Method and device for processing detection values of wharf bearing platform construction

Abstract

A method and device for processing detection values in the construction of wharf caissons, belonging to the technical field of electrical digital data processing. It compares the reconstruction deviation of each current numerical point with the critical amount of maneuver. The numerical points higher than the critical amount are registered as interference values, and then the interference value sorting method is used to remove such interference points, ensuring that the numerical points of the horizontal steel bars of the wharf caisson only contain temperature data that meets the monitoring requirements, improving the accuracy performance and efficiency of the temperature data of the horizontal steel bars of the wharf caisson after sorting and sent to the liquid crystal screen for display. It also prevents the adverse effects of interference values on subsequent processes, ensuring the accuracy and efficiency of the temperature data of the horizontal steel bars of the wharf caisson after sorting and sent to the liquid crystal screen for display.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical digital data processing, and particularly relates to a method and device for processing detection values for wharf cap construction. Background Art

[0002] The wharf cap, as an important structure for bearing and distributing the load transmitted by the pier body, is usually arranged on the top of the foundation piles, and its function is to connect the tops of each pile to form a reinforced concrete platform. When constructing the wharf cap, multiple factors need to be comprehensively considered to ensure the smooth progress of the construction.

[0003] In practical applications, currently during the construction of the wharf cap, as mentioned in the prior art solution with the patent publication number "CN110644363A", it includes a temperature sensor installed on the horizontal steel bars of the wharf cap. The temperature sensor and the liquid crystal screen are both connected to the controller. The temperature sensor is used to sample the temperature data of the horizontal steel bars of the wharf cap and transmit it into the controller, and the controller is used to transmit the transmitted temperature data of the horizontal steel bars of the wharf cap to the liquid crystal screen for display, thereby completing the temperature detection of the horizontal steel bars of the wharf cap.

[0004] Due to the messiness of the site where the horizontal steel bars of the wharf cap are located and the suddenness of external electromagnetic interference, the temperature data of the horizontal steel bars of the wharf cap often contains a lot of interference values. Such interference values are very unfavorable to the performance of the temperature data of the horizontal steel bars of the wharf cap, and even more unfavorable to the accuracy of the temperature data of the horizontal steel bars of the wharf cap that contains a lot of interference values and is transmitted to the liquid crystal screen for display. Therefore, it is necessary to sort out its interference values to remove the interference values in the temperature data of the horizontal steel bars of the wharf cap.

[0005] In the current method of sorting out the interference values in the temperature data of the horizontal steel bars of the wharf cap, a constant critical quantity is often used to determine whether the temperature data of the horizontal steel bars of the wharf cap is an interference value. However, the constant critical quantity is not suitable for the dynamic change property of the temperature data of the horizontal steel bars of the wharf cap. Especially under the on-site conditions of the messy horizontal steel bars of the wharf cap, the fluctuation and suddenness of the temperature data of the horizontal steel bars of the wharf cap make it difficult for the constant critical quantity method to accurately identify interference values. In addition, the current method of sorting out the interference values in the temperature data of the horizontal steel bars of the wharf cap often lacks in-depth consideration of the composition and property of the temperature data of the horizontal steel bars of the wharf cap, resulting in insufficient efficiency when sorting out the large, multi-point, and dynamically changing temperature data of the horizontal steel bars of the wharf cap, and it is not easy to ensure the accuracy and reliability of the data. Summary of the Invention

[0006] To solve the defects in the prior art, the present invention proposes a processing device and method for the detection values in the construction of a wharf bearing platform, which compares the reconstruction deviation of each current numerical point with the mobile critical quantity. The numerical points higher than the critical quantity are registered as interference values, and then the interference value sorting method is used to remove such interference values, ensuring that only the temperature data that meets the monitoring requirements is contained in the numerical points of the horizontal steel bars of the wharf bearing platform. After improvement, the accuracy and efficiency of the sorted temperature data of the horizontal steel bars of the wharf bearing platform displayed on the liquid crystal screen are improved, and the adverse effects of the interference values on the subsequent links are also prevented, ensuring the accuracy and efficiency of the sorted temperature data of the horizontal steel bars of the wharf bearing platform displayed on the liquid crystal screen.

[0007] The present invention adopts the following technical solutions.

[0008] A processing method for the detection values in the construction of a wharf bearing platform, comprising:

[0009] The temperature sensor samples the temperature data of the horizontal steel bars of the wharf bearing platform and transmits it to the controller. The controller processes the transmitted temperature data of the horizontal steel bars of the wharf bearing platform, and then transmits the processed temperature data of the horizontal steel bars of the wharf bearing platform to the liquid crystal screen for display;

[0010] The method for the controller to process the transmitted temperature data of the horizontal steel bars of the wharf bearing platform includes:

[0011] Step 1, process according to the modulation and demodulation mode using the previous numerical point group of the horizontal steel bars of the wharf bearing platform, and set the initial critical quantity. Here, the initial critical quantity is the initial index for determining whether a numerical point is an interference value;

[0012] Step 2, obtain the current numerical point in the current numerical point group of the horizontal steel bars of the wharf bearing platform and send it into the modulation and demodulation mode for numerical reconstruction, obtain the reconstruction deviation, and calculate the reconstruction variance and reconstruction mean according to the reconstruction deviation;

[0013] Step 3, use the moving queue refresh rule to send the reconstruction deviation into the moving queue with a preset capacity according to the time sequence;

[0014] Step 4, refresh the initial critical quantity according to the reconstruction variance and reconstruction mean of all the reconstruction deviations in the moving queue to obtain the mobile critical quantity;

[0015] Step 5, select the reconstruction deviation corresponding to each current numerical point according to the mobile critical quantity to obtain the interference value, and sort out the interference value.

[0016] Further, in step 1, the number of temperature sensors is several, and several temperature sensors are arranged on the surface of the horizontal steel bars of the wharf cap. The several temperature sensors synchronously sample the temperature data of the horizontal steel bars of the wharf cap. When the temperature data is transmitted to the controller, it is also stored in the memory of the controller. All the temperature data transmitted at the same sampling moment by the several temperature sensors form a numerical point of the horizontal steel bars of the wharf cap. The previous numerical point group is the numerical point group formed by the controller taking out the corresponding numerical points at each sampling moment in the previously set time period in its memory.

[0017] Further, in step 2, the current numerical point group of the horizontal steel bars of the wharf cap is all the numerical points of the horizontal steel bars of the wharf cap transmitted within a currently set time period.

[0018] Further, step 1 specifically includes:

[0019] Step 1-1, construct a modulation and demodulation mode. Here, the modulation and demodulation mode includes the principal component analysis method and the PCA inverse transformation;

[0020] Step 1-2, use the principal component analysis method to perform dimensionality reduction on each previous numerical point in the previous numerical point group of the horizontal steel bars of the wharf cap to obtain the corresponding reduced-dimensional numerical values of each previous numerical point, and then the PCA inverse transformation performs operations on the reduced-dimensional numerical values one by one to reconstruct the corresponding reconstructed numerical points;

[0021] Step 1-3, after obtaining the corresponding reconstructed numerical points, obtain the previous reconstruction deviation of each corresponding reconstructed numerical point. All the previous reconstruction deviations form a previous reconstruction deviation group. Define the variance and mean of the previous reconstruction deviations in the previous reconstruction deviation group as the starting variance and starting mean respectively, and calculate the starting critical quantity according to the starting variance and starting mean.

[0022] Further, in step 1-3, the method for obtaining the previous reconstruction deviation of each corresponding reconstructed numerical point includes:

[0023] Obtain the L2 norm of the reconstructed numerical point and its corresponding previous numerical point, and take the L2 norm as the previous reconstruction deviation of the reconstructed numerical point.

[0024] Further, in step 1-3, the operation equation of the starting critical quantity is:

[0025] , where is the starting critical quantity, is the starting mean, is the starting variance, is a pre-set constant.

[0026] Further, step 2 specifically includes:

[0027] Step 2-1: Perform operations on each current numerical point within the current numerical point group one by one via the principal component analysis method in the modulation and demodulation mode to obtain the reduced-dimension values corresponding to each current numerical point.

[0028] Step 2-2: Perform reconstruction on the reduced-dimension values of the current numerical points one by one via the PCA inverse transformation in the modulation and demodulation mode to obtain the corresponding reconstructed numerical points.

[0029] Step 2-3: Obtain the L2 norm between each current numerical point and its corresponding reconstructed numerical point, take this L2 norm as the reconstruction deviation corresponding to this current numerical point, and calculate the reconstruction variance and reconstruction mean based on the reconstruction deviation.

[0030] Further, in Step 2-3, the method for calculating the reconstruction variance and reconstruction mean based on the reconstruction deviation includes:

[0031] Form the reconstruction deviation group from the reconstruction deviations corresponding to all the current numerical points, and then take the variance and mean of the reconstruction deviations within the reconstruction deviation group as the reconstruction variance and reconstruction mean respectively.

[0032] Further, Step 3 specifically includes:

[0033] Step 3-1: Arrange the reconstruction deviations via time sequence to obtain the reconstruction deviation queue arranged according to the time sequence.

[0034] Step 3-2: Start filling the reconstruction deviation queue arranged according to the time sequence into the moving queue to obtain the moving queue containing a preset number of reconstruction deviations.

[0035] Step 3-3: Dynamically update the reconstruction deviations in the moving queue after the start filling according to the moving queue update rule to keep the reconstruction deviations within the moving queue up-to-date.

[0036] Further, Step 4 specifically includes:

[0037] Step 4-1: Add the reconstruction variance and reconstruction mean of all the reconstruction deviations within the moving queue to the past reconstruction deviation group.

[0038] Step 4-2: Calculate the current mean and current variance via all the starting variances and starting means within the past reconstruction deviation group, as well as the reconstruction variance and reconstruction mean of all the reconstruction deviations within the moving queue. Here, the calculation equation for the current mean is:

[0039] , where is the current mean, and are the starting mean and the reconstruction mean respectively, and are respectively parameter one of the starting mean and parameter two of the reconstructed mean;

[0040] The operation equation of the current variance is:

[0041] , where and are respectively the number of previous numerical points and the number of current numerical points in the moving queue, and are respectively the starting variance and the reconstructed variance;

[0042] Step 4-3, calculate the maneuvering critical quantity according to the current variance and the current mean. Here, the operation equation of the maneuvering critical quantity is:

[0043] , where is the maneuvering critical quantity, is the current mean, is the current variance, is a preset constant.

[0044] Further, step 5 specifically includes:

[0045] Compare the maneuvering critical quantity with the reconstruction deviation of the current numerical point. If the reconstruction deviation is higher than the maneuvering critical quantity, then the current numerical point corresponding to the reconstruction deviation is identified as an interference value, and the interference value is removed. The current numerical point after removing the interference value is the temperature data of the horizontal steel bars of the dock cap after sorting.

[0046] A processing device for the detection values during the construction of a dock cap, including:

[0047] A temperature sensor installed on the horizontal steel bars of the dock cap. The temperature sensor and the liquid crystal screen are both connected to the controller. The temperature sensor is used to sample the temperature data of the horizontal steel bars of the dock cap and transmit it into the controller. The controller is used to process the transmitted temperature data of the horizontal steel bars of the dock cap, and then transmit the processed temperature data of the horizontal steel bars of the dock cap to the liquid crystal screen for display;

[0048] The modules running on the controller include:

[0049] A previous processing module, which is used to process according to the previous numerical point group of the horizontal steel bars of the dock cap by using a modulation and demodulation mode, and set a starting critical quantity. Here, the starting critical quantity is the starting index for identifying whether a numerical point is an interference value;

[0050] A reconstruction module, which is used to obtain the current numerical points within the current numerical point group of the horizontal steel bars of the wharf bearing platform and send them into the modulation and demodulation mode to perform numerical reconstruction, obtain the reconstruction deviation, and calculate the reconstruction variance and reconstruction mean based on the reconstruction deviation;

[0051] A queue module, which is used to send the reconstruction deviation into a moving queue with a preset capacity according to the time sequence by using the moving queue refresh rule;

[0052] A maneuver module, which is used to refresh the starting critical quantity based on the reconstruction variance and reconstruction mean of all the reconstruction deviations in the moving queue to obtain the maneuver critical quantity;

[0053] An arrangement module, which is used to select the reconstruction deviation corresponding to each current numerical point according to the maneuver critical quantity to obtain the interference value, and arrange the interference value.

[0054] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0055] Processing according to the previous numerical point group of the horizontal steel bars of the wharf bearing platform by using the modulation and demodulation mode, and setting the starting critical quantity, where the starting critical quantity is the starting index for determining whether a numerical point is an interference value; obtaining the current numerical points of the horizontal steel bars of the wharf bearing platform and sending them into the modulation and demodulation mode to perform numerical reconstruction, obtaining the reconstruction deviation, and calculating the reconstruction variance and reconstruction mean based on the reconstruction deviation; sending the reconstruction deviation into a moving queue with a preset capacity according to the time sequence by using the moving queue refresh rule; refreshing the starting critical quantity based on the reconstruction variance and reconstruction mean of all the reconstruction deviations in the moving queue to obtain the maneuver critical quantity. The present invention performs numerical arrangement according to the maneuver critical quantity, which significantly improves the performance and credibility of the temperature data of the horizontal steel bars of the wharf bearing platform. Description of the Drawings

[0056] Figure 1 is a flowchart of the method for processing the detection values for the construction of the wharf bearing platform described in the present invention;

[0057] Figure 2 is a partial structural diagram of the device for processing the detection values for the construction of the wharf bearing platform described in the present invention. Detailed Embodiments

[0058] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the drawings in the embodiments of the present invention. The embodiments described herein are only some of the embodiments of the present invention, rather than all the embodiments. According to the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] AsFigure 1 As shown in Figure 1 , a method for processing the detection values for the construction of a wharf bearing platform according to the present invention includes:

[0060] The temperature sensor samples the temperature data of the horizontal steel bars of the wharf bearing platform and transmits it to the controller. The controller processes the transmitted temperature data of the horizontal steel bars of the wharf bearing platform, and then transmits the processed temperature data of the horizontal steel bars of the wharf bearing platform to the liquid crystal screen for display, thereby completing the temperature detection of the horizontal steel bars of the wharf bearing platform;

[0061] The method for the controller to process the transmitted temperature data of the horizontal steel bars of the wharf bearing platform includes:

[0062] Step 1, process according to the modulation and demodulation mode using the previous numerical point group of the horizontal steel bars of the wharf bearing platform, and set the starting critical quantity. Here, the starting critical quantity is the starting index for determining whether a numerical point is an interference value;

[0063] In a preferred but non-limiting embodiment of the present invention, in Step 1, the number of temperature sensors is several. The several temperature sensors are installed on the horizontal steel bars of the wharf bearing platform. The several temperature sensors synchronously sample (synchronously sampling means that the sampling times of each of the several temperature sensors are the same). The sampling time of the temperature data of the horizontal steel bars of the wharf bearing platform is also the corresponding sampling time of its temperature data. When the temperature data is transmitted to the controller, it is also stored in the memory of the controller. All the temperature data transmitted by the several temperature sensors at the same sampling time form a numerical point of the horizontal steel bars of the wharf bearing platform. The previous numerical point group is a numerical point group formed by the controller extracting the corresponding numerical points at each sampling time within the previously set time period in its memory.

[0064] In the present application, process according to the modulation and demodulation mode using the previous numerical point group of the horizontal steel bars of the wharf bearing platform, and set a starting critical quantity according to the previous numerical total analysis. This critical quantity is the starting index for determining whether a numerical point is an interference value.

[0065] The starting critical quantity is used to give a reference index when the modulation and demodulation mode has not yet fully adapted to the changes in the on-site conditions of the horizontal steel bars of the wharf bearing platform, which is conducive to preventing excessive misidentification of interference values or failure to detect interference values during the mode initialization process.

[0066] Step 2, obtain the current numerical point in the current numerical point group of the horizontal steel bars of the wharf bearing platform and send it into the modulation and demodulation mode for numerical reconstruction, obtain the reconstruction deviation, and calculate the reconstruction variance and reconstruction mean according to the reconstruction deviation;

[0067] In a preferred but non-limiting embodiment of the present invention, in step 2, the current numerical point group of the horizontal steel bars of the wharf cap is all the numerical points of the horizontal steel bars of the wharf cap transmitted within a set duration at the current time. Here, the current time is the current moment, and a set duration at the current time is a set duration starting from the current moment. For example, if the current time is 15:00 on the same day, that is, the current time is 15:00 on the same day, and the set duration is 5 seconds, then a set duration at the current time is the time period between 15:00 and 15:00:05 on the same day.

[0068] In this application, the numerical points of the horizontal steel bars of the wharf cap transmitted at the current time are obtained, sent into the modulation and demodulation mode, and the mode sends out the reconstructed numerical points. Then, the difference between the source numerical points (the source numerical points are the numerical points of the horizontal steel bars of the wharf cap transmitted at the current time) and the reconstructed numerical points, that is, the reconstruction deviation, is calculated. Based on this, the deviation between the current numerical points and the mode evaluation numerical values is quantified, providing a numerical basis for subsequent interference value detection.

[0069] Step 3: Use the moving queue refresh rule to send the reconstruction deviation into a moving queue with a pre-set capacity according to the time sequence.

[0070] In this application, a time period queue with a constant span is constructed. When a new reconstruction deviation is formed, it is added to the queue, and the deviation that was first sent into the queue is removed to keep the number of deviations in the queue constant.

[0071] Through the moving queue rule, the dynamic detection of the reconstruction deviation of the numerical points in the recent time period is achieved, enabling the interference value detection to better adapt to the changes in the conditions at the site where the horizontal steel bars of the wharf cap are located. This improves the dynamic adaptability and accuracy of the interference value detection, and reduces the number of misidentified interference values caused by the fluctuations in the conditions at the site where the horizontal steel bars of the wharf cap are located.

[0072] Step 4: Refresh the initial critical quantity according to the reconstruction variance and reconstruction mean of all the reconstruction deviations in the moving queue to obtain the dynamic critical quantity.

[0073] In this application, the mean and variance of the reconstruction deviations in the moving queue are calculated, and the initial critical quantity is configured based on these total metrics to form the dynamic critical quantity. This dynamic critical quantity can more accurately reflect the overall attributes of the current numerical points, reduce the number of misidentifying reasonable temperature data as interference values and misidentifying interference values as reasonable temperature data, and can adapt to the relatively large changes in the duration presented by the temperature data, improving the robustness and stability of the interference value detection.

[0074] Step 5: Select the corresponding reconstruction deviation of each current numerical point according to the dynamic critical quantity to obtain the interference value, and then process the interference value.

[0075] In this application, the reconstruction deviation of each current numerical point is compared with the maneuvering critical quantity. The numerical points higher than the critical quantity are registered as interference values, and then the interference value sorting method is used to remove such interference values, ensuring that the numerical points of the horizontal steel bars of the wharf cap only contain temperature data that meets the monitoring requirements. After improvement, the accuracy and efficiency of the sorted temperature data of the horizontal steel bars of the wharf cap displayed on the liquid crystal screen are improved, and the adverse effects of the interference values on subsequent links are also prevented, ensuring the accuracy and efficiency of the sorted temperature data of the horizontal steel bars of the wharf cap displayed on the liquid crystal screen.

[0076] In a preferred but non-limiting embodiment of the present invention, step 1 specifically includes:

[0077] Step 1-1, constructing a modulation and demodulation mode. Here, the modulation and demodulation mode includes the principal component analysis method and the PCA inverse transformation;

[0078] In this application, the principal component analysis method is used to perform dimensionality reduction on the past numerical points in the past numerical point group of the horizontal steel bars of the wharf cap one by one to obtain the dimensionality-reduced values corresponding to each past numerical point, and the PCA inverse transformation attempts to reconstruct the corresponding restored numerical values, that is, the corresponding reconstructed numerical points, from the dimensionality-reduced values.

[0079] Step 1-2, using the principal component analysis method to perform dimensionality reduction on the past numerical points in the past numerical point group of the horizontal steel bars of the wharf cap one by one to obtain the dimensionality-reduced values corresponding to each past numerical point, and then the PCA inverse transformation performs operations on the dimensionality-reduced values one by one to reconstruct the corresponding reconstructed numerical points;

[0080] After obtaining the corresponding reconstructed numerical points, obtain the past reconstruction deviation of each corresponding reconstructed numerical point. All the past reconstruction deviations form a past reconstruction deviation group. Define the variance and mean of the past reconstruction deviations in the past reconstruction deviation group as the starting variance and starting mean respectively, and calculate the starting critical quantity based on the starting variance and starting mean.

[0081] In a preferred but non-limiting embodiment of the present invention, in step 1-3, the method for obtaining the past reconstruction deviation of each corresponding reconstructed numerical point includes:

[0082] Obtain the L2 norm of the reconstructed numerical point and its corresponding past numerical point, and use this L2 norm as the past reconstruction deviation of the reconstructed numerical point.

[0083] In a preferred but non-limiting embodiment of the present invention, in step 1-3, the calculation equation of the starting critical quantity is:

[0084] , where is the starting critical quantity, is the starting mean, is the starting variance, is a preset constant. This constant can be three.

[0085] In this application, the past reconstruction deviations of each past numerical point are recorded. Such past reconstruction deviations reflect the function of pattern reconstruction values. Through the total analysis of such past reconstruction deviations, its starting mean and starting variance can be calculated. Thus, the starting critical quantity is confirmed by multiplying the variance by a preset constant and adding it to the mean. This critical quantity is the starting index for determining whether a numerical point is an interference value.

[0086] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes:

[0087] Step 2-1, perform operations on each current numerical point in the current numerical point group one by one through the principal component analysis method of the modulation and demodulation mode to obtain the reduced-dimensional values corresponding to each current numerical point;

[0088] Step 2-2, perform reconstruction on the reduced-dimensional values of the current numerical points one by one through the PCA inverse transformation of the modulation and demodulation mode to obtain the corresponding reconstructed numerical points;

[0089] Step 2-3, obtain the L2 norm between each current numerical point and its corresponding reconstructed numerical point, take this L2 norm as the reconstruction deviation corresponding to this current numerical point, and calculate the reconstruction variance and reconstruction mean based on the reconstruction deviation.

[0090] In a preferred but non-limiting embodiment of the present invention, in step 2-3, the method for calculating the reconstruction variance and reconstruction mean based on the reconstruction deviation includes:

[0091] Form the reconstruction deviation group of the reconstruction deviations corresponding to all current numerical points, and then take the variance and mean of the reconstruction deviations in the reconstruction deviation group as the reconstruction variance and reconstruction mean respectively.

[0092] In this application, calculate the L2 norm between the current numerical point and the reconstructed numerical point. For each current numerical point, there will be a corresponding reconstruction deviation, which is the difference between the source numerical point and the reconstructed numerical point. Then, perform total analysis on the reconstruction deviations of all current numerical points, calculate the mean (reconstruction mean) and variance (reconstruction variance) of such deviations. The reconstruction mean reflects the mean deviation of the pattern reconstruction values, and the reconstruction variance represents the discrete amplitude of the deviation. Such total quantities will be used for subsequent dynamic critical quantity refreshing.

[0093] In a preferred but non-limiting embodiment of the present invention, step 3 specifically includes:

[0094] Step 3-1, arrange the reconstruction deviations according to time sequence to obtain a reconstruction deviation queue arranged according to time sequence;

[0095] In the present application, the reconstruction deviations of all current numerical points are aggregated, and a reconstruction deviation queue is formed by arranging them in the order of the sampling times of each numerical point before and after. Here, each value in the reconstruction deviation queue is a numerical cluster containing the sampling time and the corresponding reconstruction deviation. The arrangement method ensures that the reconstruction deviations are arranged in the order of the sampling times of the corresponding numerical points before and after, which is crucial for the formation of the subsequent movement queue rules.

[0096] Step 3-2: Start filling the reconstruction deviation queue arranged in time sequence into the movement queue to obtain a movement queue containing a preset number of reconstruction deviations.

[0097] In the present application, a movement queue with a corresponding capacity (the corresponding capacity is the number of values in the movement queue) is preset. The capacity of this queue is often determined based on past numerical analysis and represents the number of numerical points that can be sampled within a certain time period (such as 2s). The preset number of deviations before the corresponding sampling time in the arranged reconstruction deviation queue are sent into the movement queue. The capacity of the queue should be high enough to obtain the short-term trend of the current numerical point, but not too high to cause feedback lag.

[0098] Step 3-3: Dynamically update the reconstruction deviations in the movement queue filled at the beginning according to the movement queue update rule to keep the reconstruction deviations in the movement queue up-to-date.

[0099] In the present application, with the arrival of a new current numerical point, its reconstruction deviation is calculated and added to the end of the movement queue. At the same time, the earliest reconstruction deviation in the movement queue is removed to keep the number of reconstruction deviations in the queue constant, indicating that the values in the queue always reflect the condition of the horizontal steel bars of the wharf bearing platform in the recent certain time period. The movement queue update rule ensures that the dynamic critical quantity can be configured according to the latest current numerical point, improving the timeliness and accuracy of interference value detection.

[0100] In a preferred but non-limiting embodiment of the present invention, step 4 specifically includes:

[0101] Step 4-1: Add the reconstruction variance and reconstruction mean of all reconstruction deviations in the movement queue to the past reconstruction deviation group.

[0102] In the present application, the reconstruction variance and reconstruction mean of all reconstruction deviations are aggregated in the movement queue, and these total quantities are aggregated with the past reconstruction deviation group to form an overall numerical group. This method ensures that both the new values and the total attributes of the past values are considered, providing an overall total value for the calculation of the dynamic critical quantity.

[0103] Step 4-2: Calculate the current mean and the current variance through the overall starting variance and starting mean of the past reconstruction deviation group, as well as the reconstruction variance and reconstruction mean of all the reconstruction deviations in the moving queue. Here, the calculation equation for the current mean is:

[0104] , where is the current mean, and are the starting mean and the reconstruction mean respectively, and are Parameter 1 of the starting mean and Parameter 2 of the reconstruction mean respectively;

[0105] Here, the values of Parameter 1 and Parameter 2 can be and respectively.

[0106] In this application, during the calculation of the current mean and the current variance, lower importance ( ) is given to the starting mean and variance of the past values (corresponding to Parameter 1 respectively), while higher importance ( ) is given to the reconstruction mean and variance of the current values (i.e., the values in the moving queue, corresponding to Parameter 2 respectively). By giving higher importance to the current values, the current mean can be closer to the actual situation of the current values. Meanwhile, the mean of the past values still has some influence on the result, ensuring the smoothness of the current mean and preventing strong fluctuations caused by individual interfering values.

[0107] The calculation equation for the current variance is:

[0108] , where and are the numbers of the past value points and the current value points in the moving queue respectively, and are the starting variance and the reconstruction variance respectively.

[0109] The overall calculation of the current variance takes into account the variances of the past values and the current values. By configuring the importance of each value in the dividend (determined by the number of value points), it is ensured that the current variance not only reflects the smoothness of the past values but also the mobility of the current values. The calculation method of the current variance ensures that the flexible configuration of the interference value detection threshold can take into account both the long-term trend of the past values and the short-term fluctuations of the current values, improving the flexibility and accuracy of the threshold setting and reducing the risks of misidentification and undetected interference values.

[0110] Step 4-3: Calculate the flexible threshold based on the current variance and the current mean. Here, the calculation equation for the flexible threshold is:

[0111] , where is the mobile critical quantity, is the current mean, is the current variance, is a preset constant.

[0112] In this application, it is often set to three to ensure that the critical quantity can accurately distinguish reasonable values from interference values. By sending the current mean and the current variance into the equation, a mobile updated critical quantity can be obtained, which can perform autonomous configuration according to the total attributes of the values, improving the accuracy and adaptability of interference value detection.

[0113] In a preferred but non-limiting embodiment of the present invention, step 5 specifically includes:

[0114] Compare the mobile critical quantity with the reconstruction deviation of the current numerical point. If the reconstruction deviation is higher than the mobile critical quantity, then the current numerical point corresponding to the reconstruction deviation is identified as an interference value, and the interference value is removed. The current numerical point after removing the interference value is the temperature data of the horizontal steel bars of the dock cap after sorting.

[0115] In this application, for the reconstruction deviation of each current numerical point, compare it with the mobile critical quantity. If the reconstruction deviation is higher than the mobile critical quantity, it means that there is an obvious deviation between the numerical point and the reasonable value evaluated by the mode. At this time, this numerical point is identified as an interference value. The detection of the interference value is based on the difference between the current numerical point and the mode reconstruction numerical point, that is, the reconstruction deviation. By comparing with the mobile critical quantity, the corresponding interference situation can be immediately identified.

[0116] Once an interference value is detected, immediately identify it and record its situation in the current numerical group. It is often to attach an interference mark during the recording, or store it in the interference value queue for subsequent processing. Recording the sampling time, source data value, reconstruction deviation, etc. of the interference value is crucial for analyzing the source of interference and improving the on-site conditions.

[0117] Perform sorting processing on the numerical points identified as interference, and the sorting processing is to remove the interference values.

[0118] Accordingly, by introducing the previous numerical point group of the horizontal steel bars of the dock cap into the set modulation and demodulation mode and combining the mobile critical quantity configuration scheme, efficient and accurate processing of the temperature data of the horizontal steel bars of the dock cap is achieved. Not only is the recognition accuracy of interference values significantly improved, but also the misrecognition quantity and the quantity of undetected interference values are effectively reduced, ensuring the credibility and efficiency of numerical sorting.

[0119] That is, through the reconstruction process of the modulation and demodulation mode, the depth consideration of the temperature data, its composition and properties, of the horizontal steel bars of the wharf cap is strengthened. Thus, the reasonable fluctuation range of the temperature data of the horizontal steel bars of the wharf cap can be evaluated more accurately, providing an accurate basis for setting the mobile critical quantity. And the mobile critical quantity can immediately feedback the change trend of the temperature data of the horizontal steel bars of the wharf cap. Even when the temperature data of the horizontal steel bars of the wharf cap fluctuates greatly, an excellent interference value detection function can still be maintained.

[0120] In addition, the application of the mobile queue refresh rule further strengthens the processing function of this method for time series values, enabling it to flexibly configure the critical quantity within the continuous numerical point interval, ensuring the quick and accurate identification of the newly transmitted temperature data of the horizontal steel bars of the wharf cap. This not only improves the timeliness of organizing the temperature data of the horizontal steel bars of the wharf cap, but also ensures the timeliness and accuracy of the temperature data of the horizontal steel bars of the wharf cap.

[0121] Finally, the performance of the temperature data of the horizontal steel bars of the wharf cap sorted out by this application has been significantly improved, and the interference value has been efficiently removed, providing a more reliable and high-performance numerical support for the subsequent process of displaying the temperature data of the horizontal steel bars of the wharf cap on the liquid crystal screen.

[0122] Such as Figure 2 shown, a processing device for the detection values during the construction of the wharf cap according to the present invention includes:

[0123] A temperature sensor installed on the horizontal steel bars of the wharf cap. Both the temperature sensor and the liquid crystal screen are connected to the controller. The temperature sensor is used to sample the temperature data of the horizontal steel bars of the wharf cap and transmit it into the controller. The controller is used to process the transmitted temperature data of the horizontal steel bars of the wharf cap, and then transmit the processed temperature data of the horizontal steel bars of the wharf cap to the liquid crystal screen for display, thereby completing the temperature detection of the horizontal steel bars of the wharf cap;

[0124] The modules running on the controller include:

[0125] A past processing module, which is used to process according to the past numerical point group of the horizontal steel bars of the wharf cap using the modulation and demodulation mode, and set the starting critical quantity. Here, the starting critical quantity is the starting index for determining whether a numerical point is an interference value;

[0126] A reconstruction module, which is used to obtain the current numerical point in the current numerical point group of the horizontal steel bars of the wharf cap and send it into the modulation and demodulation mode for numerical reconstruction, obtain the reconstruction deviation, and calculate the reconstruction variance and reconstruction mean according to the reconstruction deviation;

[0127] A queue module, which is used to send the reconstruction deviation into a moving queue with a preset capacity according to the time sequence by applying the moving queue refresh rule;

[0128] A maneuver module, which is used to refresh the starting critical quantity according to the reconstruction variance and the reconstruction mean of all the reconstruction deviations in the moving queue to obtain the maneuver critical quantity;

[0129] An arrangement module, which is used to select the reconstruction deviation corresponding to each current numerical point according to the maneuver critical quantity to obtain the interference value, and arrange the interference value. The controller can be an FPGA, a PLC or an industrial control computer.

[0130] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0131] Disposing the past numerical point group of the horizontal steel bars of the wharf bearing platform by using the modulation and demodulation mode, and setting the starting critical quantity. Here, the starting critical quantity is the starting index for determining whether the numerical point is an interference value; obtaining the current numerical point of the horizontal steel bars of the wharf bearing platform and sending it into the modulation and demodulation mode to perform numerical reconstruction to obtain the reconstruction deviation, and obtaining the reconstruction variance and the reconstruction mean according to the reconstruction deviation; sending the reconstruction deviation into a moving queue with a preset capacity according to the time sequence by applying the moving queue refresh rule; refreshing the starting critical quantity according to the reconstruction variance and the reconstruction mean of all the reconstruction deviations in the moving queue to obtain the maneuver critical quantity. The present invention performs numerical arrangement according to the maneuver critical quantity, which significantly improves the performance and reliability of the temperature data of the horizontal steel bars of the wharf bearing platform.

[0132] 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 them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that: still can modify or equivalently replace the specific implementation manners of the present invention, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered within the protection scope of the claims of the present invention.

Claims

1. A method for processing detection values ​​for wharf cap construction, characterized in that: include: The temperature sensor samples the temperature data of the horizontal steel bars of the wharf cap and transmits it to the controller. The controller sorts the temperature data of the horizontal steel bars of the wharf cap and then transmits the sorted temperature data of the horizontal steel bars of the wharf cap to the LCD screen for display. The method for the controller to sort out the temperature data of the horizontal steel bars of the wharf cap transmitted includes: Step 1, using the modulation and demodulation mode to process the previous value point group of the horizontal steel bars of the wharf cap, and setting the initial critical value, where the initial critical value is the initial indicator for determining whether the value point is an interference value; Step 2, obtaining the current value point in the current value point group of the horizontal steel bar of the wharf cap and sending it into the modulation and demodulation mode to perform numerical reconstruction, obtaining the reconstruction deviation, and obtaining the reconstruction variance and reconstruction mean according to the reconstruction deviation calculation; Step 3: Use the mobile queue refresh rule to send the reconstruction deviation into the mobile queue with a preset capacity according to the time sequence; Step 4, refreshing the initial critical amount according to the reconstruction variance and reconstruction mean of all reconstruction deviations in the mobile queue to obtain the maneuvering critical amount; Step 5, selecting the reconstruction deviation corresponding to each current value point according to the maneuvering critical amount, obtaining the interference value, and sorting the interference value; Step 3 specifically includes: Step 3-1, arranging the reconstruction deviations according to the time sequence, and obtaining a reconstruction deviation queue arranged according to the time sequence; Step 3-2, starting to fill the reconstruction deviation queue arranged in time sequence into the mobile queue, and obtaining a mobile queue containing a preset number of reconstruction deviations; Step 3-3, performing a dynamic refresh on the reconstruction deviation in the mobile queue after the mobile queue is initially filled according to the mobile queue refresh rule to maintain the reconstruction deviation in the mobile queue to be recent; Step 4 specifically includes: Step 4-1, add the reconstruction variance and reconstruction mean of all reconstruction deviations in the mobile queue to the previous reconstruction deviation group; Step 4-2, calculate the current mean and current variance by calculating the initial variance and initial mean of all previous reconstruction deviation groups, as well as the reconstruction variance and reconstruction mean of all reconstruction deviations in the mobile queue. Here, the calculation equation of the current mean is: , here, is the current mean, and are the initial mean and the reconstructed mean, and They are parameter 1 of the initial mean and parameter 2 of the reconstructed mean; The current equation for the variance is: , Here, and They are the number of previous value points and the number of current value points in the mobile queue. and They are the initial variance and the reconstruction variance respectively; Step 4-3, obtain the maneuverability critical value based on the current variance and the current mean. Here, the calculation equation of the maneuverability critical value is: , here, is the critical mass of mobility, is the current mean, is the current variance, is a constant set in advance; Step 5 specifically includes: Compare the mobile critical value with the reconstruction deviation of the current numerical point. If the reconstruction deviation is higher than the mobile critical value, the current numerical point corresponding to the reconstruction deviation will be identified as an interference value, and the interference value will be removed. The current numerical point after removing the interference value is the sorted temperature data of the horizontal steel bars of the wharf foundation.

2. The method for processing detection values ​​for wharf cap construction according to claim 1, characterized in that: In step 1, there are several temperature sensors, and several temperature sensors are arranged on the surface of the horizontal steel bars of the wharf cap. Several temperature sensors synchronously sample the temperature data of the horizontal steel bars of the wharf cap. When the temperature data is transmitted to the controller, it is also stored in the memory of the controller. The overall temperature data transmitted by several temperature sensors at the same sampling time forms a numerical point of the horizontal steel bars of the wharf cap. The previous numerical point group is a numerical point group formed by the controller taking out the numerical points corresponding to each sampling time in the time length set in the previous section in its memory.

3. The method for processing detection values ​​for wharf cap construction according to claim 2, characterized in that: In step 2, the current value point group of the horizontal steel bars of the wharf cap is the value points of the horizontal steel bars of all wharf caps transmitted within a set time period.

4. The method for processing detection values ​​for wharf cap construction according to claim 3, characterized in that: Step 1 specifically includes: Step 1-1, constructing a modulation and demodulation model, where the modulation and demodulation model includes principal component analysis and PCA inverse transformation; Step 1-2, using the principal component analysis method to perform dimensionality reduction on the past value points in the past value point group of the horizontal steel bars of the wharf cap one by one to obtain the corresponding reduced-dimensional values ​​of each past value point, and then the PCA inverse transformation performs operations on the reduced-dimensional values ​​one by one to reconstruct the corresponding reconstructed value points; Step 1-3, after obtaining the corresponding reconstruction value points, obtain the past reconstruction deviations of each corresponding reconstruction value point, and all past reconstruction deviations form a past reconstruction deviation group. The variance and mean of the past reconstruction deviations in the past reconstruction deviation group are defined as the starting variance and the starting mean, respectively, and the starting critical amount is calculated based on the starting variance and the starting mean.

5. The method for processing detection values ​​for wharf cap construction according to claim 4, characterized in that: In step 1-3, the method of obtaining the past reconstruction deviation of each corresponding reconstruction value point includes: Obtain the L2 norm of the reconstructed value point and its corresponding past value point, and use the L2 norm as the past reconstruction deviation of the reconstructed value point; In steps 1-3, the calculation equation for the initial critical volume is: , here, is the starting critical mass, is the starting mean, is the starting variance, is a pre-set constant.

6. The method for processing detection values ​​for wharf cap construction according to claim 5, characterized in that: Step 2 specifically includes: Step 2-1, performing operations on each current value point in the current value point group one by one through the principal component analysis method of the modulation and demodulation mode, and obtaining the corresponding dimension-reduced value of each current value point; Step 2-2, reconstructing the dimension-reduced values ​​of the current value point one by one through the PCA inverse transformation of the modulation and demodulation mode to obtain the corresponding reconstructed value point; Step 2-3, obtain the L2 norm between each current value point and its corresponding reconstruction value point, use the L2 norm as the reconstruction deviation corresponding to the current value point, and obtain the reconstruction variance and reconstruction mean based on the reconstruction deviation calculation.

7. The method for processing detection values ​​for wharf cap construction according to claim 6, characterized in that: In step 2-3, the method of obtaining the reconstruction variance and the reconstruction mean according to the reconstruction deviation calculation includes: The reconstruction deviations corresponding to all current value points are formed into a reconstruction deviation group, and then the variance and mean of the reconstruction deviations in the reconstruction deviation group are used as the reconstruction variance and reconstruction mean, respectively.

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