A concrete test piece quality inspection data compression management method

Abstract

The application relates to the technical field of data processing, in particular to a concrete test piece quality inspection data compression management method, which comprises the following steps: numbering the quality inspection data of each concrete test piece according to production time, and determining the neighborhood data of each concrete test piece; determining the data sequence of each concrete test piece according to the quality inspection data and the neighborhood data of each concrete test piece, determining the first average value of the data sequence, modifying the first average value based on the first type data greater than the first average value and the second type data smaller than the first average value in the data sequence, and obtaining a modified average value; determining the outlying degree of the quality inspection data of each concrete test piece, and determining the quality inspection data with an outlying degree greater than a threshold value as outlying data; and compressing other data except the outlying data in the quality inspection data of each concrete test piece based on a sliding window trajectory compression algorithm to obtain compressed data. Thus, the compression effect of the quality inspection data of the concrete test piece is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method for compressing and managing quality inspection data of concrete specimens. Background Technology

[0002] In modern construction engineering, concrete is a critical structural material; its strength and durability directly affect the safety and lifespan of the project. To ensure concrete quality meets standards, concrete specimens are typically prepared on-site and cured and tested for compressive strength according to regulations. Traditional methods of concrete specimen management rely on manual numbering, registration, and submission for testing, which carries risks such as specimen confusion, substitution, and data falsification, seriously affecting the impartiality and reliability of test results. With numerous sensors operating continuously, the generated quality inspection data is characterized by high frequency and large volume, placing significant pressure on data transmission, storage, and subsequent analysis. Therefore, trajectory data compression algorithms are commonly used to compress and transmit the quality inspection data of concrete specimens.

[0003] Traditional methods often use the Sliding Window Trajectory Compression (SWTC) algorithm for data compression. However, when compressing various quality inspection data of concrete specimens using the SWTC algorithm, the differences between adjacent normal data of the concrete specimens are small. But once abnormal data of the concrete specimens are replaced, the data will fluctuate greatly. This causes the fitting curve of the SWTC algorithm at outlier data to show obvious bulges or depressions, resulting in poor overall data compression effect. Summary of the Invention

[0004] To address the technical problem of poor compression effect of concrete specimen quality inspection data, the present invention aims to provide a method for compressing and managing concrete specimen quality inspection data.

[0005] To solve the above technical problems, the specific technical solution adopted is as follows:

[0006] In a first aspect, the present invention provides a method for compressing and managing quality inspection data of concrete specimens, comprising: numbering the quality inspection data of each concrete specimen according to the production time, and determining the neighborhood data of each concrete specimen; determining the data sequence of each concrete specimen based on the quality inspection data and neighborhood data of each concrete specimen, determining the first average value of the data sequence, and correcting the first average value based on the first type of data in the data sequence that is greater than the first average value and the second type of data that is less than the first average value, to obtain a corrected average value; determining the outlier degree of the quality inspection data of each concrete specimen based on the quality inspection data, the corrected average value, and the interquartile range and interquartile range multiple of the corresponding data sequence, and determining the quality inspection data with an outlier degree greater than a threshold as outlier data; and compressing the other data in the quality inspection data of each concrete specimen, excluding outlier data, based on a sliding window trajectory compression algorithm to obtain compressed data.

[0007] Preferably, the correction of the first average value based on the first type of data in the data sequence that is greater than the first average value and the second type of data that is less than the first average value to obtain the corrected average value includes: determining the second average value of the first type of data and the third average value of the second type of data; determining the degree of influence of outlier data on the first average value of the concrete specimen data sequence based on the first average value, the third average value and the second average value; and correcting the first average value using the degree of influence to obtain the corrected average value.

[0008] Preferably, determining the degree to which the first average value of the concrete specimen data sequence is affected by outlier data based on the first average value, the third average value, and the second average value includes: determining the first difference between the first average value and the third average value, and the second difference between the second average value and the first average value; and determining the degree to which the first average value of the concrete specimen data sequence is affected by outlier data based on the first difference and the second difference.

[0009] Preferably, determining the outlier degree of the quality inspection data of each concrete specimen based on the quality inspection data, the corrected average value, and the interquartile range and interquartile range multiples of the corresponding data sequence includes: determining the product between the interquartile range and the interquartile range multiples of the data sequence; and determining the outlier degree of the quality inspection data of each concrete specimen based on the quality inspection data, the corrected average value, and the product.

[0010] Preferably, after compressing the quality inspection data of each concrete specimen, excluding outlier data, using a sliding window trajectory compression algorithm to obtain compressed data, the method further includes transmitting the outlier data and the compressed data.

[0011] Preferably, transmitting outlier data and compressed data includes: indexing the outlier data in the quality inspection data of each concrete specimen and recording the outlier data in an outlier data table; and transmitting the outlier data table and compressed data.

[0012] Preferably, after transmitting the outlier data and compressed data, the method further includes: decompressing the compressed data to obtain decompressed data; and analyzing the decompressed data and outlier data to determine the cause of the outlier data.

[0013] Preferably, determining the neighborhood data of each concrete specimen includes: taking the quality inspection data of the m concrete specimens that are closest to the current concrete specimen in terms of production time as the neighborhood data, where m is an integer greater than or equal to 2.

[0014] Preferably, determining the data sequence of each concrete specimen based on its quality inspection data and neighborhood data includes: sorting the quality inspection data and neighborhood data of the concrete specimens according to the production time of the corresponding concrete specimens to obtain the data sequence.

[0015] Preferably, the quality control data of the concrete specimens include at least one of temperature, humidity and weight.

[0016] Beneficial Effects: This invention constructs a data sequence according to production time and introduces neighboring data, making outlier identification more closely aligned with the temporal patterns of concrete specimen quality changes. By combining the first and second types of data to correct the average value, the problem of the original average value being affected by data distribution deviations is effectively avoided. Furthermore, relying on interquartile range and multiple analysis to determine the degree of outlier identification significantly improves the accuracy and robustness of outlier identification. This invention compresses the remaining normal data excluding outliers, solving the problem of reduced compression efficiency or even failure caused by outliers in traditional algorithms. While retaining key quality data, it minimizes data redundancy and improves the compression effect of concrete specimen quality inspection data. For construction sites with limited network bandwidth, it significantly reduces data transmission bandwidth usage and communication overhead, while also alleviating storage pressure, achieving efficient transmission and storage of quality inspection data. By correcting the average value and using the interquartile range rule, the scientific nature of outlier screening is ensured, avoiding misjudging normal data as outliers or missing outliers. The resulting compressed data fully retains the true characteristics of concrete quality changes, providing high-quality data support for subsequent cloud-based data analysis and quality trend prediction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for compressing and managing quality inspection data of concrete specimens, provided as an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of a concrete specimen quality inspection data compression and management device provided in one embodiment of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a concrete specimen quality inspection data compression and management method proposed according to the present invention. In the following description, different "one embodiment" or "one embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] The specific scheme of the concrete specimen quality inspection data compression management method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Example 1:

[0024] Please see Figure 1 The flowchart illustrates a method for compressing and managing quality inspection data of concrete specimens according to an embodiment of the present invention, including:

[0025] Step S101: Number the quality inspection data of each concrete specimen according to the production time, and determine the neighboring data of each concrete specimen.

[0026] Specifically, in this embodiment of the invention, concrete specimens are first prepared using a concrete mold equipped with Global Positioning System (GPS) positioning and a unique identifier. Specifically, concrete is poured into the cavity of the mold body, and then the geographical location information of the concrete specimen during the preparation process is determined using the GPS positioning system. The mold and concrete specimen are then identified and photographed using a unique identifier, and the data is uploaded to a monitoring information system. To prevent data loss due to substitution during concrete molding, quality inspection data of the concrete specimens is collected using embedded sensors. This quality inspection data includes, but is not limited to, the weight, temperature, and humidity of the concrete specimens, and is transmitted in real time to a computer or cloud platform for analysis and recording. According to another embodiment of the invention, the quality inspection data of the concrete specimens includes at least one of temperature, humidity, and weight.

[0027] More specifically, in this embodiment of the invention, the first concrete specimen... The first concrete specimen The quality inspection data is recorded as follows The number of data items in the collected quality inspection data is recorded as follows: The number of concrete specimens collected is recorded as follows: .

[0028] More specifically, in the process of concrete quality testing based on concrete specimens, since a large number of concrete specimens are collected, it is necessary to divide the local data range of the quality test data for each concrete specimen, and record the data within the local data range as the neighborhood data of the quality test data of that concrete specimen.

[0029] More specifically, according to another embodiment of the present invention, determining the neighborhood data of each concrete specimen includes: taking the quality inspection data of the m concrete specimens that are closest to the current concrete specimen in terms of production time as the neighborhood data, where m is an integer greater than or equal to 2.

[0030] Specifically, in this embodiment of the invention, a local data range is preset. The value can be selected based on the actual situation; in this embodiment of the invention, an empirical value is used. This embodiment does not limit the local data range; implementers can set the local data range according to the actual implementation situation. Then, the concrete specimens are numbered according to their production time, and the specimens closest to the specified time are grouped together. The quality inspection data of the first concrete specimen is recorded as the neighborhood data of the current concrete specimen's quality inspection data. That is, the quality inspection data of the m concrete specimens whose production time is closest to the current concrete specimen's production time are the neighborhood data. In this embodiment of the invention, the quality inspection data of the first concrete specimen is recorded as the neighborhood data. Within the neighborhood data of the quality inspection data of the concrete specimen, the first... The first concrete specimen The quality inspection data is recorded as follows .

[0031] Step S102: Determine the data sequence of each concrete specimen based on the quality inspection data and neighborhood data of each concrete specimen, determine the first average value of the data sequence, and correct the first average value based on the first type of data in the data sequence that is greater than the first average value and the second type of data that is less than the first average value, to obtain the corrected average value.

[0032] It should be noted that if all data in the data sequence are equal, that is, all data are equal to their average value, then the corrected average value is equal to the first average value, and step S103 is continued.

[0033] Specifically, for the quality inspection data of each concrete specimen, the local outlier degree is obtained based on the difference between the quality inspection data and its neighboring data. During the compression of the concrete specimen quality inspection data using the sliding window trajectory compression algorithm, only data with a large outlier degree are sensitive; therefore, the outlier degree of each quality inspection data point is quantified. It should be noted that the outlier degree does not accurately measure the degree of abnormality in the quality inspection data; therefore, data with a large outlier degree does not necessarily represent abnormal data. It should also be noted that for outlier data in the concrete specimen quality inspection data, each concrete specimen is continuously produced from the same concrete mold; therefore, the differences in quality inspection data between adjacent concrete specimens are minimal. However, if abnormal situations such as specimen swapping exist, there will be a significant difference between abnormal and normal concrete specimens. Therefore, the outlier degree of the quality inspection data can be obtained based on the difference between the quality inspection data of each concrete specimen and the average level of its corresponding neighboring data.

[0034] More specifically, according to another embodiment of the present invention, determining the data sequence of each concrete specimen based on the quality inspection data and neighborhood data of each concrete specimen includes: sorting the quality inspection data and neighborhood data of the concrete specimen according to the production time of the corresponding concrete specimen to obtain the data sequence.

[0035] Specifically, the embodiments of the present invention are based on the first... The first concrete specimen Taking the quality inspection data as an example, the first item The first concrete specimen Neighborhood data of the quality inspection data and the first The first concrete specimen The quality inspection data itself is sorted by production time and denoted as the [number]. The first concrete specimen Data sequence of quality inspection data and obtain the first The first concrete specimen Data sequence of quality inspection data The average value of all data in the present invention is denoted as . .

[0036] It should be noted that, in this embodiment of the invention, outliers are identified based on the difference between the data in each data sequence and the average value. In the quality inspection data of concrete specimens, since outliers are made from different types of concrete and concrete molds, there will be significant differences between concrete specimens under different conditions. Therefore, outlier data will have a significant impact on the average value of the data sequence. Thus, the degree of influence of outliers on the average value of the data sequence can be obtained by considering the difference in the number of data points greater than and less than the average value in the data sequence. The degree of influence is then used to correct the average value of the data sequence. Therefore, as an optional embodiment of the invention, the correction of the first average value based on a first type of data greater than the first average value and a second type of data less than the first average value in the data sequence to obtain the corrected average value includes: determining a second average value of the first type of data and a third average value of the second type of data; determining the degree of influence of outliers on the first average value of the concrete specimen data sequence based on the first average value, the third average value, and the second average value; and correcting the first average value using the degree of influence to obtain the corrected average value.

[0037] Specifically, in the embodiments of the present invention, the first The first concrete specimen Data sequence of quality inspection data All data in the dataset are categorized as greater than the first average. and less than the average For the first type of data and the second type of data, obtain the second average value of the first type of data respectively. The third average of the second type of data Then, based on the first average value... Second average and the third average Get the The first concrete specimen Data sequence of quality inspection data The degree to which the average value is affected by outlier data. In another embodiment of the present invention, determining the degree to which the first average value of the concrete specimen data sequence is affected by outlier data, based on a first average value, a third average value, and a second average value, includes: determining a first difference between the first average value and the third average value, and a second difference between the second average value and the first average value; and determining the degree to which the first average value of the concrete specimen data sequence is affected by outlier data based on the first difference and the second difference.

[0038] It should be noted that before calculating the average of the data, the data values ​​in the data sequence are first normalized. As a specific example, the maximum-minimum normalization method is used for normalization, and the average of the data is the average of the normalized data values. The maximum-minimum normalization method is existing technology and will not be elaborated on further here.

[0039] Specifically, in this embodiment of the invention, the following formula is used to calculate the first... The first concrete specimen Data sequence of quality inspection data The extent to which the mean is affected by outlier data:

[0040]

[0041] In the above formula, Indicates the first The first concrete specimen Data sequence of quality inspection data The degree to which the first average is affected by outlier data. Indicates the first The first concrete specimen Data sequence of quality inspection data The first average value. Indicates the first The first concrete specimen Data sequence of quality inspection data The second average of the first category of data that is greater than the first average. Indicates the first The first concrete specimen Data sequence of quality inspection data The third average of the second category of data that is less than the first average.

[0042] More specifically, according to the above method, the average value of the data sequence of quality inspection data for all items of all concrete specimens is affected by outlier data.

[0043] It should be noted that if the concrete specimens are swapped or other abnormalities occur, outliers in the data sequence will exist, causing the average value of the data sequence to be biased towards higher levels. For data sequences without outliers, all data will be distributed almost evenly around the average value. However, when outliers appear, they will cause the average value to rise, turning some data points that were originally above the average into those below it, thus reducing the number of data points above the average. Since the total difference between the data points on both sides of the data sequence and the average value is always the same, reducing the number of data points above the average value will increase the average difference between all data points that are also above the average value and the average value. Therefore, this embodiment of the invention divides the data sequence into two categories based on the average value and obtains the degree of influence of outliers on the average value of the data sequence based on the difference between the data points and the average value. Regarding the formula for calculating the degree of influence, it should be noted that... The partial value represents the ratio of the differences between the means of the two classes of data divided according to the mean of the data series and the mean of the data series itself. The closer this ratio is to 1, the less the calculation of the mean of the data series is affected by outliers. Further adjustments to the mean of the data series are made by using radicals to make the ratio even closer to 1.

[0044] More specifically, embodiments of the present invention employ the following formula based on the first... The first concrete specimen Data sequence of quality inspection data The first mean of the data series is adjusted to account for the degree to which outlier data affects the mean:

[0045]

[0046] In the above formula, Indicates based on the degree of impact For the The first concrete specimen Data sequence of quality inspection data The first average value is corrected to obtain the corrected average value. Indicates the first The first concrete specimen Data sequence of quality inspection data The degree to which the first average is affected by outlier data. Indicates the first The first concrete specimen Data sequence of quality inspection data The first average value is obtained by adjusting the first average value of the data sequence corresponding to all items of quality inspection data of all concrete specimens according to the degree of influence, thus obtaining the corrected average value.

[0047] It should be noted that, according to the formula for calculating the degree of influence, if the calculated degree of influence is greater than 1, it means that the data in the corresponding data sequence that are smaller than the average value of the data sequence are more likely to have outliers that are smaller than the average value relative to all the data. The existence of these outliers makes the average value of the data sequence too low, so the average value of the data sequence can be increased according to the degree of influence. Conversely, when the degree of influence is less than 1, the average value of the data sequence can also be decreased according to the degree of influence.

[0048] Step S103: Based on the quality inspection data, corrected average value, interquartile range and interquartile range multiple of each concrete specimen, determine the outlier degree of the quality inspection data of each concrete specimen, and determine the quality inspection data with an outlier degree greater than the threshold as outlier data.

[0049] Specifically, in the quality inspection data of concrete specimens, outliers often differ significantly from normal data, greatly impacting the average value of the data series. Without adjusting the average value, subsequent calculations of outlier severity will be significantly biased. The adjusted average value largely eliminates the influence of outliers, better representing the average level of each data series after outlier removal. Based on the quality inspection data, the data series of the quality inspection data, and the adjusted corrected average value, the degree of outlier in the quality inspection data is obtained.

[0050] More specifically, according to another embodiment of the present invention, determining the outlier degree of the quality inspection data of each concrete specimen based on the quality inspection data of each concrete specimen, the corrected average value, and the interquartile range and interquartile range multiple of the corresponding data sequence includes: determining the product between the corrected average value and the interquartile range and interquartile range multiple of the corresponding data sequence; and determining the outlier degree of the quality inspection data of each concrete specimen based on the quality inspection data of the concrete specimen and the product.

[0051] Specifically, in this embodiment of the invention, a multiple of the interquartile range is first preset. In the embodiments of the present invention, the empirical value is This embodiment of the invention does not limit the interquartile range multiple; the interquartile range multiple can be set according to the actual implementation. Then, for the... The first concrete specimen For each quality inspection data item, the interquartile range of all data in its data sequence is denoted as... (The principle of interquartile range can be found in known technologies, and will not be elaborated here in this embodiment of the invention.) Then, the following formula is used to obtain the first... The first concrete specimen Outlier extent of quality inspection data:

[0052]

[0053] In the above formula, Indicates the first The first concrete specimen The degree of outlier in the quality inspection data. Indicates the first The first concrete specimen Quality inspection data. This indicates a multiple of the interquartile range. Indicates the first The first concrete specimen The interquartile range of all data in the data sequence of the quality inspection data. Indicates based on the degree of impact For the The first concrete specimen Data sequence of quality inspection data The first average value is corrected to obtain the corrected average value. This indicates a preset zero-prevention constant. In this embodiment, The value is 0.0001.

[0054] It should be noted that, in this embodiment of the invention, the acceptable deviation is calculated by multiplying a preset interquartile range multiple by the interquartile range of the data sequence. The adjusted average value of the data sequence represents the average level of the data sequence. The first concrete specimen The product of a quality inspection data point and its corresponding data series average level, and its acceptable deviation, is calculated. A higher ratio indicates a better quality inspection result. The first concrete specimen The greater the outlier the quality inspection data, the better.

[0055] At this point, the outlier rate of all quality inspection data for all concrete specimens was obtained.

[0056] More specifically, the threshold in this embodiment of the invention can be determined according to the actual situation. In this embodiment, the value is 1. It should be noted that the thresholds mentioned in this embodiment are all outlier thresholds. Based on the outlier degree of all quality inspection data of all concrete specimens, this embodiment considers quality inspection data with a large outlier degree as outlier data. During the compression of the quality inspection data of the concrete specimens through sliding window trajectory compression, outlier data is processed separately, which can better optimize the compression effect. In this embodiment, outlier data with an outlier degree greater than 1 in all quality inspection data of all concrete specimens is recorded as outlier data.

[0057] Step S104: Based on the sliding window trajectory compression algorithm, compress the data other than outliers in the quality inspection data of each concrete specimen to obtain compressed data.

[0058] Specifically, outliers may exist due to unforeseen events such as the replacement of concrete specimens or machine malfunctions. The specific reasons can be obtained through subsequent computer analysis of various data from the concrete specimens. During the transmission of concrete specimen data to the computer, the quality inspection data is compressed using a sliding window trajectory compression algorithm to reduce bandwidth during transmission. Special processing is required for outliers to optimize the compression effect.

[0059] More specifically, in this embodiment of the invention, each quality inspection data item of the concrete specimen is compressed according to the sliding window trajectory compression algorithm. During the sliding window trajectory compression process of traversing the quality inspection data by two pointers, when the pointer encounters outlier data, the outlier data is skipped, and the error corresponding to the outlier data is recorded as 0 when calculating the error, thereby temporarily removing the outlier data and compressing only the normal quality inspection data to obtain compressed data.

[0060] More specifically, according to another embodiment of the present invention, after compressing the quality inspection data of each concrete specimen, excluding outlier data, based on the sliding window trajectory compression algorithm to obtain compressed data, the method further includes: transmitting the outlier data and the compressed data.

[0061] Specifically, in this embodiment of the invention, the index of outlier data appearing in the quality inspection data of each concrete specimen and the outlier data are recorded in an outlier data table; the outlier data table and compressed data are transmitted.

[0062] More specifically, according to another embodiment of the present invention, after transmitting the outlier data and the compressed data, the method further includes: decompressing the compressed data to obtain decompressed data; and analyzing the decompressed data and the outlier data to determine the cause of the outlier data.

[0063] Specifically, the existence of outliers may be due to unforeseen events such as the replacement of concrete specimens or machine malfunctions. By analyzing the decompressed data and outliers of various quality inspection data of the concrete specimens using a computer, the cause of the outliers can be determined.

[0064] This invention constructs a data sequence based on production time and introduces neighboring data, making outlier identification more closely aligned with the temporal patterns of concrete specimen quality changes. By combining the first and second types of data to correct the average value, the problem of the original average value being affected by data distribution deviations is effectively avoided. Furthermore, relying on interquartile range and multiple analysis to determine the degree of outlier identification significantly improves the accuracy and robustness of outlier identification. This invention compresses the remaining normal data (excluding outliers), solving the problem of reduced compression efficiency or even failure caused by outliers in traditional algorithms. While retaining key quality data, it minimizes data redundancy and improves the compression effect of concrete specimen quality inspection data. For construction sites with limited network bandwidth, this significantly reduces data transmission bandwidth usage and communication overhead, while also alleviating storage pressure, achieving efficient transmission and storage of quality inspection data. By correcting the average value and using the interquartile range rule, the scientific nature of outlier screening is ensured, avoiding misjudging normal data as outliers or omitting outliers. The resulting compressed data fully preserves the true characteristics of concrete quality changes, providing high-quality data support for subsequent cloud-based data analysis and quality trend prediction.

[0065] Furthermore, in this embodiment of the invention, the average level of local data is obtained based on the data of each concrete specimen and the neighboring data. The average level of local data is adjusted by obtaining the degree of influence of outlier data on the average level through local data. The local outlier degree of each data item of each concrete specimen is obtained based on the difference between each data value and the local data average. Concrete specimen data with a large local outlier degree are recorded as outlier data. During the compression of concrete specimen data by sliding window compression algorithm, an outlier data table is maintained to store outlier data values ​​and their indexes. The outlier data table is also transmitted during the transmission process.

[0066] Example 2:

[0067] Corresponding to the concrete specimen quality inspection data compression and management method provided in the above embodiments, based on the same technical concept, this embodiment of the invention also provides a concrete specimen quality inspection data compression and management device, which is used to execute the above concrete specimen quality inspection data compression and management method. Figure 2 To illustrate the structure of another concrete specimen quality inspection data compression and management device according to various embodiments of the present invention, as shown in the schematic diagram... Figure 2As shown at the hardware level, the concrete specimen quality inspection data compression and management device includes a processor, and optionally, an internal bus, a network interface, and memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, this concrete specimen quality inspection data compression and management device may also include other hardware required for other operations.

[0068] The processor, network interface, and memory can be interconnected via an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, this diagram uses only a single bidirectional arrow, but it does not imply that there is only one bus or one type of bus.

[0069] Memory is used to store programs. Specifically, programs can include program code, which includes computer operation commands. Memory can include main memory and non-volatile memory, and it provides instructions and data to the processor.

[0070] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming the concrete specimen quality inspection data compression and management device at the logical level. The processor executes the program stored in memory and specifically performs the following: Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods in the preceding method embodiments, and will not be repeated here.

[0071] It should be noted that the concrete specimen quality inspection data compression and management device provided in this embodiment of the invention and the concrete specimen quality inspection data compression and management method provided in this embodiment of the invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned concrete specimen quality inspection data compression and management method, and has the same or similar beneficial effects. Repeated parts will not be described again.

[0072] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for compressing and managing quality inspection data of concrete specimens, characterized in that, Methods for compressing and managing concrete specimen quality inspection data include: The quality inspection data of each concrete specimen is numbered according to the production time, and the neighborhood data of each concrete specimen is determined, including: the quality inspection data of the m concrete specimens closest to the current concrete specimen in terms of production time are taken as the neighborhood data, where m is an integer greater than or equal to 2; the data sequence of each concrete specimen is determined by sorting the quality inspection data and neighborhood data of each concrete specimen according to the production time of the corresponding concrete specimen, and the first average value of the data sequence is determined. The first average value is then corrected based on the first type of data in the data sequence that is greater than the first average value and the second type of data that is less than the first average value, to obtain the corrected average value. Based on the quality inspection data, corrected average value, interquartile range and interquartile range multiple of each concrete specimen, the outlier degree of the quality inspection data of each concrete specimen is determined, and the quality inspection data with an outlier degree greater than the threshold is identified as outlier data. The sliding window trajectory compression algorithm is used to compress the quality inspection data of each concrete specimen, excluding outliers, to obtain compressed data. Obtaining the corrected average involves: determining the second average of the first type of data and the third average of the second type of data; determining the degree to which the first average of the concrete specimen data sequence is affected by outlier data based on the first average, the third average, and the second average; and correcting the first average using the degree of influence to obtain the corrected average. Determining the outlier degree of the quality inspection data for each concrete specimen includes: determining the product between the interquartile range and multiples of the interquartile range of the data sequence; and determining the outlier degree of the quality inspection data for each concrete specimen based on the quality inspection data, the corrected mean, and the product. Determining the degree to which the first average value of the concrete specimen data sequence is affected by outliers based on the first average value, the third average value, and the second average value includes: determining the first difference between the first average value and the third average value, and the second difference between the second average value and the first average value; and determining the degree to which the first average value of the concrete specimen data sequence is affected by outliers based on the first difference and the second difference.

2. The method for compressing and managing quality inspection data of concrete specimens according to claim 1, characterized in that, After compressing the quality inspection data of each concrete specimen (excluding outliers) using a sliding window trajectory compression algorithm, the method further includes: Transmit outlier data and compressed data.

3. The method for compressing and managing quality inspection data of concrete specimens according to claim 1, characterized in that, Transmitting outlier and compressed data includes: The index of outliers appearing in the quality inspection data of each concrete specimen and the outlier data are recorded in the outlier data table; Transmit outlier data tables and compressed data.

4. The method for compressing and managing quality inspection data of concrete specimens according to claim 1, characterized in that, After transmitting outlier and compressed data, the method further includes: Decompress the compressed data to obtain decompressed data; Analyze the decompressed data and outlier data to determine the causes of the outliers.

5. The method for compressing and managing quality inspection data of concrete specimens according to claim 1, characterized in that, Quality control data for concrete specimens include at least one of temperature, humidity, and weight.

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