A road construction information visual analysis system

Abstract

The application relates to the technical field of data visualization, in particular to a road construction information visualization analysis system, which comprises a construction state layering module, a construction mode analysis module, a construction trend prediction module, an error dynamic correction module and an information storage management module.In the application, the real-time performance and interactivity of the construction state are improved through multi-dimensional dynamic analysis of road construction information, the information such as equipment operation, construction duration and state fluctuation is accurately identified and sorted by combining a layering state model and construction characteristic weight value analysis, the future construction trend can be dynamically predicted and corrected in time, the construction information storage mode is optimized, data management is more accurate and reliable, the deficiencies of static display and lack of real-time update in the traditional scheme are made up, the strain capacity and flexibility in the complex construction environment are enhanced, the continuous tracking and adjustment of the construction information are ensured, and the accuracy of management and the timeliness of decision-making are improved.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, and in particular to a road construction information visualization and analysis system. Background Technology

[0002] The field of data visualization technology primarily involves presenting data through graphics, images, animations, and other forms to facilitate users' intuitive and convenient understanding and analysis. Core aspects of this technology include graphic design, data representation methods, user interactivity, and the visual expression of data analysis. Data visualization technology is applied to information display in various data processing processes, simplifying data content through graphical methods and making complex data more readable and operable. It is widely used in fields such as business, healthcare, transportation, environment, and social sciences. Through effective visualization, it helps decision-makers and users discover patterns and trends within massive amounts of data, improving the efficiency and accuracy of data analysis. Traditional road construction information visualization and analysis systems refer to systems used in road construction processes to display and analyze construction-related information through data visualization. Traditional road construction information visualization and analysis uses static charts, simple maps, or two-dimensional views to present information such as construction progress, resource allocation, and on-site management. This traditional approach lacks dynamic interactivity and real-time data updates, and cannot effectively cope with complex and ever-changing construction environments and immediate decision-making needs.

[0003] In practice, existing technologies rely on static charts and simple diagrams to display construction information. Traditional methods lack dynamic updates and real-time interactivity, making it impossible to cope with changing needs in complex construction environments. Information such as construction progress, resource allocation, and on-site management cannot be reflected in the actual situation in a timely manner, leading to an inability to accurately grasp the construction status during decision-making. In particular, when unexpected changes occur during construction, strategies cannot be quickly adjusted to cope with new situations. This limitation results in low construction management efficiency, an inability to adjust construction plans and resource scheduling in real time, and consequently affects the progress and quality of the project, making it impossible to achieve precise control and effective decision-making during construction. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a road construction information visualization and analysis system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a road construction information visualization and analysis system comprising:

[0006] The construction status stratification module identifies the distribution characteristics of nodes in the construction area based on multi-dimensional status data during road construction, divides the construction status into layers, extracts and compares the frequency of status changes between nodes with the distribution density of construction cycles, determines the status attribution relationship, and obtains a stratified construction status model.

[0007] The construction mode analysis module calls the hierarchical construction state model to extract the construction equipment operation data, construction duration data and state fluctuation data under each category. It sorts the equipment operation data and state fluctuation data by amplitude, filters the top features, and obtains the construction characteristic weight value.

[0008] The construction trend prediction module arranges the construction flow direction in the nodes within the future time period according to the construction characteristic weight value, extracts the construction growth trend of adjacent time periods and judges the offset angle between trends, and maps the offset angle to the duration to obtain the reconstructed construction prediction trend table.

[0009] Based on the reconstructed construction prediction trend table, the error dynamic correction module extracts the gap range between the predicted value and the real-time value within the time node, analyzes the consistency ratio of the gap range direction and the gap magnitude ratio, and compares the two ratios to obtain a list of construction information error dynamic correction data.

[0010] As a further embodiment of the present invention, the hierarchical construction state model includes state density level, construction cycle distribution weight, and hierarchical label mapping relationship; the construction characteristic weight value includes equipment operation fluctuation weight, state response weight, and construction frequency distribution weight; the reconstructed construction prediction trend table includes construction growth direction sequence, trend offset angle interval, and time period change gradient; and the construction information error dynamic correction data list includes prediction error adjustment parameters, trend consistency evaluation value, and dynamic correction ratio.

[0011] As a further aspect of the present invention, the construction status layering module includes:

[0012] The construction cycle segmentation submodule is based on multi-dimensional state data during road construction, including equipment start-up and shutdown records, construction logs, and state time node sequences, to delineate abrupt change locations and state partitions in the construction cycle and obtain construction cycle segmentation data.

[0013] The status frequency extraction submodule divides the data according to the construction cycle, filters the construction logs in each partition, extracts the tag frequency and identifies the average occurrence frequency, and obtains the status tag frequency value.

[0014] The distribution density comparison submodule calls the state tag frequency value, analyzes the density of partition state nodes and state frequency, calculates the difference value between state and density matching, compares the attribution judgment value and makes a judgment, identifies the construction cycle attribution division quantity, judges the partition attribution relationship, and obtains the layered construction state model.

[0015] As a further aspect of the present invention, the construction mode analysis module includes:

[0016] The construction data extraction submodule calls the hierarchical construction status model to extract equipment operation data, construction duration and status fluctuation data for each category, performs field validation and converts them into continuous variables to obtain construction behavior feature quantities.

[0017] The sorting feature filtering submodule compares the amplitude values ​​of equipment operation data and status fluctuation data based on the construction behavior feature quantities, sorts them according to the amplitude values ​​within each category, retains the feature indicators before sorting, and obtains high-frequency change feature values.

[0018] The classification weight calculation submodule calls the construction duration data of the high-frequency changing feature values, extracts the cumulative value of the equipment operation and status fluctuation items before sorting, identifies the ratio of the cumulative value to the construction duration in each category, summarizes the feature ratio items, and obtains the construction characteristic weight value.

[0019] As a further aspect of the present invention, the construction trend prediction module includes:

[0020] The classification weight recognition submodule extracts the construction flow direction in the node based on the construction characteristic weight value, identifies the flow increase / decrease interval value and node category characteristics, analyzes the degree of matching between the node and the growth trend, and obtains the flow trend classification weight value.

[0021] The trend offset judgment submodule calls the traffic trend classification weight value, extracts the growth trend vector of adjacent time periods, identifies the ratio of the vector angle to the growth rate, and combines it with the duration of the time period to obtain the trend offset angle matching degree.

[0022] The prediction trend analysis submodule extracts the construction trend and direction angle of the matching degree interval based on the trend offset angle matching degree, and reorganizes and extends them in sequence to obtain the reconstructed construction prediction trend table.

[0023] As a further aspect of the present invention, the error dynamic correction module includes:

[0024] The error interval extraction submodule, based on the reconstructed construction prediction trend table, identifies the difference between the real-time data and the predicted data for the corresponding time period of the node, calculates the error identification value of the node in the time period, calibrates the difference of the node in the time of difference, divides the error interval and determines the upper and lower limits, records the start and end timestamps of the error interval, extracts the error direction, and obtains the error direction interval sequence.

[0025] The consistency ratio calculation submodule calculates the duration and direction switching density of the direction intervals based on the error direction interval sequence, calculates the proportion of the absolute value of the error within the error direction duration interval, judges the error trend fluctuation, and obtains the error trend deviation index.

[0026] The offset data prediction submodule calls the error trend offset index, finds the area of ​​error offset index change, merges the corrected section with the original trend data, updates the construction trend curve, and obtains a list of dynamic correction data for construction information errors.

[0027] As a further aspect of the present invention, the system also includes an information storage management module:

[0028] Based on the construction information error dynamic correction data list, the information storage management module extracts the interval equipment identification field, construction trajectory field and timestamp field, separates the fields according to the field type, and distributes the equipment identification field to multiple nodes after strong encryption. It records the encrypted index path of the encrypted data corresponding to the node to obtain the partitioned storage path of the construction analysis field.

[0029] The partitioned storage path for the construction analysis field includes the encrypted device index path, the construction field storage location, and the timestamp data partition mapping.

[0030] As a further aspect of the present invention, the information storage management module includes:

[0031] The field separation submodule identifies equipment records and parses field content based on the construction information error dynamic correction data list, performs classification processing according to field identifiers and annotation features, and generates field classification and splitting results.

[0032] The distributed encryption submodule calls the device identifier field in the field classification and splitting results, extracts the sequence structure and field bitmap, performs strength encryption according to the mapping relationship, and distributes the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains the encryption node distribution data;

[0033] The path indexing module calls the key path and node index in the encrypted node distribution data to classify and map the node paths, organize the index structure of the construction trajectory field and the timestamp field, and obtain the partitioned storage path of the construction analysis field.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] This invention effectively improves the real-time nature and interactivity of construction status by conducting multi-dimensional dynamic analysis of road construction information. By employing a hierarchical construction status model and analysis method based on construction characteristic weights, it enables more accurate identification and sorting of equipment operation data, construction duration, and status fluctuations during the construction process. This provides dynamic prediction and timely correction of future construction trends, improves the storage method of construction information, and makes data management more accurate and reliable. This significantly enhances the management efficiency and decision-making accuracy of the construction process. This approach solves the shortcomings of static display and real-time updates in traditional solutions, significantly improving the ability and flexibility to cope with complex construction environments. It ensures dynamic tracking and timely adjustment of construction information, thereby improving the accuracy of construction management and the timeliness of decision-making. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the construction status layered module in this invention;

[0038] Figure 3 This is a flowchart of the construction mode analysis module in this invention;

[0039] Figure 4 This is a flowchart of the construction trend prediction module in this invention;

[0040] Figure 5 This is a flowchart of the error dynamic correction module in this invention;

[0041] Figure 6 This is a flowchart of the information storage management module in this invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 A road construction information visualization and analysis system includes:

[0045] The construction status stratification module identifies the distribution characteristics of nodes in the construction area based on multi-dimensional status data during road construction, divides the construction status into layers, extracts and compares the frequency of status changes between nodes with the distribution density of construction cycles, determines the status attribution relationship, and obtains a stratified construction status model.

[0046] The construction mode analysis module calls the hierarchical construction state model to extract the construction equipment operation data, construction duration data and state fluctuation data under each category. The equipment operation data and state fluctuation data are sorted by amplitude, and the top features are selected to obtain the construction characteristic weight value.

[0047] The construction trend prediction module arranges the construction flow direction in nodes within a future time period according to the construction characteristic weight value, extracts the construction growth trend of adjacent time periods and judges the offset angle between trends, and maps the offset angle to the duration to obtain the reconstructed construction prediction trend table.

[0048] The error dynamic correction module is based on the reconstructed construction prediction trend table. It extracts the gap between the predicted value and the real-time value within the time node, analyzes the consistency ratio of the gap interval direction and the gap amplitude ratio, and judges the ratio of the two ratios to obtain the construction information error dynamic correction data list.

[0049] The information storage management module extracts the equipment identification field, construction trajectory field, and timestamp field from the dynamic correction data list of construction information errors. It separates the fields according to their types, encrypts the equipment identification field with strong encryption and distributes it to multiple nodes, records the encrypted index path of the encrypted data corresponding to each node, and obtains the partitioned storage path of the construction analysis field.

[0050] The hierarchical construction status model includes status density level, construction cycle distribution weight, and hierarchical label mapping relationship. The construction characteristic weight values ​​include equipment operation fluctuation weight, status response weight, and construction frequency distribution weight. The reconstructed construction prediction trend table includes construction growth direction sequence, trend offset angle interval, and time period change gradient. The construction information error dynamic correction data list includes prediction error adjustment parameters, trend consistency evaluation value, and dynamic correction ratio. The partitioned storage path of construction analysis fields includes encrypted equipment index path, construction field storage location, and timestamp data partition mapping.

[0051] Please see Figure 2 The construction status layered module includes:

[0052] The construction cycle segmentation submodule is based on multi-dimensional state data during road construction, including equipment start-up and shutdown records, construction logs, and state time node sequences, to delineate abrupt change locations and state partitions in the construction cycle and obtain construction cycle segmentation data.

[0053] Based on multi-dimensional state data during road construction, including equipment start-up and shutdown records, construction logs, and state time node sequences, the raw data is first extracted. For example, for a typical asphalt road paving project, the equipment start-up and shutdown records include the record of paver P001 starting at 08:00:00 on July 1st and stopping at 12:00:00, and the record of roller R002 starting at 08:05:00 and stopping at 11:30:00. The construction log records events such as "Asphalt paving begins at 08:10:00" or "Lunch break pause at 12:30:00". The state time node sequence, combined with the records, forms a list of events arranged in chronological order. For example: P001 starts... The process involves starting equipment (R002), commencing asphalt paving, stopping equipment (P001), and stopping equipment (R002). Subsequently, abrupt changes and status zones are defined within the construction cycle. To identify these abrupt changes, a sliding window analysis method based on event density is used. A sliding window of, for example, 30 minutes is set. Within this window, the total number of equipment status change events (such as start / stop) and key construction log entries (such as the start / end of a phased operation) is counted. If this total exceeds a preset "abrupt change threshold," the center moment of the window is marked as the abrupt change location. This "abrupt change threshold" is set based on data analysis of historical road construction projects. By analyzing data from one hundred similar projects in the past, it was found that abrupt changes within 30 minutes... Five or more equipment status changes or two or more critical log events indicate a significant transition in the construction phase. The threshold is set at 5 for equipment status changes and 2 for critical log events. If six equipment status changes and one critical log event occur within a 30-minute window, the weighted count is 6 + 1 × 2 = 8, exceeding the threshold of 5. Therefore, the center point of that window is marked as a sudden change location. For example, within a window from 08:00:00 to 08:30:00, if six events are recorded (paver start, roller start, paver pause, roller pause, material truck arrival, personnel rest), and there is a critical log entry for "asphalt paving begins," then the total number of events within that window is [not specified]. If (after weighting) the threshold for mutation is exceeded, then 08:15:00 (the point in the window) is identified as a mutation location. After identifying all mutation locations, the entire construction cycle is precisely divided into multiple "state partitions". Each partition represents a relatively stable construction stage. For example, if 08:15:00 and 14:30:00 are identified as mutation locations, the entire day's construction can be divided into 00:00:00-08:15:00 (such as the preparation stage), 08:15:00-14:30:00 (such as the core paving stage), and 14:30:00-23:59:59 (such as the finishing and curing stage). This process accurately obtains the construction cycle division data.

[0054] The status frequency extraction submodule divides the data according to the construction cycle, filters the construction logs in each partition, extracts the tag frequency and identifies the average occurrence frequency, and obtains the status tag frequency value.

[0055] Based on the construction cycle data, the system performs detailed processing on each defined "status partition." For example, for the identified "core paving phase," it precisely filters all construction log entries occurring within that time period. This filtering is done by comparing the timestamps of the log entries with the start and end timestamps of the partition. For instance, it selects only logs with timestamps greater than or equal to 08:15:00 and less than 14:30:00. Within the filtered logs, the system automatically identifies and extracts the frequency of predefined "status tags." Status tags are keywords describing construction activities, such as "paving operation," "compaction operation," "material transportation," "equipment failure," and "personnel rest." The system counts these tags. In the construction log of the "core paving phase", if the "paving operation" label appears 4 times, the "compaction operation" label appears 2 times, the "material transportation" label appears 2 times, the "temporary equipment shutdown" label appears 1 time, and the "personnel rest" label appears 1 time, then the average frequency of each label is identified. This is calculated by dividing the frequency of each label by the total duration of the "state zone". For example, the average frequency of "paving operation" is 4 times / 375 minutes ≈ 0.0107 times / minute, and the average frequency of "compaction operation" is 2 times / 375 minutes ≈ 0.0053 times / minute. This calculation process is repeated for all identified state labels to finally obtain the state label frequency value.

[0056] The distribution density comparison submodule calls the status label frequency value, analyzes the density of partition status nodes and status frequency, calculates the difference value between status and density matching, compares the attribution judgment value and makes a judgment, identifies the construction cycle attribution division amount, judges the partition attribution relationship, and obtains the layered construction status model.

[0057] The status tag frequency value is used to analyze the "status node density" and corresponding "status frequency" of each partition. The "node density" is obtained by calculating the sum of equipment start / stop events and key construction log events within the partition and then dividing it by the duration of the partition. For example, if a "core paving phase" lasts for 375 minutes and has 10 equipment start / stop events and 3 key log events, then the node density of that partition is (10+3) / 375≈0.0347 events / minute. This density is compared with the average status frequency of different construction phases for joint analysis to calculate the "status and density matching difference value," which quantifies the consistency between the observed status frequency and node density. The matching difference value is calculated by comparing the standardized state frequency vector of each partition with the node density vector. Specifically, the state frequency vector of each partition is compared with the frequency vector of the reference phase, and combined with the node density, the matching difference value is finally calculated. This difference value reflects the degree of fit between the state label frequency and the node density. Based on historical data analysis and expert experience, the matching difference value is compared with a preset "attribution judgment value" to determine which construction stage each partition belongs to. For example, if the matching difference value is below a certain threshold (such as 0.12), it is determined to be in the "subgrade construction" stage; if it is between 0.12 and 0.25, it is in the "asphalt paving" stage; and if it exceeds 0.25, it is in the "abnormal or mixed" stage. Through this process, the construction stages can be accurately identified and divided, ultimately forming a layered construction state model.

[0058] Please see Figure 3 The construction mode analysis module includes:

[0059] The construction data extraction submodule calls the hierarchical construction status model to extract equipment operation data, construction duration and status fluctuation data for each category, performs field validation and converts them into continuous variables to obtain construction behavior feature quantities.

[0060] A layered construction status model is invoked, which defines in detail each stage of the construction project. For example, the entire "asphalt paving" stage from July 1st to July 7th is identified. Based on this model, specific construction data for each category is accurately extracted. This includes "equipment operation data" (such as real-time sensor data on paver and roller engine speed, fuel consumption rate, travel speed, and vibration frequency), "construction duration" (i.e., the precise duration of each category stage, for example, the "asphalt paving" stage lasted 6 days and 150 hours), and "state fluctuation data" (such as fluctuations in quality control parameters like real-time temperature, paving thickness, and compaction degree of the asphalt mixture during paving). For example, for the "asphalt paving" stage, the rotational speed (RPM) records of all equipment P001 are retrieved from the data bus, along with infrared sensors installed on the paving slab. The asphalt temperature data recorded by the sensor is then validated and converted into continuous variables. This validation process involves checking the integrity of the extracted data, such as ensuring that all data points with timestamps exist, and identifying, correcting, or marking outliers. For example, if the asphalt temperature sensor records a non-physical temperature of 500°C at a certain moment, it is marked as invalid or corrected to a reasonable boundary value (e.g., 180°C). After data cleaning, all discrete or categorical data are converted into continuous variables. For example, if the equipment operating status was originally a discrete state of "running," "idle," or "stopped," it is converted into a continuous variable of equipment power consumption percentage. Alternatively, graded compaction data (e.g., levels 1-5) can be converted into specific compaction percentage values. This ensures that all characteristic quantities used for subsequent analysis are quantifiable and continuous. This conversion yields construction behavior characteristic quantities.

[0061] The sorting feature filtering submodule compares the amplitude values ​​of equipment operation data and status fluctuation data based on construction behavior characteristics, sorts them according to the amplitude values ​​within each category, and retains the feature indicators before sorting, using the following formula:

[0062] ;

[0063] Obtain high-frequency variation characteristic values;

[0064] in, Represents high-frequency changing characteristic values. Representing the The amplitude value of the device operation data at each data point. This represents the average value of the equipment's operating data. The standard deviation of equipment operating data Representing the The amplitude value of the state fluctuation data of each data point The mean of the state fluctuation data. The standard deviation represents the state fluctuation data. This represents the total number of data points;

[0065] Based on the characteristic quantities of construction behavior, where "amplitude value" specifically refers to the instantaneous measurement value of equipment operating parameters or state fluctuation parameters at a certain moment, such as the paver's travel speed (meters / minute) at a certain second or the real-time temperature (degrees Celsius) of the asphalt mixture at a certain location, the instantaneous amplitude values ​​are compared horizontally to identify their relative size in the overall data distribution. Subsequently, the amplitude values ​​within each category are sorted. This sorting is not a simple ascending or descending order, but a weighted sort based on their frequency and importance within a specific construction stage. For example, in the "asphalt paving" stage, if the paver's speed range of 4.0-4.5 meters / minute accounts for 80% of the total running time, and this speed range is closely related to the optimal paving efficiency, then this speed range is determined to be a high-importance feature and retained at the top of the ranking. The retained characteristic indicators are key parameters representing the core operating mode and state characteristics of this construction stage, along with their typical value ranges. Then, a formula is used... Calculate the high-frequency variation characteristic values, where, Representing high-frequency variation characteristic values, it quantifies the product effect of the instantaneous deviation of equipment operation and the overall volatility of the construction status. Representing the The amplitude value of the equipment operation data at each data point, for example, the instantaneous speed (m / min) of the paver at a certain moment. This represents the average value of equipment operating data, such as the average speed of the paver throughout the entire "asphalt paving" phase. The standard deviation of equipment operating data measures the dispersion of the data relative to the mean, reflecting the stability of equipment operation. Representing the The amplitude value of the state fluctuation data of each data point, for example, the instantaneous temperature (°C) of the asphalt mixture at a certain moment. This represents the mean of the state fluctuation data, such as the average temperature of the asphalt throughout the entire stage. The standard deviation represents the dispersion of state fluctuation data relative to the mean, reflecting the stability of construction quality or environmental parameters. This represents the total number of data points, i.e., the total number of state fluctuation data points collected during the current construction phase;

[0066] The calculation logic of this formula is as follows: First, Partial calculation of individual device operating data points Relative to its average value The standardized absolute deviation, i.e., the degree to which a point deviates from the average level, is denoted by the standard deviation. Normalization is performed to make equipment data with different dimensions comparable. Secondly... Partial calculation of individual state fluctuation data points Relative to its average value The standardized absolute deviation, and the standard deviation Normalization, again, The squares of all standardized state fluctuation deviations are summed, ensuring all deviations contribute positively and highlighting larger fluctuations. Finally, the summation is returned to its original dimension by taking the square root, and then multiplied by the standardized deviation of equipment operation to obtain a comprehensive high-frequency variation characteristic value. ;

[0067] The advantage of this formula lies in its combination of instantaneous changes in equipment operation with the overall volatility of the construction process. Through a product, it highlights critical moments where significant instantaneous deviations in equipment operation occur simultaneously with substantial fluctuations in the construction process. This provides a more comprehensive reflection of the dynamic changes and potential problems during construction, rather than focusing on a single dimension. For example, for a one-hour asphalt paving operation, the total number of data points... The specific data is shown in Table 1;

[0068] First, calculate the average speed of the paver. meters per minute;

[0069] Its standard deviation meters per minute;

[0070] Next, the average temperature of the asphalt is calculated. Celsius;

[0071] Its standard deviation Celsius;

[0072] Then, calculate the sum of squares of the state fluctuation terms. ,by For example, Sum all 10 data points to get Finally, using the first data point ( , ) for example, calculate , calculate , , , as well as Substitute the value into the formula: ;

[0073] This calculation process is performed for each data point, ultimately yielding high-frequency variation characteristic values;

[0074] Table 1: Example of operating and status fluctuation data for asphalt paving equipment:

[0075]

[0076] As shown in Table 1, the data will be used to calculate high-frequency variation characteristic values.

[0077] The classification weight calculation submodule calls the construction duration data of high-frequency changing feature values, extracts the cumulative value of equipment operation and status fluctuation items before sorting, identifies the ratio of the cumulative value to the construction duration in each category, summarizes the feature ratio items, and obtains the construction characteristic weight value.

[0078] The system calls upon construction duration data with frequently changing feature values. The "pre-sorting feature indicators" are the key operating modes and state characteristics identified in the previous module, such as "paver speed in the 4.0-4.5 m / min range" and "asphalt temperature in the 150-160℃ range." It precisely calculates the cumulative duration of these features throughout the entire "asphalt paving" phase (e.g., a total construction time of 375 minutes). For example, if the paver operated for a cumulative 300 minutes in the 4.0-4.5 m / min speed range and the asphalt temperature remained in the 150-160℃ range for a cumulative 320 minutes, the system then identifies the ratio of the cumulative value to the construction duration within each category. This ratio quantifies each feature. The relative importance or prevalence throughout the construction phase is considered. For example, the proportion of the paver speed in the 4.0-4.5 m / min range is 300 minutes / 375 minutes = 0.80, and the proportion of the asphalt temperature in the 150-160℃ range is 320 minutes / 375 minutes ≈ 0.853. The characteristic proportions are summarized. This summarization process can use a weighted average or aggregation method. For example, if the weights of paver speed and asphalt temperature are equal, the comprehensive weight value is (0.80+0.853) / 2 = 0.8265. This value reflects the overall compliance or stability of the construction phase in terms of key operating parameters and quality control, and finally, the construction characteristic weight value is obtained.

[0079] Please see Figure 4 The construction trend prediction module includes:

[0080] The classification weight recognition submodule extracts the construction flow direction in the nodes based on the construction characteristic weight value, identifies the flow increase / decrease interval value and node category characteristics, analyzes the degree of matching between the nodes and the growth trend, and obtains the flow trend classification weight value.

[0081] Based on the construction characteristic weight values, which reflect the typical operation mode and state characteristics of each construction stage, the construction flow direction is first extracted from the nodes. Here, "node" refers to key milestones or time points on the project timeline, such as the completion of a certain length of road section or the daily completion point. The "construction flow direction" quantifies the project's progress between nodes, such as the daily paving length (meters / day) or the total amount of materials consumed (tons / day). Then, the flow increase / decrease interval values ​​and node category features are identified. The flow increase / decrease interval values ​​are determined by comparing the changes in construction flow direction between adjacent nodes. For example, if 150 meters were paved one day and 180 meters were paved the next day, the flow increase is 30 meters. Node category features classify each node, such as "daily completion node," "phased acceptance node," and "equipment failure node." These features provide additional contextual information about the node's nature. Next, the degree of matching between the node and the growth trend is analyzed, which involves... The process compares the current "flow increase / decrease range" with a predefined "growth trend matching threshold." For example, it matches the daily paving length increment with a "normal growth trend" (defined as a daily increment range of 150-200 meters). This "growth trend matching threshold" is set based on historical project data and industry standards. For instance, by analyzing the daily progress data of the past 100 projects, it was found that when the daily paving length increment is within ±10% of the planned value, the project progress is considered normal. Therefore, if the planned daily increment is 175 meters, the matching threshold range is set to [157.5, 192.5] meters. For example, if the paving length increment of a certain "daily completion node" is 165 meters, its matching degree with the "normal growth trend" is relatively high. However, if the increment is 0 meters and the node category is "equipment failure node," its matching degree with any positive growth trend is extremely low. This analysis process accurately obtains the flow trend classification weight value.

[0082] The trend offset judgment submodule calls the traffic trend classification weight value, extracts the growth trend vector of adjacent time periods, identifies the ratio of the vector angle to the growth rate, and combines it with the duration of the time period to obtain the trend offset angle matching degree.

[0083] When calling the traffic trend classification weight value, the daily construction progress is represented by a vector containing the amount of work completed and the amount of resources consumed. For example, the first day involves completing 150 meters and consuming 500 liters of fuel, and the second day involves completing 180 meters and consuming 550 liters of fuel. Then, the ratio of the vector angle to the growth rate is compared: the vector angle reflects the consistency of the construction direction; a smaller angle indicates a more stable trend, while a larger angle indicates a significant change in direction. The growth rate ratio measures the degree to which progress is accelerating or slowing down; for example, going from 150 meters to 180 meters implies approximately a 20% acceleration. Furthermore, the duration of the time period is used for weighting to highlight the importance of long-term trends. For example, a slight acceleration trend lasting 5 days is more meaningful than a significant acceleration trend lasting only 1 day. In this way, when calculating the trend offset angle matching degree, the construction direction, growth rate, and duration are comprehensively considered, thus accurately reflecting the degree of matching between the construction progress and the predetermined growth trend, resulting in the trend offset angle matching degree.

[0084] The prediction trend analysis submodule extracts the construction trend and direction angle within the matching degree range based on the trend offset angle matching degree, and reorganizes and extends them in sequence to obtain a reconstructed construction prediction trend table.

[0085] Based on the trend offset angle matching degree, this matching metric quantifies the deviation between the current construction trend and the expected trend. First, the construction trend and direction angle within the matching degree interval are extracted. Specifically, the calculated "trend offset angle matching degree" value is mapped to a predefined "matching degree interval." Each interval is associated with a specific "construction trend" (such as "stable growth," "slight fluctuation," or "significant deviation") and a "direction angle." This "direction angle" represents the angle by which the actual trend deviates from the ideal path. For example, if the matching degree is between 0.0 and 0.1, it is identified as a "stable growth" trend with a direction angle of 0 degrees; if the matching degree is between 0.1 and 0.3, it is identified as a "slight fluctuation" trend with a direction angle of 5 degrees; if the matching degree is greater than 0.5, it is identified as a "significant deviation" trend with a direction angle exceeding 20 degrees. The intervals and corresponding trends and angles are set based on statistical analysis of the impact of different degrees of deviation on the final project from historical construction data. Then, according to... Sequential reorganization and extension refers to using identified "construction trends" and "direction angles" to sequentially adjust and extrapolate the future construction progress in the "reconstruction prediction trend table." For example, if the matching degree of the current and recent time periods indicates "slight fluctuations" and the direction angle is stable at 5 degrees, then when predicting future construction trends, a perfectly linear ideal growth will no longer be assumed. Instead, this slight deviation of 5 degrees will be incorporated into the future prediction curve. Through sequential analysis of historical trends and direction angles, patterns can be identified, and future trends can be extrapolated more accurately. For example, if the daily completed length growth trend is observed to be 150 meters, 160 meters, and 155 meters for three consecutive days, and the corresponding "trend deviation angle matching degree" is within the "slight fluctuation" range, then it will be predicted that the next day will also remain within this fluctuation range, and the expected completed length will be adjusted accordingly. This adjustment ensures that the prediction results are more consistent with the actual operating conditions, ultimately resulting in the reconstructed construction prediction trend table.

[0086] Please see Figure 5 The error dynamic correction module includes:

[0087] The error interval extraction submodule, based on the reconstructed construction prediction trend table, identifies the difference between real-time data and predicted data for the corresponding time period of a node, using the following formula:

[0088] ;

[0089] The error identification value of the node in the time period is calculated, the node is calibrated at the time difference of the difference, the error interval is divided and the upper and lower limits are determined, the start and end timestamps of the error interval are recorded, the error direction is extracted, and the error direction interval sequence is obtained.

[0090] in, This represents the error identification value of the node within a time period. Representative node Real-time data, Representative node The predicted data, This represents the average real-time data of all nodes within a given time period. Represents the duration of a time period. A constant factor, The maximum duration of all time periods. Indicates the total number of nodes;

[0091] Based on the reconstructed construction forecast trend table, which provides updated construction progress forecast data, the differences between real-time data and forecast data for the corresponding time periods of each node are first identified. Here, "node" refers to a specific time point in the forecast trend table, such as the amount of construction completed per hour or every four hours. The actual "real-time data" recorded at each node is then precisely compared. ) and "predictive data" For example, in a 24-hour construction day, if it is predicted that 12 meters will be completed in a certain hour, but the real-time data shows only 10 meters, there is a discrepancy. The formula is then used... The error identification value of the calculation node over the time period is as follows: The error identification value of the representative node over a time period comprehensively quantifies the standardized magnitude of the prediction bias and its temporal persistence. Representative node Real-time data, such as nodes The actual length of construction completed at any given time (unit: meters). Representative node Predictive data, such as nodes Predict the required construction length (in meters) at all times. This represents the average real-time data of all nodes within the current time period, such as the average length completed in each hour within a 24-hour period. It provides a benchmark for subsequent standardized error calculations. This represents the duration of the current time period, for example, the current error calculation period is 24 hours. This is a constant factor with a value of 0.5. This value was determined based on sensitivity analysis of multiple historical construction cases, aiming to ensure that long-term systematic errors are adequately weighted while avoiding overreaction to transient fluctuations. The maximum value of the duration of all considered time periods, for example, the longest continuous prediction period found in historical data is 72 hours, is used to normalize the duration of the current time period. This represents the total number of nodes used for calculation within the current time period. For example, if data is collected hourly within a 24-hour period, then... ;

[0092] The operational logic of this formula is as follows: the first part of the formula Calculated the nodes The standardized value of the absolute difference between real-time data and predicted data, where the denominator is... This represents the overall volatility of real-time data within the current time period, making the error values ​​comparable across construction projects of different scales. The second part of the formula... It is a time-weighted factor, which varies with the duration of the error. It increases with the growth, when The closer The greater the weight given to error correction, the more this design ensures that persistent systematic errors receive more significant attention and correction than sporadic errors.

[0093] The advantage of this formula lies in the fact that it not only quantifies the magnitude of instantaneous prediction errors, but also, by introducing a time persistence factor, enables the model to distinguish and prioritize the correction of errors that have accumulated over a long period of time and exhibit systematic deviation characteristics. This significantly improves the long-term stability and correction effect of the prediction model. For example, assuming that there are data for five key nodes during a 24-hour construction period as shown in Table 2, the average value of the real-time data is first calculated. rice;

[0094] Next, calculate the denominator. ;

[0095] Then calculate for each node. , with nodes For example, its real-time data Predicted data Duration of time period Hours, maximum duration Hours, constant factor ;

[0096] Substituting into the formula, we get: ;

[0097] Similarly, node 2 Node 3 Node 4 Node 5 Then, the node time difference at the differentiated time is calibrated, and the error interval is divided to determine the upper and lower limits. Based on the calculated... The value divides the error into different intervals, for example, by... Determined to be in the "low error range", Determined as "mean error range", The threshold for classifying a data point as "high error range" is set based on statistical analysis of historical project error tolerance. For example, by analyzing past project data, it was found that when... When the value is below 0.06, the final error rate of the project is controlled within 2%, while when... When the value is higher than 0.11, the project error rate may exceed 5%, for example, in the calculation results above. It falls within the "low error range". It falls within the "medium error range". If the data falls within a "high error range," record the start and end timestamps of that range. For example, recording the "high error range" as the time from the current node to the start of the next stable range. Simultaneously, extract the error direction, i.e., whether the real-time data is higher or lower than the predicted data. For example, if... This is a positive error. This is a negative error, and this process accurately obtains the error direction interval sequence;

[0098] Table 2: Example of comparison between construction forecast and real-time data:

[0099]

[0100] As shown in Table 2, this is the real-time node data and prediction data used for error calculation.

[0101] The consistency ratio calculation submodule calculates the percentage of absolute error within the error direction interval based on the error direction interval sequence, statistically analyzes the duration and direction switching density of the interval, judges the error trend fluctuation, and obtains the error trend deviation index.

[0102] Based on the error direction interval sequence, which contains information on the magnitude, direction, and duration of the error, the duration and direction switching density of each direction interval are first calculated. For example, in a 24-hour analysis period, if a "negative error" lasts for 4 hours, followed by a "positive error" lasting for 6 hours, and then a "negative error" lasting for 3 hours, the duration of each direction interval is recorded, and the direction switching density is calculated. For example, if there are 2 direction switches within 24 hours, the density is 2 / 24 hours ≈ 0.083 times / hour. Next, the percentage of the absolute value of the error within each direction interval is calculated. This is done by dividing the sum of the absolute values ​​of all errors within each interval by the sum of the absolute values ​​of all errors throughout the entire analysis period. For example, if the sum of the absolute values ​​of the error within the 4-hour "negative error" interval is 5 meters, and the total absolute value of the error over 24 hours is 20 meters, then the percentage of this interval is 5 meters / 20 meters = 0.25. Then, the error is determined... The judgment of poor trend volatility is based on "direction switching density" and "percentage of absolute error value". For example, if the "direction switching density" exceeds the preset "high volatility threshold" (e.g., 0.1 times / hour) and the percentage of error in any single direction does not reach the "dominance threshold" (e.g., 0.6), then the error trend is judged to be "high volatility". This "high volatility threshold" and "dominance threshold" are empirically set based on the impact of error patterns on prediction stability in historical project data. For example, by analyzing a large amount of historical error data, it was found that when the direction switching density exceeds 0.1 times / hour, the prediction accuracy will decrease significantly, indicating that the error pattern tends to fluctuate more randomly. If the "direction switching density" of a certain period is 0.125 times / hour (exceeding the threshold of 0.1) and the percentage of the largest absolute error value is 0.385 (not reaching the dominance threshold of 0.6), then the error trend of that period is judged to have "high volatility", and the error trend deviation index is finally obtained.

[0103] The offset data prediction submodule calls the error trend offset index, finds the area of ​​error offset index change, merges the corrected section with the original trend data, updates the construction trend curve, and obtains a list of dynamic correction data for construction information errors.

[0104] The Error Trend Deviation Index is invoked. This index quantitatively describes the stability and systematic nature of prediction errors. First, the region of change in the Error Trend Deviation Index is identified. This involves continuously monitoring the dynamic changes of the Error Trend Deviation Index and identifying specific time periods where its value undergoes significant shifts. For example, if the index suddenly jumps from a long-term stable low value (e.g., 0.05) to a high value (e.g., 0.15), exceeding a preset "change region threshold," then this region is marked as a "change region." This "change region threshold" is set based on the analysis of the index's fluctuation characteristics in actual projects. For example, when the rolling average of the Error Trend Deviation Index changes by more than 10% over the past 24 hours, it is considered to have entered a "change region." The 10% change range is determined based on the smallest significant change in historical data that indicates the need for adjustment in the future prediction model. Then, the corrected segment is merged with the original trend data to update the construction trend curve. Within the identified "change area," or based on the latest "error trend offset index," a correction factor is generated and applied to the "original trend data" (from the "reconstructed construction prediction trend table") to correct future predictions. For example, if the "error trend offset index" shows a persistent 5% overestimation in the prediction, all subsequent predicted construction volumes will be multiplied by 0.95 and adjusted downwards. This correction factor is directly related to the "error trend offset index." This process accurately yields a dynamic correction data list for construction information errors.

[0105] The "Dynamic Correction Data List for Construction Information Errors" is used to monitor and correct information errors that occur during construction. By dynamically collecting and analyzing data errors during construction, it helps identify potential problem areas and generate corrective solutions. This list provides specific data on various types of construction errors, such as positional deviations and time errors, and allows for timely adjustments to construction strategies based on this data. This ensures improved construction accuracy and quality. Through continuous tracking and correction of errors, it effectively reduces potential deviations during construction, thereby optimizing the overall construction process and results.

[0106] Please see Figure 6 The information storage management module includes:

[0107] The field separation submodule dynamically corrects the data list based on construction information errors, identifies equipment records and parses field content, classifies them according to field identifiers and annotation features, and generates field classification and splitting results.

[0108] Based on a dynamic error correction data list for construction information, this list contains dynamically corrected construction forecasts and real-time data. First, equipment records are identified and their field contents are parsed. This involves identifying data entries generated by specific equipment from the raw data stream; for example, identifying all data records belonging to paver P001 or roller R002. Then, the records are parsed, breaking down semi-structured or unstructured raw data (such as "P001, 07-15 09:30:00, Speed=4.2m / min, Temp=155C") into independent, identifiable fields, such as "Equipment ID," "Timestamp," "Speed," and "Temperature." The classification process is based on field identifiers and annotation features. Here, "field identifier" is a predefined field name, such as "Speed," and "annotation features" are metadata related to the field, such as data type (numeric, string), unit of measurement (meters / minute, degrees Celsius), and data sensitivity (high, medium, low). Fields are classified according to their identifiers and features. For example, "Device ID" is classified as "identifier data," "timestamp" is classified as "time data," and "speed" and "temperature" are classified as "operational parameter data." Furthermore, based on their impact on project progress, they are marked as "criticality: high" or "criticality: medium." This process accurately generates field classification and splitting results.

[0109] The distributed encryption submodule calls the device identifier field in the field classification and splitting results, extracts the sequence structure and field bitmap, performs strength encryption based on the mapping relationship, and distributes the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains the encryption node distribution data;

[0110] By analyzing the device identification field (e.g., "P001"), its structure is extracted, identifying "P" as the device type and "001" as the serial number. A field bitmap is generated to mark sensitive and non-sensitive information. Sensitive parts (e.g., "001") are encrypted, while non-sensitive parts (e.g., "P") are left unencrypted. Encryption uses a predefined mapping relationship, associating field types with encryption algorithms (e.g., AES-256) and key management rules. Specifically, for the sensitive part of "P001," the AES-256 algorithm and a preset key (e.g., K_P001) are used for encryption to generate ciphertext. The encrypted data is distributed across multiple nodes according to a distributed storage strategy. For example, the first half of the ciphertext is stored on node A, and the second half on node B. The decryption key path and node number for each data segment are recorded to ensure the accuracy of subsequent secure reconstruction and decryption processes. Through this process, efficient and secure device identification encryption and storage are achieved, and encrypted node distribution data is obtained.

[0111] The path indexing module calls the key path and node index in the encrypted node distribution data, classifies and maps the node paths, organizes the index structure of the construction trajectory field and timestamp field, and obtains the partitioned storage path of the construction analysis field.

[0112] The process involves retrieving key paths and node indexes from the encrypted node distribution data. This data precisely describes the storage location of encrypted data segments and the access path for their decryption keys. First, the node paths are categorized and mapped. Here, a "node path" is an address pointing to the physical or logical storage location where encrypted data or keys are stored. Paths are categorized according to predefined rules (such as data sensitivity, device type, geographical location, etc.). For example, all paths storing "core confidential device ID" data are categorized as "high-security area paths," and these paths are mapped to specific security access policies and storage partitions. Subsequently, the index structure of the construction trajectory field and timestamp field is organized. This operation is for unencrypted or independently encrypted "construction trajectory fields" (e.g., ...). The device's GPS coordinates (longitude, latitude, altitude) and "timestamp field" are used to build an efficient index structure. For example, an R-tree spatial index is built for geographic coordinate data to quickly retrieve all construction activities within a specific geographic area, and a B-tree time index is built for timestamp data to efficiently query construction records within a specific time period. For instance, to quickly query the operation trajectory of all pavers on a certain road section during July, the system will build a composite index that combines timestamps with geospatial information. This index structure will ensure that even if sensitive information is encrypted and stored in a distributed manner, non-sensitive but crucial trajectory and time information can still be efficiently retrieved and utilized, ultimately resulting in the partitioned storage path for the construction analysis field.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A road construction information visual analysis system, characterized by, The system comprises: The construction state stratification module identifies node distribution characteristics of a construction area, divides construction state levels, extracts state change frequency and construction cycle distribution density, and compares them to determine state attribution relationship, and obtains a stratified construction state model based on multi-dimensional state data in a road construction process; The construction mode analysis module calls the stratified construction state model, extracts construction equipment operation data, construction duration data, and state fluctuation data under each classification, sorts the equipment operation data and state fluctuation data by amplitude, filters the front features of the sorted order, and obtains construction characteristic weight values; The construction trend prediction module arranges construction flow directions in nodes in future time periods according to the construction characteristic weight values, extracts construction growth trends of adjacent time periods, and judges the trend offset angle, and obtains a reconstructed construction prediction trend table by corresponding the offset angle and the time length. The error dynamic correction module extracts a prediction value and a real-time value difference interval in a time node based on the reconstructed construction prediction trend table, analyzes the consistency proportion and the difference amplitude proportion of the difference interval, judges the two proportions by ratio, and obtains a construction information error dynamic correction data list. The error dynamic correction module comprises: The error interval extraction submodule identifies the difference between real-time data and prediction data of a node corresponding to a time period based on the reconstructed construction prediction trend table, calculates error identification values of the node between time periods, calibrates the node at the differentiating time difference, divides the error interval and determines the upper and lower limit values, records the start and end time stamps of the error interval, extracts the error direction, and obtains an error direction interval sequence; The consistency proportion calculation submodule calculates the proportion of error absolute values in the error direction continuous interval according to the error direction interval sequence, judges the error trend fluctuation, and obtains an error trend offset index; The offset data prediction submodule calls the error trend offset index, finds the error offset index change area, fuses the corrected section and the original trend data, updates the construction trend curve, and obtains the construction information error dynamic correction data list; The information storage management module extracts interval equipment identification fields, construction trajectory fields, and time stamp fields based on the construction information error dynamic correction data list, separates the fields according to field types, disperses the equipment identification field strength to multiple nodes after encryption, records the encryption index path of the corresponding encrypted data of the nodes, and obtains a partition storage path of the construction analysis field; The partition storage path of the construction analysis field comprises an encrypted equipment index path, a construction field storage location, and a time stamp data partition mapping.

2. The road work information visualizing analysis system according to claim 1, characterized by, The stratified construction state model comprises state density levels, construction cycle distribution weights, and level label mapping relationships, the construction characteristic weight values comprise equipment operation fluctuation weights, state response weights, and construction frequency distribution weights, the reconstructed construction prediction trend table comprises construction growth direction sequences, trend offset angle intervals, and time period change gradients, and the construction information error dynamic correction data list comprises prediction error adjustment parameters, trend consistency evaluation values, and dynamic correction ratios.

3. The road work information visual analysis system of claim 1, wherein, The construction state stratification module comprises: The construction period division sub-module divides the construction period based on multi-dimensional state data in the road construction process, including equipment start-stop records, construction logs, state time node sequences, divides mutation positions and state partitions in the construction period, and obtains construction period division data; The state frequency extraction sub-module filters the construction logs in each partition according to the construction period division data, extracts label frequencies and identifies average occurrence frequencies, and obtains state label frequency values; The distribution density comparison sub-module calls the state label frequency values, analyzes the state node density and state frequency of the partition, calculates a state and density matching difference value, compares and judges an attribution judgment value, identifies a construction period attribution division quantity, judges a partition attribution relationship, and obtains a layered construction state model.

4. The road work information visual analysis system of claim 3, wherein, The construction mode analysis module includes: The construction data extraction sub-module calls the layered construction state model, extracts equipment operation data, construction duration and state fluctuation data of each category, performs field verification and converts into continuous variables, and obtains construction behavior characteristic quantities; The sorting feature screening sub-module compares the amplitude values of the equipment operation data and the state fluctuation data according to the construction behavior characteristic quantities, sorts the amplitude values in each category, retains the feature indexes before sorting, and obtains high-frequency variation characteristic values; The classification weight calculation sub-module calls the construction duration data of the high-frequency variation characteristic values, extracts the cumulative values of the equipment operation and state fluctuation items before sorting, identifies the proportion of the cumulative values to the construction duration in each category, aggregates feature proportion items, and obtains construction characteristic weight values.

5. The road work information visual analysis system of claim 4, wherein, The construction trend prediction module includes: The classification weight identification sub-module extracts the construction flow direction in the node, identifies the flow increase and decrease interval values and the node category characteristics, analyzes the matching degree of the node and the growth trend, and obtains flow trend classification weight values according to the construction characteristic weight values; The trend offset judgment sub-module calls the flow trend classification weight values, extracts adjacent time period growth trend vectors, identifies the vector angle and the growth rate ratio, and combines the time period duration length to obtain a trend offset angle matching degree; The prediction trend analysis sub-module extracts the construction trend and the direction angle in the matching degree interval according to the trend offset angle matching degree, reorganizes the extension in sequence, and obtains a reconstructed construction prediction trend table.

6. The road work information visual analysis system of claim 1, wherein, The information storage management module includes: The field separation sub-module identifies equipment records and analyzes field contents based on the construction information error dynamic correction data list, classifies and processes the field contents according to field identifiers and labeling characteristics, and generates a field classification and splitting result; The distribution encryption sub-module calls the equipment identifier field in the field classification and splitting result, extracts sequence structures and field bitmaps, performs intensity encryption according to a mapping relationship, disperses the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains encrypted node distribution data; The path indexing sub-module calls the key path and node index in the encrypted node distribution data, classifies and maps the node path, organizes the construction trajectory field and timestamp field index structure, and obtains a partition storage path of the construction analysis field.

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