters from hindering clutter confirmation and improving the reliability of parameter processing. By using the parameter coordinate system and performing dynamic time-point correction processing on the point to be processed and the sample point within the parameter coordinate system, the movement direction between the point to be processed and the sample point can be determined through the dynamic time-point correction processing. Then, based on the movement direction and the parameter fluctuation, the parameter to be processed can be efficiently determined. The extended parameter terms can be accurately identified, preventing excessive workload due to too many extended parameter terms and inefficient correlation due to too few elements. This ensures the rationality of the extended parameter terms and the accuracy of the correlation between them and the parameter to be processed. Because the clutter probability baseline of the parameter to be processed is calculated based on the parameter difference and correlation, the parameter difference is the difference between the parameter to be processed and its adjacent set of parameters, representing the abnormal state of the parameter itself. The dry quantity is the parameter to be processed and the coherence quantity with the extended parameter. It can be used to represent the unusual state of the parameter to be processed. Thus, the clutter probability benchmark quantity is obtained by calculation, which can accurately determine the probability that the parameter to be processed is clutter. Based on the clutter probability benchmark quantity, reasonable parameters and clutter parameters are identified. The clutter parameters are marked with an identification code. The identification code is used as the processing quantity of several groups of parameters. In this way, parameter selection is performed on a group of parameters to identify the clutter parameters. In summary, this invention can efficiently improve the accuracy of clutter parameter acquisition and enhance the reliability of processing several groups of parameters. Attached Figure Description

[0052] Figure 1 This is a partial flowchart of the parameter processing method for the ultrasonic bird deterrent device described in this invention;

[0053] Figure 2 This is a partial module structure diagram of the parameter processing device of the ultrasonic bird deterrent device described in this invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, any other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0055] like Figure 1 As shown, the parameter processing method for an ultrasonic bird deterrent device according to the present invention includes:

[0056] An ultrasonic generator for bird deterrence, installed on the crossarm of a power pole, is activated to perform bird deterrence. Simultaneously, a frequency sampling device, an ultrasonic intensity meter, and a power sensor sample several groups of parameters during the operation of the ultrasonic generator, including ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator, and transmit these parameters to the controller. The ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator are each three parameters in the same group. The controller then transmits these parameters to the backend terminal for processing and storage.

[0057] The methods for handling actions performed by the background terminal specifically include:

[0058] Step 1: The backend terminal receives several groups of parameters, groups the parameters according to the parameter groups, treats parameters of the same group as a group of parameters, randomly selects a parameter from a group of parameters as the parameter to be processed, and determines the parameter difference between the parameter to be processed and the adjacent group of parameters.

[0059] Here, parameters sampled by frequency sampling equipment, ultrasonic intensity meter and power sensor over a set time period can be used as several groups of parameters.

[0060] It is understandable that when obtaining several groups of parameters, they are obtained in a sequential order (which is the order of the sampling times). For example, the ultrasonic frequency, ultrasonic intensity, and output power of the ultrasonic generator during operation are sampled by a timed frequency sampling device, an ultrasonic intensity meter, and a power sensor, respectively, and used as several groups of parameters. Therefore, each of the sampled groups of parameters carries a corresponding time point message, which can be the sampling time point, etc., to facilitate the subsequent analysis of the several groups of parameters by adding the time point message.

[0061] Here, parameter processing can be performed on several groups of parameters according to the parameter group, treating parameters of the same group as a set of parameters.

[0062] Here, a set of parameters is any one of the ultrasonic frequency, ultrasonic intensity, and ultrasonic generator output power within several sets of parameters, and based on this, several sets of parameters are divided into several sets of parameters.

[0063] Here, a set of parameters can be serialized in sequence. Then, based on the sequenced parameters, a set of parameters at any point in time can be selected as the parameters to be processed. For example, the output power of an ultrasonic generator at 6 a.m. can be selected as the parameters to be processed.

[0064] In a preferred but non-limiting embodiment of the present invention, determining the parameter difference between the parameter to be processed and a group of adjacent parameters includes: identifying a pre-defined group of two parameters adjacent to the parameter to be processed as adjacent parameters; calculating the modulus obtained by subtracting each adjacent parameter from the parameter to be processed (this modulus is the amount obtained by subtracting the parameter with the smaller value from the parameter with the larger value among the adjacent parameters) as the adjacent difference; and calculating the standardized value of the sum of all adjacent difference values ​​as the parameter difference.

[0065] Alternatively, it can be determined that the parameter to be processed is the mean of two pre-set adjacent parameters. Then, the modulus of the parameter to be processed and the mean (the modulus is the quantity obtained by subtracting the smaller value from the larger value of the parameter to be processed and the mean) is calculated as the adjacent distinguishing quantity. The adjacent distinguishing quantity is used to identify the unusual state of the parameter to be processed among the adjacent set of parameters.

[0066] Here, the pre-set number two is the pre-set number of adjacent parameters. The pre-set number two can be ten, that is, the group of ten parameters adjacent to the parameter to be processed is taken as adjacent parameters. Here, the most recently pre-set group of two parameters adjacent to the parameter to be processed can be directly selected as adjacent parameters, or adjacent parameters can be selected by setting the previous adjacent or the next adjacent.

[0067] Since the parameters to be processed and the adjacent parameters all have corresponding parameters, such as the ultrasonic frequency, ultrasonic intensity, and output power of the ultrasonic generator when it is working, the modulus between the parameters to be processed and each adjacent parameter can be calculated as the adjacent difference quantity between the parameters to be processed and the adjacent parameters. Then, the sum of the obtained adjacent difference quantities is calculated, and the sum is standardized to obtain the parameter difference quantity.

[0068] In a preferred but non-limiting embodiment of the present invention, the parameter differentiation equation is:

[0069]

[0070] In the equation, g is the parameter distinguishing quantity, c is the pre-defined number of parameters, j is the sequence code of adjacent parameters, and E j Let E be the adjacency difference between the j-th adjacent parameter and the parameter to be processed. j =e0-e j e0 is the parameter to be processed, e j Let H be the j-th adjacent parameter, and H be the parameter within the parentheses following it. Implement standardization.

[0071] Here, the standardization method can use the BN algorithm. The BN algorithm can be used in all subsequent standardization processes of this method. Since the clutter parameter is quite different from the adjacent parameter, the parameter difference between the parameter to be processed and the adjacent parameter can be calculated. The higher the parameter difference, the higher the difference between the parameter to be processed and the adjacent parameter, and the higher the probability that the parameter to be processed is a clutter parameter.

[0072] In a preferred but non-limiting embodiment of the present invention, after step 1, the method further includes:

[0073] Step 2: Take a set of adjacent parameters with the parameters to be processed, which is set in advance, as the parameter item to be processed. Obtain the previous parameters and identify the previous parameters at the same point in time as the parameters in the parameter item to be processed as sample parameters. Combine the sample parameters as sample parameter items and calculate the difference between the parameter item to be processed and the sample parameter items as the parameter fluctuation.

[0074] Here, the first preset number is the number of parameters in a group that are adjacent to the parameter to be processed. The first preset number can be the same as or different from the second preset number. For example, the first preset number can be forty-eight. Then, the group of parameters adjacent to the first preset number that are adjacent to the parameter to be processed is merged as the parameter item to be processed.

[0075] Here, the previous parameters are the previous parameters corresponding to a set of parameters. For example, if a set of parameters is the ultrasonic frequency, ultrasonic intensity, and ultrasonic generator output power collected between 11:00 AM and 11:00 AM two minutes ago, then the corresponding previous parameters can be the ultrasonic frequency, ultrasonic intensity, and ultrasonic generator output power collected between 11:00 AM and 11:00 AM yesterday.

[0076] Here, the previous parameters at the same time as the parameters in the parameter item to be processed are identified as sample parameters. The same time point can be set based on the previous parameters. For example, if the previous parameters are a set of parameters from yesterday, then the corresponding same time point is within a different day. That is, if the parameter item to be processed is the corresponding time point from 8:00 to 12:00 on the current day, then the sample parameters of the same time point are a set of parameters obtained from 8:00 to 12:00 yesterday.

[0077] Here, sample parameters can be merged in order to form sample parameter items, which facilitates parameter parsing of the parameters to be processed based on the sample parameter items and the parameters to be processed later.

[0078] In a preferred but non-limiting embodiment of the present invention, the difference between the parameter item to be processed and the sample parameter item is taken as the parameter fluctuation quantity, including:

[0079] The modulus of the parameters in the parameter item to be processed and the parameters in the sample parameter item at the same time point are taken as the parameter difference quantity at the same time point; the standardized quantity of the sum obtained after adding all the parameter difference quantities at the same time point is taken as the parameter fluctuation quantity.

[0080] In a preferred but non-limiting embodiment of the present invention, that is, the parameters in the sample parameter item at the same time point as the parameters in the parameter item to be processed are identified. Because the time points corresponding to the parameter item to be processed and the sample parameter item are the same, and the number of parameters is the same, the parameters in the parameter item to be processed can be associated one-to-one with the parameters in the sample parameter item. Therefore, the modulus of the parameters associated at the same time point can be calculated as the parameter difference quantity at the same time point, and the standardized quantity obtained by adding all the parameter difference quantities at the same time point is used as the parameter fluctuation quantity. Here, the calculation equation for the parameter fluctuation quantity is:

[0081]

[0082] Within the equation, Q represents the parameter fluctuation, n is a pre-defined number, k is the sequence code of the parameter in the parameter term to be processed, and V... k V represents the parameter difference between parameters within the same sample parameter at the same time point for the parameter to be processed in the k-th parameter item. k =|o k -l k |,o k For the parameter in the k-th parameter item to be processed, l k For the parameter in the sample parameter item that is the same as the parameter in the k-th parameter item to be processed, H is the parameter in the parentheses following it. Implement standardization.

[0083] Therefore, the higher the parameter difference at the same point in time, the higher the difference between the parameter to be processed and the sample parameter at that point in time. The difference is often due to the shift of parameters in sequence, or it is often due to the continuous formation of several echoes in the parameter to be processed. Thus, when the difference between the parameter to be processed and the sample parameter is not low, the corresponding parameter fluctuation is higher.

[0084] Here, the sum of all parameters in the parameter item to be processed can be calculated as the sum of the parameters to be processed, and the sum of all parameters in the sample parameter item can be calculated as the sum of the sample parameters. The sum of the parameters to be processed minus the sum of the sample parameters is standardized and used as the parameter fluctuation.

[0085] Step 3: Construct a parameter coordinate system, identify the points to be processed and sample points within the parameter coordinate system, perform dynamic time-point correction processing on the points to be processed and sample points, obtain the movement direction of the parameter items to be processed, extend the sample parameter items based on the movement direction and parameter fluctuation, obtain the extended parameter items, and determine the correlation between the extended parameter items and the parameter items to be processed.

[0086] In a preferred but non-limiting embodiment of the present invention, constructing a parametric coordinate system and identifying the points to be processed and sample points within the parametric coordinate system includes:

[0087] Construct a parameter coordinate system using the time point as the X-axis and the parameters as the Y-axis; associate the parameters in the parameter items to be processed with the parameter coordinate system in sequence to obtain the points to be processed; associate the parameters in the sample parameter items with the parameter coordinate system in sequence to obtain the sample points.

[0088] Here, by constructing a parameter coordinate system, and then associating the parameters in the parameter item to be processed with the parameters in the sample parameter item with the parameter coordinate system, the difference between the parameters in the parameter item to be processed and the sample parameter item can be better perceived, which is beneficial for subsequent processing of the point to be processed and the sample point.

[0089] In a preferred but non-limiting embodiment of the present invention, performing a time-point correction process on the point to be processed and the sample point to obtain the movement direction of the parameter item to be processed includes:

[0090] Based on the Jaccard index calculation method, dynamic time-point correction is performed on the treatment points and sample points, and the related treatment points and sample points are identified as related parameter clusters. The difference between the corresponding time point of the treatment point on the X-axis and the corresponding time point of the sample point on the X-axis within the related parameter cluster is the X-axis subtraction. The sum of the X-axis subtractions of all related parameter clusters is taken as the shift factor. When the shift factor is not less than zero, the parameter item to be treated is shifted backward; when the shift factor is less than zero, the parameter item to be treated is shifted forward.

[0091] Here, the Jaccard exponent calculation method is a method for adjusting parameters in terms of sequence. The Jaccard exponent calculation method can identify similar quantities between two pairs of parameters within a set time interval. In this application, the Jaccard exponent calculation method is used to perform dynamic time-point correction processing on the treatment point and sample points (this processing includes: calculating the Jaccard exponent for the treatment point and sample points according to the Jaccard exponent calculation method; if the Jaccard exponent is higher than a pre-set limit, the treatment point and sample point are considered related, thus treating the related treatment point and sample point as a cluster of related parameters). After the dynamic time-point correction processing, the cluster of related parameters is determined. Therefore, sample points associated with the same treatment point often have different quantities in terms of sequence, allowing the corresponding time points of the related treatment points on the X-axis to be identified separately. The corresponding time point of a point on the X-axis is calculated, and the difference between the corresponding time points of two points on the X-axis is taken as the X-axis subtraction. The X-axis subtraction has states of being higher than zero, equal to zero, or less than zero. When the X-axis subtraction is higher than zero, the corresponding time point of the point to be processed on the X-axis is higher than the corresponding time point of the sample point on the X-axis. When the X-axis subtraction is lower than zero, the corresponding time point of the point to be processed on the X-axis is lower than the corresponding time point of the sample point on the X-axis. When the X-axis subtraction is zero, the corresponding time point of the point to be processed on the X-axis is the same as the corresponding time point of the sample point on the X-axis. Therefore, the sum of the X-axis subtractions of all related parameter clusters is taken as the shift factor, and the shift direction is determined based on whether the shift factor is lower than zero. When the shift factor is not lower than zero, the parameter item to be processed is considered to move backward; when the shift factor is lower than zero, the parameter item to be processed is considered to move forward.

[0092] In a preferred but non-limiting embodiment of the present invention, based on the movement direction and parameter fluctuation amount, the sample parameter items are extended from the previous parameters to obtain extended parameter items, including: multiplying the calculated parameter fluctuation amount by a pre-set number of parameters as the number of parameters to be extended; using the parameters for which the number of parameters to be extended is obtained from the previous parameters according to the movement direction as the parameters to be extended; and merging the sample parameter items and the parameters to be extended as the extended parameter items.

[0093] Here, the equations corresponding to the number of parameters need to be extended to:

[0094] M = n * q

[0095] In the equation, M is the number of parameters to be extended, n is the number set in advance, and q is the parameter fluctuation.

[0096] Here, because a higher parameter fluctuation means a higher parameter shift, the product of the parameter fluctuation and a pre-set number can be used as the number of parameters to be extended. Since the pre-set number is a pre-defined quantity, the number of parameters to be extended is positively correlated with the parameter fluctuation.

[0097] After determining the number of parameters to be extended, the parameters whose number of extensions is obtained from the previous parameters according to the movement direction can be used as the parameters to be extended. Here, because when performing parameter extension on the sample parameter item, the corresponding parameters to be extended are the parameters adjacent to the same sample parameter item, the parameters whose number of extensions is obtained from the previous parameters according to the movement direction can be used as the parameters to be extended. That is, when the parameter item to be processed moves backward, the parameters whose number of extensions is obtained after the sample parameter item (that is, according to the trend of increasing time points) can be used as the parameters to be extended. When the parameter item to be processed moves forward, the parameters whose number of extensions is obtained before the sample parameter item (that is, according to the trend of decreasing time points) can be used as the parameters to be extended. Thus, the parameters to be extended can be determined efficiently.

[0098] After obtaining the parameters to be extended, this application can perform a merging process in sequence to combine the sample parameter items with the parameters to be extended as extended parameter items, which is beneficial for determining the correlation between the extended parameter items and the parameter items to be processed based on the extended parameter items.

[0099] Naturally, within this application, based on the movement direction and parameter fluctuation amount, the sample parameter items are extended from the previous parameters. Furthermore, based on the magnitude of the parameter fluctuation amount and the pre-set number of parameters, an initial number of parameters to be extended is obtained. The parameters corresponding to the initial number of parameters to be extended from the previous parameters are added to the sample parameter items to form initial extended parameter items. Then, the parameter fluctuation amounts of the initial extended parameter items and the parameter items to be processed are calculated. When the parameter fluctuation amount meets the set threshold, the initial extended parameter item is used as an extended parameter item. When the parameter fluctuation amount does not meet the set threshold, the calculation of the number of parameters to be extended continues according to the obtained initial extended parameter items and the parameter fluctuation amounts of the parameter items to be processed, until the parameter fluctuation amount meets the threshold, and the final initial extended parameter item is sent out as an extended parameter item.

[0100] In a preferred but non-limiting embodiment of the present invention, determining the coherence between the extended parameter item and the parameter item to be processed includes:

[0101] The minimum Jaccard exponent for the extended parameter term and the parameter term to be processed is determined based on the Jaccard exponent calculation method, and the standardized quantity of the minimum Jaccard exponent is taken as the coherent quantity.

[0102] Here, the coherence quantity is the benchmark quantity of the correlation between the extended parameter item and the parameter item to be processed. Therefore, the minimum Jaccard index of the extended parameter item and the parameter item to be processed is determined by using the Jaccard index calculation method. This minimum Jaccard index can be the similarity quantity between the extended parameter item and the parameter item to be processed. The standardized quantity of the minimum Jaccard index is taken as the coherence quantity. Then, the higher the coherence quantity, the closer the extended parameter item and the parameter item to be processed are.

[0103] Step 4: Based on the parameter differentiation quantity and coherence quantity, calculate the clutter probability benchmark quantity of the parameter to be processed, poll all groups of parameters, identify reasonable parameters and clutter parameters within a group of parameters based on the clutter probability benchmark quantity, note the clutter parameters to obtain identification codes, and use the identification codes as processing quantities for several groups of parameters.

[0104] In a preferred but non-limiting embodiment of the present invention, the clutter probability reference quantity of the parameter to be processed is calculated based on the parameter differentiation quantity and the coherence quantity, including:

[0105] When the coherence quantity is higher than the pre-set coherence quantity threshold, the clutter probability reference quantity is considered to be zero; when the coherence quantity is not higher than the pre-set coherence quantity threshold, the standardized quantity of the quotient obtained by dividing the operational parameter difference quantity by the coherence quantity is used as the clutter probability reference quantity.

[0106] Here, the clutter probability reference quantity is the reference quantity for the probability that the parameter to be processed is a clutter parameter. The higher the clutter probability reference quantity, the higher the probability that the corresponding parameter to be processed is a clutter parameter.

[0107] In a preferred but non-limiting embodiment of the present invention, the pre-set coherence threshold is a pre-defined limit on the coherence quantity. For example, the pre-set coherence threshold could be 0.65. That is, when the coherence is higher than 0.65, the clutter probability reference quantity of the parameter to be processed is considered to be zero. In this case, it can be directly determined that the corresponding parameter to be processed is not a clutter parameter. When the coherence is not higher than 0.65, the product of the parameter difference and the coherence is used as the clutter probability reference quantity. The corresponding clutter probability reference quantity calculation equation is:

[0108]

[0109] In the equation, W is the clutter probability reference quantity, g is the parameter differentiation quantity, I is the coherence quantity, and H is the correlation coefficient within the parentheses following it. Implement standardization.

[0110] Because a higher parameter difference means a higher difference between the parameter to be processed and its adjacent parameters, the probability that the parameter to be processed is a clutter parameter is higher. In other words, the parameter difference and the clutter probability baseline are positively correlated. Since the coherence can be regarded as the similarity between the extended parameter and the parameter to be processed, the higher the coherence, the higher the similarity between the extended parameter and the parameter to be processed, and the lower the probability that the parameter to be processed within the parameter to be processed is clutter. Therefore, the coherence and the clutter probability baseline are negatively correlated.

[0111] In a preferred but non-limiting embodiment of the present invention, reasonable parameters and clutter parameters are determined from a set of parameters based on a clutter probability reference quantity, including:

[0112] Parameters whose clutter probability baseline is not higher than the pre-set probability threshold are considered reasonable parameters; parameters whose clutter probability baseline is higher than the pre-set probability threshold are considered clutter parameters.

[0113] Here, the pre-set probability threshold is a limit on the clutter probability reference value. When the clutter probability reference value is not higher than the pre-set probability threshold value, the corresponding parameters to be handled can be treated as reasonable parameters. When the clutter probability reference value is higher than the pre-set probability threshold value, the corresponding parameters to be handled can be treated as clutter parameters. The pre-set probability threshold value can be 0.88.

[0114] Therefore, by performing analysis on the clutter probability benchmark, the probability of the parameter to be processed is determined to be the clutter parameter, thereby ensuring the efficient selection of clutter parameters.

[0115] Here, the clutter parameters are annotated to obtain identification codes. These identification codes are used as processing quantities for several groups of parameters. After detecting clutter parameters, pre-defined identifiers are used to annotate the clutter parameters, which facilitates subsequent investigation and removal of the annotated clutter parameters. Thus, by using the identification codes as processing quantities for several groups of parameters, clutter parameters within several groups can be efficiently identified, ensuring accurate identification of clutter parameters.

[0116] like Figure 2 As shown, the parameter processing device for an ultrasonic bird deterrent device according to the present invention includes:

[0117] An ultrasonic transmitter for bird deterrence, mounted on the crossarm of a power pole, is equipped with a frequency sampling device, an ultrasonic intensity meter, and a power sensor. Specifically, it has several parameter groups, each containing three parameters from the same group: one group for ultrasonic frequency, another for ultrasonic intensity, and the remaining group for the transmitter's output power. The frequency sampling device includes an ultrasonic sensor and a connected signal converter. The ultrasonic sensor converts the received ultrasonic signal into an electrical signal, which is then transmitted to the signal converter. The signal converter converts the electrical signal into a digital frequency signal representing the ultrasonic frequency at which the transmitter operates.

[0118] The wireless communication device, frequency sampling device, ultrasonic intensity meter, and power sensor are all connected to the controller. The controller communicates with the back-end terminal within the wireless network via the wireless communication device. The frequency sampling device, ultrasonic intensity meter, and power sensor are used to sample several sets of parameters during the operation of the ultrasonic transmitter, such as ultrasonic frequency, ultrasonic intensity, and output power, and transmit them to the controller. The ultrasonic frequency, ultrasonic intensity, and output power are three parameters of the same category. The controller transmits these parameters to the back-end terminal for processing and storage. The controller can be a microcontroller or PLC, the wireless communication device can be a 4G module, the wireless network can be a 4G network, and the back-end terminal can be a computer or laptop connected to the 4G network.

[0119] In a preferred but non-limiting embodiment of the present invention, the module running on the background terminal includes:

[0120] The identification module is used to collect several groups of parameters, group the parameters according to the parameter groups, treat parameters of the same group as a group of parameters, randomly select a parameter from a group of parameters as the parameter to be processed, and identify the parameter difference between the parameter to be processed and the adjacent group of parameters.

[0121] The merging module is used to take a pre-set number of adjacent parameters with parameters to be processed as the parameter item to be processed, obtain the previous parameters, identify the previous parameters at the same point as the parameters in the parameter item to be processed as sample parameters, merge the sample parameters as sample parameter items, and calculate the difference between the parameter item to be processed and the sample parameter items as the parameter fluctuation.

[0122] The construction module is used to construct a parameter coordinate system, identify the points to be processed and sample points within the parameter coordinate system, perform dynamic time-point correction processing on the points to be processed and sample points, obtain the movement direction of the parameter item to be processed, perform extension on the sample parameter item based on the movement direction and parameter fluctuation amount, obtain the extended parameter item, and determine the correlation between the extended parameter item and the parameter item to be processed.

[0123] The annotation module is used to calculate the clutter probability benchmark quantity of the parameter to be processed based on the parameter differentiation quantity and the coherence quantity, poll all groups of parameters, identify reasonable parameters and clutter parameters within a group of parameters based on the clutter probability benchmark quantity, annotate the clutter parameters to obtain the identification code, and use the identification code as the processing quantity of several groups of parameters.

[0124] The beneficial effects of this invention are as follows: Compared with the prior art, this invention, by dividing several groups of parameters into several sets of parameters, can perform analysis based on parameters from different groups, thereby preventing the increase in parameter analysis errors caused by analyzing parameters within several groups of parameters. By identifying the parameter to be processed and determining the parameter difference between the parameter to be processed and the adjacent set of parameters, the rationality of the parameter to be processed among the adjacent set of parameters can be efficiently determined, thus facilitating the subsequent efficient analysis of the probability that the parameter to be processed is a clutter parameter based on the parameter difference. By identifying the parameter to be processed and comparing it with the sample parameter items composed of previous parameters at the same time point, the parameter fluctuation between the parameter to be processed and the sample parameter items can be determined, thereby effectively preventing continuous clutter parameters from hindering clutter confirmation and improving the reliability of parameter processing. By using the parameter coordinate system and performing dynamic time-point correction processing on the point to be processed and the sample point within the parameter coordinate system, the movement direction between the point to be processed and the sample point can be determined through the dynamic time-point correction processing. Then, based on the movement direction and the parameter fluctuation, the parameter to be processed can be efficiently determined. The extended parameter terms can be accurately identified, preventing excessive workload due to too many extended parameter terms and inefficient correlation due to too few elements. This ensures the rationality of the extended parameter terms and the accuracy of the correlation between them and the parameter to be processed. Because the clutter probability baseline of the parameter to be processed is calculated based on the parameter difference and correlation, the parameter difference is the difference between the parameter to be processed and its adjacent set of parameters, representing the abnormal state of the parameter itself. The dry quantity is the parameter to be processed and the coherence quantity with the extended parameter. It can be used to represent the unusual state of the parameter to be processed. Thus, the clutter probability benchmark quantity is obtained by calculation, which can accurately determine the probability that the parameter to be processed is clutter. Based on the clutter probability benchmark quantity, reasonable parameters and clutter parameters are identified. The clutter parameters are marked with an identification code. The identification code is used as the processing quantity of several groups of parameters. In this way, parameter selection is performed on a group of parameters to identify the clutter parameters. In summary, this invention can efficiently improve the accuracy of clutter parameter acquisition and enhance the reliability of processing several groups of parameters.

[0125] This disclosure may be a system, method, and / or computer program product. A computer program product may include a computer-readable appendix having computer-readable program instructions loaded thereon for causing a processor to achieve each aspect disclosed herein.

[0126] Computer-readable printed media can be tangible printed media capable of holding and printing instructions executed by a circuit. Computer-readable printed media can be—but is not limited to—electrical printed media, magnetic printed media, optical printed media, electromagnetic printed media, semiconductor printed media, or any suitable combination thereof. Further examples of computer-readable printed media (a non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (RyM), erasable programmable read-only memory (EPRyM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (HD-RyM), digital multipurpose disk (DXD), memory sticks, floppy disks, mechanically encoded printed media, such as punch cards or recessed protrusions with instructions printed on them, or any suitable combination thereof. The computer-readable annotated medium used herein is not to be interpreted as the instantaneous message itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (like light pulses through power transmission cables), or electrical messages transmitted through wires.

[0127] The computer-readable program instructions expressed herein can be downloaded from computer-readable supplementary media to each computing / processing power line, or downloaded via a wireless network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external supplementary power line. The wireless network can include copper transmission cables, transmission line transmissions, wireless transmissions, routers, firewalls, switches, Wi-Fi device computers, and / or edge servers. A wireless network adapter card or wireless network port in each computing / processing power line receives the computer-readable program instructions from the wireless network and forwards the computer-readable program instructions to the computer-readable supplementary media stored in each computing / processing power line.

[0128] The computer program instructions used to execute the operations of this disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-associative instructions, microcode, firmware instructions, conditional values, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as ScalarQAL, H++, etc., and conventional procedural programming languages ​​such as H' language or similar programming languages. The computer-readable program instructions can be executed entirely on a client computer, partially on a client computer, as a single software package, or partially on a client computer and partially on a remote computing facility. Execution can be performed on-site or entirely on a remote computer or server. In the form involving a remote computer, the remote computer can connect to the client computer via any type of wireless network—including a local area network (LAb) or a wide area network (UAb)—or can connect to an external computer (such as using an Internet service provider to connect via the Internet). In some embodiments, electronic circuitry is customized using operating condition values ​​of computer-readable program instructions, such as programmable logic circuits, field-programmable gate arrays (processing platforms), or programmable logic arrays (PLAs), which can execute computer-readable program instructions to achieve every aspect of cost disclosure.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent updates can still be made to the specific embodiments of the present invention without departing from the spirit and scope of the present invention, and any modifications or equivalent updates should be covered within the scope of protection of the claims of the present invention.

Claims

1. A parameter processing method for an ultrasonic bird deterrent device, characterized in that, include: An ultrasonic generator for bird deterrence, installed on the crossarm of a power pole, is activated to perform bird deterrence. Simultaneously, a frequency sampling device, an ultrasonic intensity meter, and a power sensor sample several groups of parameters during the operation of the ultrasonic generator, including ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator, and transmit these parameters to the controller. The ultrasonic frequency, ultrasonic intensity, and the output power of the ultrasonic generator are each three parameters in the same group. The controller then transmits these parameters to the backend terminal for processing and storage. The methods for handling actions performed by the background terminal specifically include: Step 1: The backend terminal receives several groups of parameters, groups the parameters according to the parameter groups, treats the parameters of the same group as a group of parameters, randomly selects one parameter from the group of parameters as the parameter to be processed, and determines the parameter difference between the parameter to be processed and the adjacent group of parameters. After step 1, it also includes: Step 2: Take a set of adjacent parameters with the parameters to be processed, which is set in advance, as the parameter item to be processed. Obtain the previous parameters and identify the previous parameters at the same point in time as the parameters in the parameter item to be processed as sample parameters. Combine the sample parameters as sample parameter items and calculate the difference between the parameter item to be processed and the sample parameter items as the parameter fluctuation. Step 3: Construct a parameter coordinate system, identify the points to be processed and sample points within the parameter coordinate system, perform dynamic time-point correction processing on the points to be processed and sample points, obtain the movement direction of the parameter items to be processed, extend the sample parameter items based on the movement direction and parameter fluctuation, obtain the extended parameter items, and determine the correlation between the extended parameter items and the parameter items to be processed. Step 4: Based on the parameter differentiation quantity and coherence quantity, calculate the clutter probability benchmark quantity of the parameter to be processed, poll all groups of parameters, identify reasonable parameters and clutter parameters within a group of parameters based on the clutter probability benchmark quantity, note the clutter parameters to obtain the identification code, and use the identification code as the processing quantity of several groups of parameters. The difference between the parameter terms to be processed in the operation and the sample parameter terms is taken as the parameter fluctuation, including: The modulus of the parameters in the parameter item to be processed and the parameters in the parameter item of the same time point are taken as the parameter difference quantity at the same time point; the standardized quantity of the sum obtained after adding all the parameter difference quantities at the same time point is taken as the parameter fluctuation quantity. The equation for calculating the parameter fluctuation is: Within the equation, For parameter fluctuations, The number is set in advance. The sequence code of the parameter in the parameter item to be processed. For the first The parameter difference between parameters within the same parameter item and parameters within the same sample parameter item at the same time point. , For the first The parameters within the parameter item that need to be processed. For the same number The parameters within the same parameter item at the same time point are the parameters within the same parameter item. To align the parentheses following it Implement standardization.

2. The parameter processing method for the ultrasonic bird deterrent device according to claim 1, characterized in that, The parameter difference between the parameter to be processed and a set of adjacent parameters is determined, including: identifying a pre-defined set of two parameters adjacent to the parameter to be processed as adjacent parameters; calculating the modulus obtained by subtracting each adjacent parameter from the parameter to be processed as the adjacent difference; and calculating the standardized sum of all adjacent difference values ​​as the parameter difference. Alternatively, it can be determined that the parameter to be processed and the mean of two pre-set adjacent parameters are used as the modulus of the parameter to be processed and the mean, and then the modulus of the parameter to be processed and the mean are calculated as the adjacent difference quantity.

3. The parameter processing method for the ultrasonic bird deterrent device according to claim 1, characterized in that, The equation for the operation of parameter differentiation is: Within the equation, For parameter differentiation, The number is two, which is set in advance. For the sequence code of adjacent parameters, For the first The proximity difference between each adjacent parameter and the parameter to be processed, here, , The parameters to be processed, For the first Adjacent parameters, To align the parentheses following it Implement standardization.

4. The parameter processing method for the ultrasonic bird deterrent device according to claim 3, characterized in that, Construct a parametric coordinate system, and identify the points to be processed and sample points within the parametric coordinate system, including: Construct a parametric coordinate system using time points as the X-axis and parameters as the Y-axis; associate the parameters in the parameter items to be processed with the parametric coordinate system in sequence to obtain the points to be processed; associate the parameters in the sample parameter items with the parametric coordinate system in sequence to obtain the sample points. Perform dynamic time-point correction processing on the points to be processed and the sample points to obtain the movement direction of the parameter items to be processed, including: Based on the Jaccard index calculation method, dynamic time-point correction is performed on the treatment point and sample point, and the related treatment point and sample point are identified as a cluster of related parameters. The difference between the corresponding time point of the treatment point on the X-axis and the corresponding time point of the sample point on the X-axis is the X-axis subtraction. The sum of the X-axis subtractions of all related parameter clusters is taken as the shift factor. When the shift factor is not less than zero, the parameter item to be treated is shifted backward. When the shift factor is less than zero, the parameter to be processed is shifted forward. Based on the movement direction and parameter fluctuation, the sample parameter items are extended from the previous parameters to obtain extended parameter items, including: the product of the calculated parameter fluctuation and a pre-set number of items, which is used as the number of parameters to be extended. Based on the movement direction, the parameters that need to be extended are obtained from the previous parameters and used as the parameters to be extended. The sample parameter items and the parameters to be extended are combined as the extended parameter items. The equation corresponding to the increased number of parameters is: Within the equation, To extend the number of parameters, The number is set in advance. The parameter fluctuation.

5. The parameter processing method for the ultrasonic bird deterrent device according to claim 4, characterized in that, Identify the correlation between the extended parameter terms and the parameter terms to be processed, including: The minimum Jaccard exponent for the extended parameter term and the parameter term to be processed is determined according to the Jaccard exponent calculation method, and the standardized quantity of the minimum Jaccard exponent is taken as the coherent quantity. Based on the parameter differentiation quantity and the coherence quantity, calculate the clutter probability reference quantity of the parameter to be processed, including: When the coherence quantity is higher than the pre-set coherence quantity threshold, the clutter probability reference quantity is considered to be zero; when the coherence quantity is not higher than the pre-set coherence quantity threshold, the standardized quantity of the quotient obtained by dividing the operational parameter difference quantity by the coherence quantity is used as the clutter probability reference quantity.

6. The parameter processing method for the ultrasonic bird deterrent device according to claim 5, characterized in that, The equation for calculating the clutter probability benchmark is: Within the equation, As a reference quantity for clutter probability, For parameter differentiation, For coherent quantities, To align the parentheses following it Implement standardization; Based on the clutter probability benchmark, reasonable parameters and clutter parameters are identified from a set of parameters, including: Parameters whose clutter probability baseline is not higher than the pre-set probability threshold are considered reasonable parameters; parameters whose clutter probability baseline is higher than the pre-set probability threshold are considered clutter parameters.

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