Highway engineering detection comprehensive service management informationization platform and method

Abstract

The application discloses a highway engineering detection comprehensive service management information platform and method, relates to the information technology field of highway engineering detection, and the platform comprises a space verification module, a expected density correction module and a supplementary inspection quantitative planning module.The space verification module constructs a three-dimensional space grid model according to a project design file, divides the project into a plurality of subunits, associates spatial coordinates for detection data, and identifies a leading side and a following side based on a construction completion time, and calculates a reference expected density and a spatial fluctuation characteristic index.The expected density correction module performs non-uniform correction on the expected density of each subunit of the following side based on the quality deviation degree of the leading side when the spatial fluctuation is significant.The supplementary inspection quantitative planning module calculates a final supplementary inspection amount based on cross-side prediction and generates a supplementary inspection paragraph for the subunit which is still insufficient after correction.An acceptance completeness output module outputs an acceptance completeness state by summarizing the judgment results of the modules.The application realizes spatialization, refinement and intelligent management of highway engineering detection services.

Description

Technical Field

[0001] This invention relates to the field of information technology for highway engineering testing, and more specifically, to an information platform and method for comprehensive business management of highway engineering testing. Background Technology

[0002] In highway construction, pavement construction typically employs a split-side construction method, dividing the same road segment into two independent construction zones, with the central median serving as the boundary, and proceeding sequentially. Current highway engineering quality inspection and evaluation standards specify the minimum sampling frequency and acceptance criteria for each testing parameter. However, in practice, the placement of testing points relies heavily on manual experience, leading to the following problems: First, the testing data lacks precise spatial coordinate correlation, making it difficult to achieve spatial traceability of testing points and quantitative assessment of coverage. Second, when there are significant spatial fluctuations in the construction quality on the first side, the subsequent side still uses a uniform point distribution method, easily resulting in insufficient testing in high-risk areas and excessive testing in stable areas. Third, the determination of supplementary testing quantities lacks scientific basis, often relying simply on doubling the sampling or experience-based judgment, making it difficult to balance testing costs with the reliability of quality verification. Fourth, the hierarchical collection of testing data and the acceptance system relies on manual processing, which is inefficient and prone to errors.

[0003] To address the above problems, this invention proposes a solution. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an information platform and method for comprehensive business management of highway engineering testing, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The information platform for comprehensive business management of highway engineering testing includes the following modules: Spatial verification module, which is used to construct a three-dimensional spatial grid model according to the project design documents, divide the project into several sub-units along the project chainage direction, associate spatial coordinates with each test data, identify the preceding and following sides based on the construction completion time of each construction side, evenly distribute the test quantity to all completed sub-units of each construction side to obtain the benchmark expected density, and calculate the spatial fluctuation characteristic index by using the arithmetic mean of all qualified test values ​​in each sub-unit as the representative test value after the foundation layer of the preceding side meets the sufficiency standard. When the spatial fluctuation is significant, a trigger signal is sent to the expected density correction module.

[0007] The expected density correction module is used to calculate the standardized quality deviation representing the relative position of the quality level based on the representative detection values ​​of each sub-unit on the leading side, calculate the correction coefficient that satisfies the total amount conservation constraint based on the standardized quality deviation, multiply the correction coefficient by the baseline expected density to obtain the corrected expected density of each sub-unit on the following side, calculate the following side correction coverage sufficiency representing the degree of matching between the actual detection density and the corrected expected density, and determine the list of insufficient sub-units and the gap amount of each sub-unit when the standard is not met.

[0008] The supplementary inspection quantitative planning module is used to predict the quality of each insufficient sub-unit across sides based on the data from the preceding side, calculate the final supplementary inspection quantity, and generate supplementary inspection segments.

[0009] The acceptance completeness output module is used to summarize the judgment results of each module and output the acceptance completeness status.

[0010] In a preferred embodiment, the three-dimensional spatial mesh model is constructed in the following dimensions: the vertical dimension uses the inspection and evaluation unit as the first-level division unit, and within each inspection and evaluation unit, it is further divided into several sub-units according to the sub-unit division spacing parameter along the direction of increasing project station number;

[0011] The vertical dimension is arranged from bottom to top according to the pavement structure layers of the pavement structure design scheme, and each structure layer is assigned a layer number that increases vertically from bottom to top;

[0012] The road surface is divided into the first construction side and the second construction side according to the cross-sectional design documents in the lateral dimension, and the location of the lanes contained in each side is marked.

[0013] Each subunit's spatial location is determined by four sets of elements: its inspection and evaluation unit number, its serial number within the corresponding inspection and evaluation unit, its corresponding structural layer number, and its construction side number.

[0014] In a preferred embodiment, the spatial verification module has a spatial location input interface in the commission registration process. Each inspection commission enters four spatial location information through this interface: the starting project chainage, the ending project chainage, the structural layer name, and the construction side identifier.

[0015] The platform takes the sub-unit where the starting project chainage is located as the starting sub-unit of the spatial coverage area and the sub-unit where the ending project chainage is located as the ending sub-unit. All sub-units between the two constitute the set of spatial coverage sub-units for this inspection commission.

[0016] Each piece of test data automatically obtains spatial coordinates containing the inspection and evaluation unit number, sub-unit number, structural layer number, and construction side number by inheriting the spatial coverage information of the test order to which it belongs.

[0017] In a preferred embodiment, the process of determining the benchmark expected density includes: calculating the effective construction area for the construction side whose construction status is completed. The effective construction area is equal to the product of the length of the inspection and evaluation unit to which the construction side belongs, the road width of the construction side, and the construction completion coefficient, wherein the construction completion coefficient is 1 when the construction status is completed and 0 when the construction status is not completed.

[0018] The quantity to be inspected is determined by two methods: dividing the effective construction area by the standard area frequency, and multiplying the minimum number of inspection points in the inspection and evaluation unit by the ratio of the effective construction area of ​​the construction side to the sum of the effective areas of all completed construction sides in the same inspection and evaluation unit. The larger of the two values ​​is taken.

[0019] The benchmark expected density is equal to the quantity to be inspected divided by the total number of sub-units on the construction side; where the total number of sub-units is the result of dividing the length of the inspection and evaluation unit by the sub-unit division spacing parameter and rounding up.

[0020] In a preferred embodiment, the criteria for determining the adequacy of the foundation layer are: the total compliance rate is greater than or equal to 1 and the sub-unit coverage rate is equal to 1; wherein, the total compliance rate is equal to the sum of the actual number of qualified test points of all sub-units divided by the quantity to be inspected, and the sub-unit coverage rate is equal to the number of sub-units with an actual number of qualified test points greater than or equal to 1 divided by the total number of sub-units on the construction side.

[0021] Spatial fluctuation characteristic indicators include spatial variation coefficient and quality range ratio; wherein, spatial variation coefficient is defined as the standard deviation of the representative detection values ​​of the leading side of all sub-units divided by its mean; quality range ratio is defined as the difference between the maximum and minimum values ​​of the representative detection values ​​of the leading side of each sub-unit, divided by the difference between the standard acceptance limit and the minimum value;

[0022] When the spatial coefficient of variation is greater than the coefficient of variation threshold and the quality range ratio is greater than the range ratio threshold, the spatial fluctuation is determined to be significant, and a trigger signal is sent to the expected density correction module.

[0023] When any indicator does not exceed the corresponding threshold, the subsequent side uses the baseline expected density to perform the write-off process, and the expected density correction module and the supplementary inspection quantitative planning module are not activated.

[0024] In a preferred embodiment, the standardized quality deviation is defined as the result of subtracting the mean of the representative detection values ​​of the preceding side of all sub-units from the representative detection values ​​of the preceding side of the corresponding sub-unit, and then dividing by the standard deviation of the representative detection values ​​of the preceding side of all sub-units; for blank sub-units with no detection data on the preceding side, a conservative deviation is assigned, which is the minimum value among all existing standardized quality deviations minus the absolute value of the standardized offset.

[0025] The calculation process of the correction coefficient is as follows: the original correction coefficient is obtained by subtracting the product of the adjusted sensitivity parameter and the standardized quality deviation from 1. Then, the original correction coefficients of all completed sub-units are normalized and scaled so that the sum of the correction coefficients of each sub-unit after normalization is equal to the number of completed sub-units, thereby satisfying the constraint that the sum of the expected densities of all sub-units after correction is equal to the total amount of the quantity to be inspected. When the normalized correction coefficient is lower than the lower limit of the correction coefficient, it is truncated to the lower limit value, and the remaining amount released by the truncation is distributed to the remaining sub-units proportionally.

[0026] The adjusted sensitivity parameters are determined as follows: at the overlapping locations where there are qualified test data in the same project chainage segment on both the advance side and the follow side, the cross-side difference of each overlapping location is obtained by subtracting the representative test value of the advance side from the mean of the qualified test values ​​in the follow side sub-unit. The arithmetic mean of all the cross-side differences of the single overlapping locations is calculated to obtain the systematic deviation, and the standard deviation is calculated to obtain the cross-side deviation dispersion.

[0027] The adjusted sensitivity parameter is obtained by multiplying the initial sensitivity parameter by 1 and adding the result of the ratio of the cross-side deviation dispersion to the statistical mean of the cross-side deviation dispersion of similar projects.

[0028] In a preferred embodiment, the calculation method for the back-side correction coverage adequacy is as follows: for each completed sub-unit, take the smaller value between the actual number of qualified test points of the sub-unit and the expected density after correction, and divide the sum of the smaller values ​​of all completed sub-units by the sum of the expected densities after correction of all completed sub-units.

[0029] When the coverage adequacy of the subsequent correction is not less than the threshold of the subsequent correction coverage adequacy, it is considered to meet the standard; when it is less than the threshold of the subsequent correction coverage adequacy, for each sub-unit, the larger value of the expected density after correction minus the actual number of qualified detection points and zero is taken as the gap amount of the sub-unit. Sub-units with gap amounts greater than zero constitute insufficient sub-units.

[0030] In a preferred embodiment, the cross-side prediction of each insufficient sub-unit based on the prior side data to extrapolate the subsequent side quality in the supplementary inspection quantitative planning module includes: taking the representative detection value of the prior side of each insufficient sub-unit plus the systematic deviation as the cross-side prediction value, and determining the prediction uncertainty based on the cross-side deviation dispersion and the project station distance from the sub-unit to the overlapping position.

[0031] The required sample size for verification is calculated based on the difference between the cross-side predicted value and the standard acceptance limit, the prediction uncertainty, the standard deviation of the test operation fluctuation, and the ratio of the corrected expected density to the baseline expected density. The final supplementary inspection quantity is the larger of the rounded-up value of the gap and the required sample size for verification. Continuous insufficient sub-units in the project chainage direction are merged into supplementary inspection segments, and sub-units with zero actual number of qualified test points are forcibly included, with the final supplementary inspection quantity not less than 1.

[0032] Based on the cross-side predicted value and the prediction uncertainty, the expected range of the supplementary inspection result is constructed. After the supplementary inspection is completed, the quality compliance judgment and the cross-side prediction consistency judgment are performed. When the mean of the supplementary inspection exceeds the expected range of the supplementary inspection result and reaches the preset prediction deviation judgment threshold, it is judged as a significant deviation in cross-side prediction. The supplementary inspection data is included in the overlapping position dataset to recalculate the systematic deviation and cross-side deviation dispersion and feed it back to the expected density correction module.

[0033] In a preferred embodiment, the acceptance completeness output module marks the acceptance completeness status as complete when all three conditions are met: the sufficiency of the foundation layer on the first side is met, the sufficiency of the correction on the second side is met, and the quality meets the standards after the supplementary inspection of all insufficient sub-units. The module then uses spatial coordinates to automatically collect the test reports by sub-project, sub-item project, and inspection and evaluation unit and generate a quality inspection and evaluation table.

[0034] The information-based method for comprehensive business management of highway engineering testing includes the following steps: constructing a three-dimensional spatial grid model based on the project design documents, dividing the project into several sub-units along the project chainage direction, entering spatial location information during testing entrustment registration, and associating spatial coordinates with each testing data; identifying the preceding and following sides based on the construction completion time of each construction side, evenly distributing the testing quantity to the completed sub-units to obtain the benchmark expected density, performing a foundation layer sufficiency judgment on the preceding side, and calculating spatial fluctuation characteristic indicators after meeting the standard to determine whether there is significant spatial fluctuation;

[0035] The standardized quality deviation is calculated based on the representative detection values ​​of each sub-unit on the leading side. The sensitivity parameters are adjusted in combination with cross-side deviation analysis. The correction coefficient that satisfies the total amount conservation constraint is calculated. The correction coefficient is multiplied by the baseline expected density to obtain the corrected expected density of each sub-unit on the trailing side. The corrected coverage adequacy of the trailing side is calculated. If it does not meet the standard, the list of insufficient sub-units and the gap amount of each sub-unit are determined.

[0036] For each insufficient subunit, cross-side predicted values ​​are calculated based on the representative detection values ​​of the preceding side and systematic deviations. The required sample size for verification is calculated by combining the prediction uncertainty. The larger value between the rounded-up gap value and the required sample size for verification is taken as the final supplementary inspection value. Consecutive insufficient subunits are merged to generate supplementary inspection segments and construct the expected range of supplementary inspection results. After the supplementary inspection is completed, quality compliance judgment and cross-side prediction consistency judgment are performed. When significant cross-side prediction deviation occurs, the cross-side prediction relationship parameters are updated and feedback is given.

[0037] The completeness condition is that the foundation layer of the first side meets the sufficiency standard, the correction layer of the subsequent side meets the sufficiency standard, and the quality meets the standard after the supplementary inspection of all insufficient sub-units. When all conditions are met, the acceptance completeness status is marked as complete. The completeness is summarized level by level from the inspection and evaluation unit to the unit project. The test reports are automatically collected by sub-project, sub-item project and inspection and evaluation unit using spatial coordinates and a quality inspection and evaluation table is generated.

[0038] The technical effects and advantages of the highway engineering testing integrated business management information platform and method of this invention are as follows:

[0039] This invention constructs a three-dimensional spatial grid model to assign precise spatial coordinates to each piece of inspection data, realizing the transformation of inspection data management from traditional quantitative statistical management to spatial positioning management. This solves the technical problems of disconnect between inspection data and construction spatial location, and the inability to quantitatively assess inspection density distribution in existing highway engineering inspection management. By identifying the preceding and following sides based on construction completion time and performing spatial fluctuation characteristic analysis on the inspected data of the preceding side, when there is significant non-uniform fluctuation in the preceding side inspection data along the project chainage, the expected density correction module uses standardized quality deviation to differentiate the expected density of each sub-unit on the following side. This allows the following side sub-units corresponding to positions with lower quality on the preceding side to receive higher expected inspection density, while the following side sub-units corresponding to positions with good quality on the preceding side have appropriately lower expected density. This achieves spatial optimization of inspection resources without changing the total amount to be inspected, avoiding the problem of insufficient inspection in high-risk areas and redundant inspection in low-risk areas under the traditional uniform allocation method. The supplementary inspection quantitative planning module transforms the determination of supplementary inspection quantities from relying on experience-based judgment to quantitative planning based on statistical analysis through step-by-step calculation of cross-side predicted values, prediction uncertainties, and quality margin indicators. Simultaneously, a mechanism that takes the larger value between the gap quantity and the sample size required for verification ensures that the supplementary inspection quantity for each insufficient sub-unit simultaneously meets the requirements for spatial density sufficiency and quality verification sufficiency. The acceptance completeness output module automatically aggregates test reports into the hierarchical structure of the acceptance system based on spatial coordinates and simultaneously generates a quality inspection evaluation table, reducing the workload of manual compilation and archiving and improving the accuracy and efficiency of acceptance data preparation. Attached Figure Description

[0040] Figure 1This is a schematic diagram of the structure of the highway engineering testing integrated business management information platform of the present invention;

[0041] Figure 2 This is a schematic diagram of the three-dimensional spatial mesh model structure in the highway engineering inspection integrated business management information platform of the present invention;

[0042] Figure 3 This is a schematic diagram of the information management method for comprehensive business management of highway engineering testing according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example: Please refer to Figure 1 As shown, the present invention discloses an information platform for comprehensive business management of highway engineering testing, comprising a spatial verification module, an expected density correction module, a supplementary inspection quantitative planning module, and an acceptance completeness output module connected in sequence.

[0045] Highway pavement construction typically employs a split-side construction method, which refers to dividing the same road section into two independent construction areas in the cross-sectional direction with the central median as the boundary. In this invention, these are defined as the first construction side and the second construction side, respectively.

[0046] The spatial verification module is used to construct a three-dimensional spatial mesh model based on the project design documents. This model divides the project into several sub-units along the project's chainage direction, associates spatial coordinates with each detection data point, identifies the preceding and following sides based on the construction completion time of each construction side, and evenly distributes the required inspection quantity to all completed sub-units to obtain the baseline expected density. After the foundation layer sufficiency of the preceding side is met, the arithmetic mean of all qualified detection values ​​within each sub-unit is used as the representative detection value to calculate the spatial fluctuation characteristic index. When the spatial fluctuation is significant, a trigger signal is sent to the expected density correction module. Specifically:

[0047] Please see Figure 2 As shown, the spatial verification module first constructs a three-dimensional spatial mesh model for detecting spatial positioning and density analysis based on the project design documents. This model is divided into three dimensions, and the specific implementation is as follows:

[0048] The first dimension of the three-dimensional spatial mesh model is the vertical dimension, along the project chainage direction. Using the inspection and evaluation unit as the primary unit, the entire project line is divided into several continuous chainage intervals, each corresponding to an independent inspection and evaluation unit. Within each inspection and evaluation unit, along the increasing chainage direction, it is further divided into several sub-units at fixed intervals. This sub-unit is the smallest spatial granularity unit for the platform to perform spatial density analysis. The fixed interval is defined by the sub-unit division spacing parameter. This indicates the sub-unit spacing parameter. The initial value is set to 100m;

[0049] The second dimension of the three-dimensional spatial grid model is the vertical dimension. According to the project's road surface structure design scheme, the subgrade, base course, base course and surface course are arranged from bottom to top to form an ordered sequence of structural layers. Each structural layer in the sequence is assigned a layer number that increases vertically from bottom to top to ensure that the spatial location of each structural layer can be uniquely identified.

[0050] The third dimension of the three-dimensional spatial mesh model is the horizontal dimension. According to the project cross-section design documents, the road surface is divided into the first construction side and the second construction side, and the positions of the lanes contained in each side are marked to ensure that the spatial range of the construction side is consistent with the actual construction area. In the three-dimensional spatial mesh model, each sub-unit determines its spatial position through four sets of unique identification elements. These four sets of elements are: the inspection and evaluation unit number to which the sub-unit belongs, the serial number of the sub-unit in the corresponding inspection and evaluation unit, the structural layer number corresponding to the sub-unit, and the construction side number where the sub-unit is located. Through these four sets of elements, the accurate positioning and unique identification of the sub-unit can be achieved.

[0051] To enable spatial traceability of testing data, the spatial verification module adds a spatial location input interface to the platform's commission registration process. This interface is used to input the spatial location information of each testing commission. The specific implementation process is as follows: When registering each testing commission, four core spatial location information items are accurately entered through the aforementioned spatial location input interface: starting project chainage, ending project chainage, structural layer name, and construction side identifier. The entered information must be consistent with the project design documents and the actual construction situation to ensure the accuracy of spatial positioning.

[0052] The platform automatically matches sub-units in the 3D spatial mesh model based on the entered starting and ending project chainages. The sub-unit containing the starting project chainage is taken as the starting sub-unit of the spatial coverage of the commission, and the sub-unit containing the ending project chainage is taken as the ending sub-unit. All sub-units between the two constitute the set of spatial coverage sub-units for the inspection commission. Each piece of inspection data is associated with its respective inspection commission order. By inheriting the spatial coverage information of the commission order, it automatically obtains its own spatial coordinates. These spatial coordinates contain four elements: inspection and evaluation unit number, sub-unit sequence number, structural layer number, and construction side number, realizing a one-to-one correspondence between inspection data and spatial location.

[0053] Furthermore, for each inspection and evaluation unit, the construction completion time is recorded independently for its first and second construction sides. Based on the recorded construction completion times, the identification of the preceding and subsequent sides is completed. The specific implementation rules are as follows:

[0054] The preceding and following sides are identified based on the completion time of each construction side: when only one side is completed, that side is the preceding side and the other side is the following side; when the completion time of the following side is later than that of the preceding side, the identification remains unchanged; when both sides are completed at the same time, the preceding and following sides are not distinguished, the expected density correction module and the supplementary inspection quantitative planning module are not activated, and the regular verification process is executed.

[0055] The effective construction area is calculated for each construction side where the construction status is "completed". Based on the effective construction area, the required inspection quantity is determined, and then the baseline expected density is calculated using the inspection quantity. The specific implementation process is as follows: The calculation of the effective construction area is based on the construction status of the construction side, and only the construction side where the construction status is "completed" is calculated. The calculation formula is as follows: In the formula, A is the effective construction area, Leu is the length of the inspection and evaluation unit to which the construction side belongs, W is the road width of the construction side, and α is the construction completion coefficient. When the construction status of the construction side is completed, α is 1; when the construction status is not completed, α is 0.

[0056] After determining the effective construction area A, the quantity to be inspected on this construction side is determined by calculating the larger value based on both area frequency and minimum number of points. The calculation formula is as follows:

[0057] In the formula, N is the quantity to be inspected, and F is the standard area frequency of the inspection parameter. This refers to the minimum number of inspection points per inspection and evaluation unit, determined according to the current highway engineering quality inspection and evaluation standards. It is the sum of the effective areas of all completed construction sides within the same inspection and evaluation unit;

[0058] For example, consider the inspection and evaluation unit of a certain highway project. For example, the length Leu of this inspection and evaluation unit is 2000m, the width W of the road surface on the construction side is 11.75m, and the construction status is completed, i.e., α=1; the detection parameter is the compaction degree of the upper base layer, the standard area frequency F is 1000m² / point, and the minimum number of detection points in the inspection and evaluation unit is... Take 6 points; the effective construction area A on the construction side is equal to 2000 multiplied by 11.75 multiplied by 1, which equals 23500m²; calculated by area frequency, it is 24, and calculated by the minimum number of points, it is 6. The quantity to be inspected N is the larger of the two, which is 24 groups.

[0059] The calculated inspection quantity N is evenly distributed to all completed sub-units on the construction side to obtain the baseline expected density. Benchmark Expected Density Equals the quantity to be inspected N divided by the total number of sub-units on this construction side. Benchmark Expected Density This represents the number of qualified inspection points that each sub-unit should have under ideal uniform distribution conditions; the total number of sub-units on the construction side. The total number of sub-units on the construction side in the 3D spatial mesh model is calculated according to the following rules: Equal to the length of the inspection and evaluation unit Sub-cell spacing after rounding up The result is independent of the construction status; the number of completed sub-units This represents the number of sub-units whose construction status is completed on the construction side. ;

[0060] The space verification module counts the actual number of inspection points that have received a pass rating within each sub-unit, and defines this number as the actual number of pass inspection points in the sub-unit. And based on the actual number of qualified inspection points in all sub-units. The calculation includes two sufficiency assessment indicators for the foundation layer; the first indicator is the total compliance rate Q, which is equal to the actual number of qualified inspection points in all sub-units of the construction side. The sum of the values ​​is divided by the quantity to be inspected, N; the second indicator is the sub-unit coverage rate, Rc, which is equal to the actual number of qualified inspection points in the sub-unit. The number of sub-units greater than or equal to 1 divided by the total number of sub-units on this construction side;

[0061] When the total compliance rate Q is greater than or equal to 1 and the sub-unit coverage rate Rc is equal to 1, the sufficiency of the foundation layer on the construction side is deemed to be compliant; if any indicator is not met, it is deemed to be non-compliant and the subsequent supplementary inspection process needs to be initiated.

[0062] After the verification of the foundation layer on the pilot side is completed, spatial fluctuation characteristic analysis is performed on all the inspected data on the pilot side to quantitatively assess the degree of fluctuation of the test data in the project chainage direction and determine whether the benchmark expected density needs to be revised on the subsequent side. The specific implementation process is as follows: For each sub-unit on the pilot side with test data, the arithmetic mean of all qualified test values ​​in the sub-unit is taken as the representative test value of the sub-unit. , represents the sequence number of the completed sub-unit on the construction side, with a value range of . Based on representative detection values ​​of each sub-unit on the advance side Calculate the mean of the representative detection values ​​of the preceding side for all sub-units, and denot it as the preceding side mean. The standard deviation of the representative test values ​​of the preceding side for all sub-units is denoted as the preceding side standard deviation. To ensure the accuracy of the statistical results;

[0063] Two spatial fluctuation characteristic indicators were calculated based on the above statistics.

[0064] The first spatial volatility characteristic indicator is the spatial coefficient of variation Cᵥ, which is equal to the standard deviation of the preceding side. Divide by the mean of the preceding side Multiply by 100%, which can be expressed by the formula: ;

[0065] The second spatial fluctuation characteristic indicator is the quality range ratio R. e This is equal to the maximum value among the representative detection values ​​of each sub-unit on the leading side. and minimum value Difference divided by the standard acceptance limit and minimum value The difference can be expressed by the formula: In the formula, This is the pass / fail value determined based on the current highway engineering quality inspection and evaluation standards;

[0066] Preset the judgment thresholds for two indicators, including the coefficient of variation threshold. and range ratio threshold Coefficient of variation threshold The initial value is set to 0.5%, and the range ratio threshold is... The initial value is 1.0, and the threshold can be adjusted according to the actual quality control needs of the project.

[0067] When both conditions are met and This indicates that the preliminary side detection data exhibits significant non-uniform fluctuations along the project's chainage direction. If the subsequent side adopts a uniform benchmark expected density... This will lead to inaccurate determination of the sufficiency of the base layer. At this time, the space verification module sends a trigger signal to the expected density correction module and simultaneously transmits the following data: representative detection values ​​of each sub-unit on the advance side. , the mean of the preceding side First-side standard deviation , Rear-side benchmark expected density The actual number of qualified inspection points in each subunit on the rear side. This is provided for the expected density correction module to perform differential correction;

[0068] when or This indicates that the fluctuations in the preliminary detection data are within a reasonable range, and the subsequent side will continue to use the baseline expected density. When executing the write-off process, the expected density correction module and the supplementary inspection quantitative planning module are not activated.

[0069] The expected density correction module is used to calculate the standardized quality deviation representing the relative position of the quality level based on the representative detection values ​​of each sub-unit on the leading side. Based on the standardized quality deviation, it calculates a correction coefficient that satisfies the total quantity conservation constraint. Multiplying the correction coefficient by the baseline expected density yields the corrected expected density of each sub-unit on the following side. It then calculates the following side correction coverage sufficiency, representing the degree of matching between the actual detection density and the corrected expected density. If the coverage is insufficient, it identifies the list of inadequate sub-units and the gap amount for each sub-unit. Specifically:

[0070] First, calculate the standardized quality deviation for each sub-unit with detection data on the pilot side. The relative position of the representative detection value of the preceding side of this sub-unit within the distribution of representative detection values ​​of the preceding side of all sub-units is represented by the following formula: In the formula, This represents the leading-side test value of the kth completed subunit. and These are the mean and standard deviation of the representative detection values ​​from the preliminary side of all sub-units, respectively;

[0071] Standardized quality deviation A positive value indicates that the quality of the preceding side at the location of this sub-unit is higher than the overall average level of the preceding side, while a negative value indicates that it is lower than the overall average level of the preceding side. The larger the absolute value, the more significant the deviation.

[0072] For blank sub-cells with no detection data on the leading side, the expected density correction module assigns a conservative deviation. Conservative deviation Equal to all existing standardized quality deviations Minimum value minus standardized offset The absolute value; the standardized offset The correction coefficient assigned to the conservative deviation of the blank sub-unit on the pilot side is used to improve the quality risk assessment level of the blank sub-unit. The initial value is 1.0, which can be adjusted according to the project's construction risk level. The higher the risk, the larger the value, ranging from 1.0 to 2.0. The basis for this assignment method is that the fact that no testing was performed at a certain location on the pilot side indicates that there may be unfavorable construction conditions for sampling at that location. Therefore, the quality risk assessment value for that location is not lower than the risk level of the worst known location. , The smaller the value, the higher the quality risk.

[0073] The position of a sub-unit where there is already detection data on the rear side and also on the same position on the front side is defined as an overlapping position. The number of overlapping positions is... This indicates that only sub-units with a completed construction status and qualified inspection data are included in the statistics; overlapping sub-units must simultaneously meet the following conditions: the construction status is completed; there are ≥3 qualified inspection points on both the preceding and following sides; the inspection data has no outliers, and the outlier judgment standard is deviation from the sub-unit mean ±3σ; when the number of overlapping positions... When necessary, the historical overlapping sub-unit parameter library of the same type of project is used for supplementation: it is stored according to highway level (highway, first class, second class) and pavement structure type (asphalt, cement). Historical data within the same region within the past 3 years is used first, and if insufficient, it is supplemented by interpolation of data from adjacent regions.

[0074] At each overlapping location, calculate the cross-side difference for a single overlapping location. Where m represents the overlapping position sub-unit and , This is the arithmetic mean of all qualified test values ​​within the subsequent sub-unit at the overlapping location, calculated using the same method as the representative test value from the preceding side. Consistency; To Single overlapping position cross-side difference at each overlapping position Calculating the arithmetic mean yields the systematic deviation. This reflects the constant difference in overall quality level between the two construction sides, and the standard deviation is calculated to obtain the cross-side deviation dispersion. This reflects the level of inherent random fluctuations when using leading-side data to predict following-side quality;

[0075] Based on cross-side deviation dispersion Dynamically adjust sensitivity parameters To ensure that the sensitivity of the correction coefficient to quality risk matches the reliability of cross-side prediction, the sensitivity parameter adjustment formula is as follows: In the formula, This is the initial sensitivity parameter, with an initial value of 0.2. It can be adjusted within the range of 0.1 to 0.5 based on the quality sensitivity of the detection parameter. The statistical mean of the cross-side deviation dispersion for similar projects is provided by the platform's built-in engineering parameter library, categorized and matched according to highway grade and pavement structure type; adjustment logic: The larger the value, the more significant the translateral prediction fluctuation, requiring an increase in the sensitivity parameter. , so that the correction factor A stronger response to quality deviations leads to increased detection density in high-risk areas; conversely, a lower sensitivity parameter is used to avoid over-correction.

[0076] Standardized quality deviation based on each sub-unit Calculate the correction factor Correction factor Deviation from standardized quality The relationship between them is monotonically decreasing, i.e., standardized quality deviation. A smaller value indicates a worse quality on the leading side, thus requiring a correction factor. The larger the value, the higher the expected density at that position on the rear side; the correction factor... The calculation is completed in two steps: the first step is to calculate the original correction coefficient. In the formula, To control the correction magnitude for the adjusted sensitivity parameters;

[0077] The second step is to adjust the original correction coefficients for all sub-units. Normalization scaling is performed to satisfy the total quantity conservation constraint, which requires that the sum of the expected densities of all sub-units after correction still equals the required quantity N on the next side. That is, the correction operation only redistributes the expected detection density at each location without changing the total required quantity. The normalized correction coefficient... The calculation formula is: In the formula, Let be the number of completed sub-units on the construction side, and let i be the sequence number of the completed sub-units during the summation process. The value range is the same as k, where i is the number of completed sub-units. ;

[0078] At the same time, based on the preset lower limit of the correction coefficient Lower limit of correction factor To avoid insufficient detection due to excessively low expected density of the rear sub-units, a minimum correction coefficient is set, with an initial value of 0.5, ensuring that the corrected expected density of the sub-units is not lower than 50% of the baseline expected density; the preset lower limit of the correction coefficient is set. With the normalized correction coefficient Comparison:

[0079] When the normalized correction coefficient Less than the lower limit of the correction factor The correction factor will be used at that time. Truncation is the lower limit of the correction factor. And the remaining amount released after the cut-off will be allocated proportionally to the remaining sub-units;

[0080] Based on the correction coefficients obtained above Combined with the expected density of the subsequent side benchmark The expected density of each completed sub-unit on the subsequent side is adjusted differentially. The specific implementation process is as follows: each sub-unit on the preliminary side is matched one-to-one with the sub-units in the corresponding project chainage interval on the subsequent side to ensure the adjustment coefficient is adjusted accordingly. The spatial position of the subsequent side sub-units is precisely matched, and the matching basis is the inspection and evaluation unit number, sub-unit serial number and structural layer number to which the sub-unit belongs;

[0081] For each completed sub-unit on the rear side, based on its corresponding correction coefficient Compared with the baseline expected density Multiply to obtain the corrected expected density of the sub-unit. The corrected expected density of each sub-unit is no longer a uniform constant value, but a non-uniform distribution that varies with spatial location. That is, the corrected expected density of sub-units with low quality on the leading side or blank leading side locations will be adjusted accordingly. Higher expected density after position correction for sub-cells with good quality on the leading side. Lower;

[0082] After the correction is completed, the corrected expected density of all completed sub-units on the rear side. Calculate the post-line side correction coverage adequacy That is, for each sub-unit, take the actual number of qualified inspection points for that sub-unit. The corrected expected density of the sub-unit The smaller of the two values, summed and divided by the smaller of all sub-units, is the expected density after correction for all sub-units. Summation, expressed by the formula: ;

[0083] Rear side correction coverage adequacy A value of 1.0 indicates that the actual detection density distribution on the subsequent side completely matches the corrected expected density distribution, indicating sufficient coverage of the subsequent side correction. A value less than 1.0 indicates insufficient detection density in high-demand locations;

[0084] The adequacy of the backside correction coverage Compared with the preset backside coverage adequacy threshold Comparison, subsequent side correction coverage sufficiency threshold The critical value used to determine whether the detection density distribution after back-side correction meets the requirements is initially set at 0.90, meaning that a back-side correction coverage adequacy of 90% or higher is considered satisfactory. This value can be adjusted according to the project acceptance criteria. Greater than or equal to the subsequent side-corrected coverage sufficiency threshold When the adequacy of the subsequent side correction is deemed to be satisfactory; when the coverage of the subsequent side correction is deemed satisfactory... Less than the backside correction coverage sufficiency threshold If the condition is deemed insufficient, the gap amount for each sub-unit is calculated. gap Equal to the corrected expected density Subtract the actual number of qualified inspection points in the sub-unit The larger of the following and 0 values ​​is taken; gap amount Sub-units with a value greater than 0 constitute a list of insufficient sub-units.

[0085] The supplementary inspection quantitative planning module is used to calculate the final supplementary inspection quantity and generate supplementary inspection segments for each insufficient sub-unit based on cross-side prediction of subsequent side quality using data from the preceding side; specifically:

[0086] The supplementary quantitative planning module first calculates the cross-side predicted value for each inadequate sub-unit. Cross-lateral predicted values This is the expected quality value at this location on the subsequent side, calculated based on the measured data of the preceding side and the systematic deviation across the same project's chainage segment; the calculation formula is: In the formula, This is the representative detection value of the preceding side of the sub-unit; the overlapping sub-unit is taken as... The blank sub-cells are taken as the corrected interpolated mean. This is a systematic bias;

[0087] For blank sub-cells with no detection data on the leading side, their representative detection value on the leading side is the corrected interpolated mean, calculated using the following formula: In the formula, , These are the representative detection values ​​of the nearest data sub-units to the blank sub-unit, located in front of and behind it, respectively. This is a systematic bias. The distance attenuation coefficient is closer to 1 the closer it is to the overlapping position, and closer to 0.3 the farther it is from the overlapping position. The initial value is 0.5.

[0088] Furthermore, the prediction uncertainty for each inadequate subunit is calculated. Predicting uncertainty It is composed of two parts; the first part is the cross-side deviation dispersion. The first part reflects the inherent fluctuations of the cross-lateral prediction method itself; the second part is the uncertainty of spatial distance attenuation. This reflects the impact of the longitudinal distance of the sub-unit from the cross-side prediction relationship calibration position, i.e., the overlapping position, on the prediction reliability. The farther the sub-unit is from the overlapping position along the highway project chainage, the more likely its geological conditions and material batches are to deviate from the situation at the overlapping position, and the lower the prediction reliability; spatial distance attenuation uncertainty. K The calculation formula is: In the formula, Let the distance be the project station number from the kth insufficient sub-unit to the nearest overlapping project station number. As a reference distance, a baseline distance is used to calculate the spatial distance attenuation uncertainty, reflecting the average distribution density of overlapping locations. This is used to quantify the degree to which the prediction reliability decreases as the subunit is farther from the overlapping location. It is calculated by taking the average project station spacing between all overlapping locations; when the number of overlapping locations... When equal to 1, the reference distance Take half the length of the test evaluation unit;

[0089] Forecast uncertainty Equal to cross-side deviation dispersion The square of the uncertainty of spatial distance decay K The square root of the sum of the squares of is expressed by the formula: ;

[0090] Based on cross-side predictions and standard acceptance limits Calculate the mass margin index for each inadequate sub-unit. Quality margin index This indicates the safety distance between the predicted quality level at this location and the acceptance criteria, given the current prediction accuracy; quality margin index. The calculation formula is as follows: ;

[0091] Quality margin index A positive value indicates that the cross-side predicted value is above the acceptable line and has a safety margin; the quality margin index A negative value indicates that the cross-side predicted value is below the acceptable line or the safety margin is insufficient; quality margin index. The smaller the absolute value, the closer the predicted value across the side is to the boundary of the qualified line. The more actual test data is needed to verify the determination of the true quality status at this position.

[0092] Based on mass margin index Calculate the sample size required for validation of each inadequate subunit. The calculation formula is as follows: In the formula, The confidence coefficient is referenced from the statistical test confidence coefficient value specification in the "Highway Engineering Quality Inspection and Evaluation Standard". The verification confidence level is 2.0 when it is 95%, 1.645 when it is 90%, and 1.44 when it is 85%. It can be manually adjusted in the platform according to the project quality control level. To ensure that the standard deviation of the detection operation reflects the level of random fluctuation in the detection operation, it is calculated by statistically analyzing the standard deviations of all qualified detection points within each completed sub-unit on the pilot side, and then taking the arithmetic mean of the standard deviations of all sub-units. The formula is as follows: ,in Let be the standard deviation of the detection points within the k-th completed sub-unit. This represents the absolute value of the distance between the predicted value and the passing line. To correct the coefficient weights, The corrected expected density for this sub-unit. As the baseline expected density; when hour, Increasing the detection density subunit proportionally requires higher verification accuracy. hour, Reducing the detection density sub-unit proportionally can decrease verification accuracy.

[0093] Based on the preset upper limit of the validation sample size The upper limit of the validation sample size To avoid wasting testing resources due to excessive re-inspection of a single sub-unit, the maximum sample size required for validation is determined by the corrected expected density and the upper limit factor, and is equal to the corrected expected density of that sub-unit. Multiply by the upper limit multiplier γ and round up. The upper limit multiplier γ is a correction coefficient that controls the upper limit of the verification sample size. The initial value is 2.0, which can be adjusted according to the project's testing resource configuration. The value range is 1.5~3.0". The value of γ is manually set by the user in the platform, and the default value is 2.0.

[0094] When the required sample size for validation Exceeding the upper limit of the validation sample size At that time, the required sample size will be verified. Truncating to the upper limit of the validation sample size And mark the location where a special quality investigation needs to be initiated;

[0095] And for each insufficient sub-unit, take the gap amount. Rounding up and the sample size required for validation The larger of the two values ​​is taken as the final supplementary inspection quantity for that sub-unit. ,Right now When the gap amount G KThe rounded-up value is greater than the sample size required for validation. When this occurs, it indicates that the need for supplementary testing at that location is dominated by spatial density gaps; when the required sample size for verification... Greater than the gap amount G K When the value is rounded up, it indicates that the re-inspection requirement at that location is dominated by the quality verification accuracy requirement; this tightening mechanism ensures that the re-inspection quantity of each insufficient sub-unit simultaneously meets the spatial density sufficiency requirement and the quality verification sufficiency requirement;

[0096] Simultaneously, the supplementary quantitative planning module merges insufficient sub-units with consecutive sub-unit numbers in the project chainage direction into continuous supplementary inspection segments, while clarifying that: all All zero-detection sub-units, regardless of their continuity, are forcibly included in the supplementary inspection segment. If zero-detection sub-units are discontinuous, a separate supplementary inspection segment is generated, for example, the starting project chainage = the ending project chainage = the center project chainage of the sub-unit. Each supplementary inspection segment must ensure that all sub-units within the segment have been supplemented and inspected. And the final amount of supplementary testing That is, the zero-detection subunit Ensure that the sub-unit coverage rate Rc=1 after the supplementary inspection;

[0097] For each insufficient sub-unit, construct an expected interval for supplementary inspection results. The lower bound of the expected interval for supplementary inspection results is equal to the cross-side predicted value. Subtract confidence coefficient Multiply by the forecast uncertainty The upper bound is equal to the cross-lateral predicted value. Add confidence coefficient Multiply by the forecast uncertainty That is, the expected range of the supplementary test results is... The lower bound is The upper boundary is ;

[0098] After the supplementary inspection data is entered into the platform, the supplementary inspection quantitative planning module takes the total final supplementary inspection quantity for each sub-unit that has completed supplementary inspection. Mean of a sample Two judgments are performed simultaneously. The first judgment is the quality compliance judgment: when Greater than or equal to the standard acceptance limit When the quality of the location is deemed acceptable, Less than the standard acceptance limit The first criterion is that the quality at that location is unqualified; the second criterion is the cross-lateral prediction consistency criterion: when The translateral prediction is considered valid when the result of the re-examination falls within the expected range of that location. When the result falls outside the expected range of the supplementary test, it is determined as a translateral prediction bias; when the mean of the supplementary test is... Exceeding the expected range of the supplementary test results When the result is 10 or above, it is considered a significant deviation in cross-lateral prediction; the result exceeds the expected range of the supplementary test but does not reach the expected range. When this occurs, it is determined to be a minor deviation, and no parameter update is performed; among which, The initial value for the prediction deviation threshold is 10%, which can be adjusted within the range of 5% to 20% according to the project quality control requirements.

[0099] Each time a significant deviation in cross-side prediction is identified, the cross-side prediction relationship parameters are updated immediately. The updated systematic deviation and cross-side deviation dispersion are synchronized to the supplementary testing quantitative planning module, and the prediction uncertainty of the untested sub-units is recalculated. This leads to an adjustment of the validation sample size, and the corrected expected density is recalculated for the insufficient sub-units that have not yet started supplementary testing. The upper limit for parameter iteration updates within a single test evaluation unit is 3 times to avoid over-optimization.

[0100] When a significant cross-side prediction bias occurs, the supplementary quantitative programming module updates the cross-side prediction relationship parameters according to the following steps: First, the mean of the subsequent side measurements at the supplementary inspection location is calculated. Compared with the representative detection value of the preceding side at this location As a new set of paired data; secondly, this paired data is added to the existing overlapping location dataset to increase the number of overlapping locations. Updated to +1; then, for the updated +1 overlapping location recalculation of all single-overlapping location cross-side differences. Finally, the arithmetic mean of the cross-side differences for all updated single-overlap positions is calculated to obtain the updated systematic bias. The updated cross-side deviation dispersion is obtained by calculating the standard deviation. The updated parameters are fed back to the expected density correction module, enabling the insufficient sub-units that have not yet completed supplementary inspection to obtain the optimized and corrected expected density.

[0101] The acceptance completeness output module is used to summarize the judgment results of each module and output the acceptance completeness status.

[0102] The acceptance completeness output module will determine the acceptance completeness status of each structural layer and each test parameter in each inspection and evaluation unit according to the following three conditions: The first condition is that the sufficiency of the foundation layer of the pilot side meets the standard. Specifically, the pilot side total compliance rate Q calculated by the space verification module is greater than or equal to 1 and the pilot side sub-unit coverage rate Rc is equal to 1.

[0103] The second condition is that the adequacy of the subsequent side correction is met, specifically requiring that the coverage adequacy of the subsequent side correction calculated by the expected density correction module be satisfactory. Greater than or equal to the subsequent side-corrected coverage sufficiency threshold When the expected density correction module is not triggered, i.e., the spatial fluctuation characteristics of the preceding side are not significant, the second condition is replaced by the sufficiency judgment of the foundation layer on the following side meeting the standard; the third condition is that all insufficient sub-units output by the supplementary inspection quantitative planning module have been supplemented, and each sub-unit that has been supplemented is qualified in the quality compliance judgment; when all three conditions are met, the acceptance completeness status of the structural layer and the test parameter of the inspection and evaluation unit is marked as complete; when any condition is not met, it is marked as incomplete, and the specific condition number and corresponding data value of the unmet condition are recorded.

[0104] The above judgment results are mapped to the acceptance system structure stipulated in the current highway engineering quality inspection and evaluation standards; this acceptance system structure is divided into four levels: unit project, sub-project, sub-item project, and inspection and evaluation unit; the mapping process is carried out according to the following path:

[0105] The hierarchical mapping from inspection and evaluation units to sub-items, sub-sections, and unit projects shall be completed in accordance with the current "Highway Engineering Quality Inspection and Evaluation Standards", and the correspondence between testing parameters and inspection items shall comply with the standard requirements.

[0106] After mapping, the acceptance completeness output module summarizes the completeness level by level from bottom to top according to the system architecture. The completeness judgment rule at the inspection item level is: when an inspection item under a sub-item project is marked as complete in all inspection and evaluation units covered by that sub-item project, the inspection item is judged as complete. The completeness judgment rule at the sub-item project level is: when all mandatory inspection items determined according to the inspection and evaluation standards under a sub-item project are judged as complete, the sub-item project is judged as complete. The completeness judgment rule at the section project level is: when all sub-items under a section project are judged as complete, the section project is judged as complete. The completeness judgment rule at the unit project level is: when all sub-projects are judged as complete, the unit project is judged as complete.

[0107] After the unit project is deemed complete, the acceptance completeness output module uses the spatial verification module to assign spatial coordinates to each test report, automatically aggregating all reports according to a three-level hierarchy: sub-project, sub-item, and inspection and evaluation unit. Specifically, the platform matches the inspection and evaluation unit number plus the structural layer number in the spatial coordinates with a pre-set engineering system mapping table, enabling automatic aggregation of test reports to sub-projects and sub-items. The spatial coordinates are used to automatically complete the hierarchical aggregation of test reports, simultaneously generating a quality inspection and evaluation table. The statistical information of the measured values ​​in the table is extracted and filled in from the calculation results of each module. The correction analysis report from the expected density correction module and the verification record from the supplementary inspection quantitative planning module are included as attachments.

[0108] Please see Figure 3As shown, this invention discloses an information-based method for comprehensive business management of highway engineering testing, including the following steps: constructing a three-dimensional spatial grid model based on the project design documents, dividing the project into several sub-units along the project chainage direction, entering spatial location information and associating spatial coordinates with each testing data point during testing commission registration; identifying the preceding and following sides based on the construction completion time of each construction side, evenly distributing the testing quantity to the completed sub-units to obtain the benchmark expected density, performing a foundation layer sufficiency judgment on the preceding side, and calculating spatial fluctuation characteristic indicators after meeting the standard to determine whether there is significant spatial fluctuation;

[0109] The standardized quality deviation is calculated based on the representative detection values ​​of each sub-unit on the leading side. The sensitivity parameters are adjusted in combination with cross-side deviation analysis. The correction coefficient that satisfies the total amount conservation constraint is calculated. The correction coefficient is multiplied by the baseline expected density to obtain the corrected expected density of each sub-unit on the trailing side. The corrected coverage adequacy of the trailing side is calculated. If it does not meet the standard, the list of insufficient sub-units and the gap amount of each sub-unit are determined.

[0110] For each insufficient subunit, cross-side predicted values ​​are calculated based on the representative detection values ​​of the preceding side and systematic deviations. The required sample size for verification is calculated by combining the prediction uncertainty. The larger value between the rounded-up gap value and the required sample size for verification is taken as the final supplementary inspection value. Consecutive insufficient subunits are merged to generate supplementary inspection segments and construct the expected range of supplementary inspection results. After the supplementary inspection is completed, quality compliance judgment and cross-side prediction consistency judgment are performed. When significant cross-side prediction deviation occurs, the cross-side prediction relationship parameters are updated and feedback is given.

[0111] The completeness condition is that the foundation layer of the first side meets the sufficiency standard, the correction layer of the subsequent side meets the sufficiency standard, and the quality meets the standard after the supplementary inspection of all insufficient sub-units. When all conditions are met, the acceptance completeness status is marked as complete. The completeness is summarized level by level from the inspection and evaluation unit to the unit project. The test reports are automatically collected by sub-project, sub-item project and inspection and evaluation unit using spatial coordinates and a quality inspection and evaluation table is generated.

[0112] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0114] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0117] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive information platform for highway engineering testing business management, characterized in that: The module includes the following modules: a spatial verification module, which is used to construct a three-dimensional spatial grid model based on the project design documents and divide the project into several sub-units along the project chainage direction, associate spatial coordinates with each detection data, identify the preceding and following sides based on the construction completion time of each construction side, evenly distribute the inspection quantity to all completed sub-units of each construction side to obtain the benchmark expected density, and calculate the spatial fluctuation characteristic index by using the arithmetic mean of all qualified detection values ​​in each sub-unit as the representative detection value after the foundation layer of the preceding side meets the sufficiency standard. When the spatial fluctuation is significant, a trigger signal is sent to the expected density correction module. The expected density correction module is used to calculate the standardized quality deviation representing the relative position of the quality level based on the representative detection values ​​of each sub-unit on the leading side, calculate the correction coefficient that satisfies the total amount conservation constraint based on the standardized quality deviation, multiply the correction coefficient by the baseline expected density to obtain the corrected expected density of each sub-unit on the following side, calculate the following side correction coverage sufficiency representing the degree of matching between the actual detection density and the corrected expected density, and determine the list of insufficient sub-units and the gap amount of each sub-unit when the standard is not met. The supplementary inspection quantitative planning module is used to predict the quality of the subsequent side based on the data from the preceding side for each insufficient sub-unit, calculate the final supplementary inspection quantity, and generate supplementary inspection segments. The cross-side prediction of the quality of the subsequent side based on the data from the preceding side for each insufficient sub-unit includes: using the representative detection value of the preceding side of each insufficient sub-unit plus the systematic deviation as the cross-side prediction value, and determining the prediction uncertainty based on the cross-side deviation dispersion and the project station distance from the sub-unit to the overlapping position. The required sample size for verification is calculated based on the difference between the cross-side predicted value and the standard acceptance limit, the prediction uncertainty, the standard deviation of the test operation fluctuation, and the ratio of the corrected expected density to the baseline expected density. The final supplementary inspection quantity is the larger of the rounded-up value of the gap and the required sample size for verification. Continuous insufficient sub-units in the project chainage direction are merged into supplementary inspection segments, and sub-units with zero actual number of qualified test points are forcibly included, with the final supplementary inspection quantity not less than 1. Based on the cross-side predicted value and the prediction uncertainty, the expected range of the supplementary inspection result is constructed. After the supplementary inspection is completed, the quality compliance judgment and the cross-side prediction consistency judgment are performed. When the mean of the supplementary inspection measurement exceeds the expected range of the supplementary inspection result and reaches the preset prediction deviation judgment threshold, it is judged as a significant deviation in cross-side prediction. The supplementary inspection data is included in the overlapping position dataset to recalculate the systematic deviation and the cross-side deviation dispersion and feed it back to the expected density correction module. The acceptance completeness output module is used to summarize the judgment results of each module and output the acceptance completeness status.

2. The information platform for comprehensive business management of highway engineering testing according to claim 1, characterized in that, The three-dimensional spatial mesh model is constructed through the following dimensions: the vertical dimension uses the inspection and evaluation unit as the first-level division unit, and within each inspection and evaluation unit, it is further divided into several sub-units according to the sub-unit spacing parameter along the direction of increasing project station number; The vertical dimension is arranged from bottom to top according to the pavement structure layers of the pavement structure design scheme, and each structure layer is assigned a layer number that increases vertically from bottom to top; The road surface is divided into the first construction side and the second construction side according to the cross-sectional design documents in the lateral dimension, and the location of the lanes contained in each side is marked. Each subunit's spatial location is determined by four sets of elements: its inspection and evaluation unit number, its serial number within the corresponding inspection and evaluation unit, its corresponding structural layer number, and its construction side number.

3. The information platform for comprehensive business management of highway engineering testing according to claim 2, characterized in that, The spatial verification module has a spatial location input interface in the commission registration process. Each inspection commission enters four spatial location information through this interface: the starting project chainage, the ending project chainage, the structural layer name, and the construction side identification. The platform takes the sub-unit where the starting project chainage is located as the starting sub-unit of the spatial coverage area and the sub-unit where the ending project chainage is located as the ending sub-unit. All sub-units between the two constitute the set of spatial coverage sub-units for this inspection commission. Each piece of test data automatically obtains spatial coordinates containing the inspection and evaluation unit number, sub-unit number, structural layer number, and construction side number by inheriting the spatial coverage information of the test order to which it belongs.

4. The information platform for comprehensive business management of highway engineering testing according to claim 1, characterized in that, The process of determining the benchmark expected density includes: calculating the effective construction area for the construction side whose construction status is completed. The effective construction area is equal to the product of the length of the inspection and evaluation unit to which the construction side belongs, the road width of the construction side, and the construction completion coefficient. The construction completion coefficient is 1 when the construction status is completed and 0 when the construction status is not completed. The quantity to be inspected is determined by two methods: dividing the effective construction area by the standard area frequency, and multiplying the minimum number of inspection points in the inspection and evaluation unit by the ratio of the effective construction area of ​​the construction side to the sum of the effective areas of all completed construction sides in the same inspection and evaluation unit. The larger of the two values ​​is taken. The benchmark expected density is equal to the quantity to be inspected divided by the total number of sub-units on the construction side; where the total number of sub-units is the result of dividing the length of the inspection and evaluation unit by the sub-unit division spacing parameter and rounding up.

5. The information platform for comprehensive business management of highway engineering testing according to claim 1, characterized in that, The criteria for determining the adequacy of the foundation layer are: the total compliance rate is greater than or equal to 1 and the sub-unit coverage rate is equal to 1; where the total compliance rate is equal to the sum of the actual number of qualified test points of all sub-units divided by the quantity to be inspected, and the sub-unit coverage rate is equal to the number of sub-units with an actual number of qualified test points greater than or equal to 1 divided by the total number of sub-units on the construction side. Spatial fluctuation characteristic indicators include spatial variation coefficient and quality range ratio; wherein, spatial variation coefficient is defined as the standard deviation of the representative detection values ​​of the leading side of all sub-units divided by its mean; quality range ratio is defined as the difference between the maximum and minimum values ​​of the representative detection values ​​of the leading side of each sub-unit, divided by the difference between the standard acceptance limit and the minimum value; When the spatial coefficient of variation is greater than the coefficient of variation threshold and the quality range ratio is greater than the range ratio threshold, the spatial fluctuation is determined to be significant, and a trigger signal is sent to the expected density correction module. When any indicator does not exceed the corresponding threshold, the subsequent side uses the baseline expected density to perform the write-off process, and the expected density correction module and the supplementary inspection quantitative planning module are not activated.

6. The information platform for comprehensive business management of highway engineering testing according to claim 5, characterized in that, The standardized quality deviation is defined as the result of subtracting the mean of the representative detection values ​​of all sub-units from the representative detection values ​​of the preceding side of the corresponding sub-unit, and then dividing by the standard deviation of the representative detection values ​​of the preceding side of all sub-units. For blank sub-units with no detection data on the preceding side, a conservative deviation is assigned. This conservative deviation is the minimum value among all existing standardized quality deviations minus the absolute value of the standardized offset. The standardized offset is a correction coefficient for the conservative deviation of the blank sub-units on the preceding side, used to improve the quality risk assessment level of the blank sub-units. The calculation process of the correction coefficient is as follows: the original correction coefficient is obtained by subtracting the product of the adjusted sensitivity parameter and the standardized quality deviation from 1. Then, the original correction coefficients of all completed sub-units are normalized and scaled so that the sum of the correction coefficients of each sub-unit after normalization is equal to the number of completed sub-units, thereby satisfying the constraint that the sum of the expected densities of all sub-units after correction is equal to the total amount of the quantity to be inspected. When the normalized correction coefficient is lower than the lower limit of the correction coefficient, it is truncated to the lower limit value, and the remaining amount released by the truncation is distributed to the remaining sub-units proportionally. The adjusted sensitivity parameters are determined as follows: at the overlapping locations where there are qualified test data in the same project chainage segment on both the advance side and the follow side, the cross-side difference of each overlapping location is obtained by subtracting the representative test value of the advance side from the mean of the qualified test values ​​in the follow side sub-unit. The arithmetic mean of all the cross-side differences of the single overlapping locations is calculated to obtain the systematic deviation, and the standard deviation is calculated to obtain the cross-side deviation dispersion. The adjusted sensitivity parameter is obtained by multiplying the initial sensitivity parameter by 1 and adding the result of the ratio of the cross-side deviation dispersion to the statistical mean of the cross-side deviation dispersion of similar projects.

7. The information platform for comprehensive business management of highway engineering testing according to claim 6, characterized in that, The calculation method for the coverage adequacy of the subsequent side correction is as follows: for each completed sub-unit, take the smaller value between the actual number of qualified test points of the sub-unit and the expected density after correction, and divide the sum of the smaller values ​​of all completed sub-units by the sum of the expected densities after correction of all completed sub-units. When the coverage adequacy of the subsequent correction is not less than the threshold of the subsequent correction coverage adequacy, it is considered to be compliant; when it is less than the threshold of the subsequent correction coverage adequacy, for each sub-unit, the larger value of the expected density after correction minus the actual number of qualified test points and zero is taken as the gap amount of the sub-unit. Sub-units with gap amounts greater than zero constitute a list of inadequate sub-units.

8. The information platform for comprehensive business management of highway engineering testing according to claim 1, characterized in that, The acceptance completeness output module marks the acceptance completeness status as complete when all three conditions are met: the sufficiency of the foundation layer on the first side is met, the sufficiency of the correction on the subsequent side is met, and the quality meets the standards after the supplementary inspection of all insufficient sub-units. It also automatically collects the test reports by sub-project, sub-item project and inspection and evaluation unit using spatial coordinates and generates a quality inspection and evaluation table.

9. A method for information management of integrated business operations in highway engineering testing, used to implement the information platform for integrated business management of highway engineering testing as described in any one of claims 1-8, characterized in that, Includes the following steps: A three-dimensional spatial grid model is constructed based on the project design documents. The project is divided into several sub-units along the project chainage direction. Spatial location information is entered during the inspection commission registration, and spatial coordinates are associated with each inspection data. Based on the construction completion time of each construction side, the preceding and subsequent sides are identified. The inspection quantity is evenly distributed to the completed sub-units to obtain the benchmark expected density. The sufficiency of the foundation layer is judged for the preceding side. After the standard is met, the spatial fluctuation characteristic index is calculated to determine whether there is significant spatial fluctuation. The standardized quality deviation is calculated based on the representative detection values ​​of each sub-unit on the leading side. The sensitivity parameters are adjusted in combination with cross-side deviation analysis. The correction coefficient that satisfies the total amount conservation constraint is calculated. The correction coefficient is multiplied by the baseline expected density to obtain the corrected expected density of each sub-unit on the trailing side. The corrected coverage adequacy of the trailing side is calculated. If it does not meet the standard, the list of insufficient sub-units and the gap amount of each sub-unit are determined. For each insufficient sub-unit, the representative detection value of the preceding side of the sub-unit plus the systematic deviation is used as the cross-side prediction value, and the prediction uncertainty is determined based on the cross-side deviation dispersion and the project station distance from the sub-unit to the overlapping position. The required sample size for verification is calculated based on the difference between the cross-side predicted value and the standard acceptance limit, the prediction uncertainty, the standard deviation of the test operation fluctuation, and the ratio of the corrected expected density to the baseline expected density. The larger value between the rounded-up value of the gap and the required sample size for verification is taken as the final supplementary inspection quantity. Continuous insufficient sub-units are merged to generate supplementary inspection segments and construct the expected range of supplementary inspection results. After the supplementary inspection is completed, a quality compliance judgment and a cross-prediction consistency judgment are performed. If a significant deviation in cross-prediction occurs, the cross-prediction relationship parameters are updated and feedback is given. The completeness condition is that the foundation layer of the first side meets the sufficiency standard, the correction layer of the subsequent side meets the sufficiency standard, and the quality meets the standard after the supplementary inspection of all insufficient sub-units. When all conditions are met, the acceptance completeness status is marked as complete. The completeness is summarized level by level from the inspection and evaluation unit to the unit project. The test reports are automatically collected by sub-project, sub-item project and inspection and evaluation unit using spatial coordinates and a quality inspection and evaluation table is generated.

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