A method and system for preparing modified emulsified asphalt

Abstract

The present invention discloses a method and system for preparing modified emulsified asphalt, and relates to the field related to the preparation of emulsified asphalt. The method comprises: obtaining target scene feature information; extracting a first performance index; traversing and matching the first significant scene feature in the target scene feature information to obtain the first target feature information; comparing with the first predetermined feature information; adjusting the first predetermined performance limit value with the first comparison deviation index as the adjustment coefficient to obtain the first target performance limit value; establishing an asphalt preparation optimization target, performing preparation optimization analysis, and obtaining the optimal asphalt preparation strategy; and controlling the preparation of the target asphalt. The method solves the technical problem that the existing modified emulsified asphalt preparation cannot be flexibly adjusted according to the specific road scene, resulting in the deviation of the prepared modified emulsified asphalt from the actual demand in performance, and achieves the technical effect of ensuring that the product performance is highly matched with the road scene demand, and improving the pertinence and effectiveness of road construction and maintenance.

Description

Technical Field

[0001] The present application relates to the field related to the preparation of emulsified asphalt, and in particular to a method and system for preparing modified emulsified asphalt. Background Art

[0002] With the rapid development of the transportation industry, the requirements for material performance in road construction and maintenance are increasing. Especially in the face of complex and changeable road conditions and environmental conditions, traditional emulsified asphalt materials can no longer meet the durability, stability and environmental protection requirements of modern transportation. As a new type of road construction material, modified emulsified asphalt has significantly improved the various properties of emulsified asphalt through the addition of modifiers, and has become a research hotspot in the field of road construction. In the preparation of modified emulsified asphalt, existing methods often adopt standardized production processes, ignoring the differentiated requirements for asphalt performance in different road scenarios (such as climate conditions, traffic flow, pavement structure, etc.).

[0003] In the current relevant technologies, the preparation of modified emulsified asphalt cannot be flexibly adjusted according to specific road scenarios, resulting in a technical problem that the performance of the prepared modified emulsified asphalt deviates from actual needs. Summary of the invention

[0004] The present application provides a method and system for preparing modified emulsified asphalt, obtains multi-dimensional scene feature information of the target application road, extracts asphalt performance indicators that are significantly correlated with these features, and adjusts the performance limits accordingly. Finally, the optimal asphalt preparation strategy is obtained through preparation optimization analysis and other technical means. The precise customization of the performance of modified emulsified asphalt is achieved, achieving the technical effect of ensuring that product performance is highly matched with road scene requirements and improving the pertinence and effectiveness of road construction and maintenance.

[0005] The present application provides a method for preparing modified emulsified asphalt, comprising:

[0006] Acquire target scene feature information, wherein the target scene feature information refers to multi-dimensional scene feature information of a target application road, and the target application road refers to a target asphalt demand application road; extract a first performance indicator among predetermined asphalt performance indicators, wherein the first performance indicator has a first correlation with a first significant scene feature; traverse and match the first significant scene feature in the target scene feature information to obtain first target feature information; compare the first target feature information with the first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index; adjust a first predetermined performance limit corresponding to the first performance indicator using the first comparison deviation index as an adjustment coefficient to obtain a first target performance limit; establish an asphalt preparation optimization target based on the first target performance limit, and perform preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy; and control the preparation of the target asphalt according to the optimal asphalt preparation strategy.

[0007] In a possible implementation, the following processing is performed:

[0008] The target scene feature information includes multiple scene features of predetermined dimensions, and the predetermined dimensions include a climate dimension, a traffic load dimension, a pavement structure dimension, and a construction method dimension.

[0009] In a possible implementation, the following processing is performed:

[0010] Read the predetermined filtering constraints; extract the first historical record in the historical asphalt application record according to the predetermined filtering constraints, the first historical record including the first historical performance index parameter and the first historical scene characteristic parameter of the first historical asphalt; extract the first historical performance parameter corresponding to the first performance index in the first historical performance index parameter; use the first historical performance parameter as the dependent variable and the first historical scene characteristic parameter as the independent variable; analyze the correlation analysis results between the dependent variable and the independent variable to obtain the first significant scene feature.

[0011] In a possible implementation, the following processing is performed:

[0012] Obtain any historical record in the historical asphalt application record, wherein the any historical record includes any historical application maintenance record of any historical asphalt; determine whether the first application maintenance in the any historical application maintenance record belongs to the predetermined road hazard maintenance; if not, add the any historical record to a candidate analysis list; randomly extract a record in the candidate analysis list and record it as the first historical record.

[0013] In a possible implementation, the following processing is performed:

[0014] Read the indicator threshold of the asphalt preparation control indicator, and establish the asphalt preparation optimization space based on the indicator threshold; obtain the first asphalt preparation strategy in the asphalt preparation optimization space; traverse the first asphalt preparation strategy in the asphalt preparation database to obtain the most similar asphalt preparation strategy, and obtain the performance test result of the most similar asphalt road prepared based on the most similar asphalt preparation strategy; determine whether the performance test result reaches the asphalt preparation optimization goal; if so, add the first asphalt preparation strategy to the candidate preparation strategy list; compare and screen the candidate preparation strategy list to obtain the optimal asphalt preparation strategy.

[0015] In a possible implementation, the following processing is performed:

[0016] Extract the first indicator among the asphalt preparation control indicators, and obtain the first indicator parameter threshold of the first indicator; obtain the indicator threshold based on the first indicator parameter threshold, and establish the asphalt preparation optimization space; wherein, the asphalt preparation control indicators include material type, shear rate, temperature and time, and the material type includes at least base asphalt type, modifier type, emulsifier type and stabilizer type.

[0017] In a possible implementation, the following processing is performed:

[0018] Obtain target performance test results of target prepared asphalt, where the target prepared asphalt refers to asphalt obtained by controlling the preparation of the target asphalt according to the optimal asphalt preparation strategy; and perform preparation inspection of the target prepared asphalt to the first target performance limit value according to the target performance test results.

[0019] In a possible implementation, the following processing is performed:

[0020] Extract the target stability test results from the target performance test results; read the predetermined feedback storage coefficient, and analyze the target stability test results based on the predetermined feedback storage coefficient to obtain the target maximum storage time; use the target maximum storage time as a storage reference for the target asphalt preparation.

[0021] The present application also provides a system for preparing modified emulsified asphalt, comprising:

[0022] A target scene feature information acquisition module, the target scene feature information acquisition module is used to acquire target scene feature information, the target scene feature information refers to the multi-dimensional scene feature information of the target application road, the target application road refers to the target asphalt demand application road; a first performance indicator extraction module, the first performance indicator extraction module is used to extract the first performance indicator in the predetermined asphalt performance indicator, the first performance indicator has a first correlation with the first significant scene feature; a first target feature information matching module, the first target feature information matching module is used to traverse and match the first significant scene feature in the target scene feature information to obtain the first target feature information; a feature information comparison module, the feature information comparison module is used to compare the first significant scene feature in the target scene feature information to obtain the first target feature information. A target feature information is compared with the first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index; a first target performance limit acquisition module, the first target performance limit acquisition module is used to adjust the first predetermined performance limit corresponding to the first performance indicator with the first comparison deviation index as the adjustment coefficient to obtain a first target performance limit; a preparation optimization analysis module, the preparation optimization analysis module is used to establish an asphalt preparation optimization target based on the first target performance limit, and perform preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy; a preparation control module, the preparation control module is used to control the preparation of the target asphalt according to the optimal asphalt preparation strategy.

[0023] A method and system for preparing modified emulsified asphalt proposed in the present application first obtain target scene feature information, which refers to multidimensional scene feature information of a target application road, and the target application road refers to a target asphalt demand application road, then extract a first performance indicator from the predetermined asphalt performance indicators, the first performance indicator has a first correlation with the first significant scene feature, and then traverse and match the first significant scene feature in the target scene feature information to obtain the first target feature information, then compare the first target feature information with the first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index, and then adjust the first predetermined performance limit corresponding to the first performance indicator with the first comparison deviation index as an adjustment coefficient to obtain a first target performance limit, and then establish an asphalt preparation optimization target based on the first target performance limit, and perform preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy, and finally control the preparation of the target asphalt according to the optimal asphalt preparation strategy, thereby achieving a technical effect of ensuring that product performance is highly matched with road scene requirements and improving the pertinence and effectiveness of road construction and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0025] Figure 1 A schematic flow chart of a method for preparing modified emulsified asphalt provided in an embodiment of the present application.

[0026] Figure 2 A schematic diagram of the structure of a system for preparing modified emulsified asphalt provided in an embodiment of the present application.

[0027] Explanation of the accompanying drawings: target scene feature information acquisition module 10, first performance indicator extraction module 20, first target feature information matching module 30, feature information comparison module 40, first target performance limit acquisition module 50, preparation optimization analysis module 60, preparation control module 70. DETAILED DESCRIPTION

[0028] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0029] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0030] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0031] The present application provides a method for preparing a modified emulsified asphalt, such as Figure 1 As shown, the method includes:

[0032] Step S100, obtaining target scene feature information, the target scene feature information refers to the multi-dimensional scene feature information of the target application road, and the target application road refers to the target asphalt demand application road. Specifically, the target scene feature information refers to all relevant feature information of the target application road, which is a collection of specific environmental conditions and usage conditions that affect the asphalt performance requirements, and is used to describe the uniqueness of the road. Determine the target application road, that is, the specific road where asphalt needs to be prepared, and collect multi-dimensional feature information of the road through on-site investigation, historical data query, expert consultation and other means, including but not limited to geographical location (latitude and longitude), climate conditions (temperature, humidity, precipitation), traffic load (traffic volume, vehicle type distribution, driving speed), road surface conditions (existing road surface materials, damage conditions), construction conditions (construction season, construction period requirements), etc.

[0033] In a possible implementation, step S100 further includes step S110, wherein the target scene feature information includes multiple scene features of predetermined dimensions, and the predetermined dimensions include climate dimension, traffic load dimension, pavement structure dimension, and construction method dimension. Specifically, determine which target road scene the modified emulsified asphalt to be prepared is to be applied to, such as highways, urban roads, bridges, etc. Prepare tools and methods for data collection, including on-site survey equipment, historical data query platforms, expert consultation questionnaires, etc. Record climate data of the area where the target application road is located, including annual average temperature, extreme temperature, annual precipitation, humidity, wind speed, etc., and evaluate the impact of climate factors on asphalt performance, including that high temperature may cause asphalt softening and low temperature may cause asphalt brittle cracking. Record traffic flow data on the target road, including daily average traffic flow, peak traffic flow, vehicle type distribution, etc., analyze the wear and pressure of traffic load on the asphalt pavement, and determine the required performance indicators such as rutting resistance and durability. Investigate the existing pavement structure of the target application road, including pavement materials, thickness, interlayer bonding, etc., and determine the performance characteristics required for the new asphalt layer based on the pavement structure analysis, such as compatibility with the old pavement, interlayer bonding strength, etc. Determine the construction conditions of the target application road, including construction season, weather conditions, construction period requirements, etc. According to the construction conditions, select appropriate construction methods and process parameters, such as paving temperature, number of compaction passes, etc., and evaluate the impact of these conditions on asphalt performance. Different dimensions correspond to different performance requirements. This implementation method comprehensively and accurately grasps the actual needs and conditions of the target application road by subdividing the dimensions of the target scene feature information, which helps to more accurately locate the required asphalt performance.

[0034] Step S200, extracting the first performance indicator from the predetermined asphalt performance indicators, wherein the first performance indicator has a first correlation with the first significant scene feature. Specifically, the predetermined asphalt performance indicator is an asphalt performance indicator directly related to road performance determined based on scientific knowledge of asphalt materials and engineering practice experience, including viscosity, stability, high temperature resistance, low temperature crack resistance, rutting resistance, waterproofness, environmental protection, etc. Analyze the correlation between each performance indicator and different scene features to determine which performance indicators have a significant relationship with specific scene features, namely, the first performance indicator and the first significant scene feature. In other words, the first performance indicator is an indicator that has a key impact on asphalt performance in the target scene; the first significant scene feature is a scene feature that has a significant correlation with the first performance indicator.

[0035] In a possible implementation, step S200 further includes step S210, reading a predetermined filtering constraint. Specifically, the predetermined filtering constraint is a rule or condition for filtering a data set, which is used to clarify which historical records should be excluded from the analysis. The predetermined filtering constraint is used to exclude road hazard records caused by force majeure (such as natural disasters, extreme weather, etc.), and only retain those historical roads that have not caused road hazards due to asphalt problems during the life cycle of being put into practical application. Step S220, extracting the first historical record in the historical asphalt application record according to the predetermined filtering constraint, the first historical record includes the first historical performance index parameter and the first historical scene feature parameter of the first historical asphalt. Specifically, traverse the historical asphalt application record database, apply the filtering constraint defined in step S210, and exclude records that do not meet the conditions. Select one from the remaining records as the first historical record, the first historical record includes the performance index parameter of asphalt and the corresponding scene feature parameter. Step S230, extract the first historical performance parameter corresponding to the first performance index in the first historical performance index parameter. Specifically, determine the specific asphalt performance index of the current analysis, such as durability, rutting resistance, low temperature crack resistance, etc. The historical performance parameters corresponding to the first performance indicator are extracted from the first historical record. Step S240, taking the first historical performance parameter as the dependent variable and the first historical scene feature parameter as the independent variable, analyzing the correlation analysis results between the dependent variable and the independent variable, and obtaining the first significant scene feature. Specifically, the first historical performance parameter is set as the dependent variable (i.e., the variable to be explained or predicted), and the first historical scene feature parameter (such as climate, traffic load, pavement structure, etc.) is set as the independent variable (i.e., the variable that may affect the dependent variable). The correlation between the dependent variable and each independent variable is analyzed using the Pearson correlation coefficient method. According to the analysis results, the scene features that have a significant correlation with the first performance indicator, i.e., the first significant scene features, are identified, and these features have an important impact on the performance of asphalt. This implementation method reduces misjudgments and decision errors caused by external interference by excluding road hazard records caused by force majeure, thereby ensuring the reliability of the analysis results.

[0036] In a possible implementation, step S220 further includes step S221, obtaining any historical record in the historical asphalt application record, wherein the any historical record includes any historical application maintenance record of any historical asphalt. Specifically, a data set stored in the historical asphalt application record is obtained by querying a database. The entire data set is traversed to obtain each historical record one by one, and each record contains information about asphalt application, maintenance and related scene features. Step S222, judging whether the first application maintenance in the any historical application maintenance record belongs to the predetermined road hazard maintenance. Specifically, for the maintenance information in each historical record, classification analysis is performed to determine what type of maintenance it belongs to. According to the predetermined road hazard maintenance standard, judging whether the first application maintenance belongs to such hazard maintenance, wherein the predetermined road hazard maintenance is a predefined series of road maintenance activities caused by asphalt performance quality problems, and these activities directly reflect the quality problems of asphalt. If the first application maintenance is caused by asphalt performance quality problems (such as insufficient durability, poor crack resistance, etc.), it is determined to belong to the predetermined road hazard maintenance; otherwise, it does not belong. Step S223, if it does not belong, adding the any historical record to the candidate analysis list. Specifically, based on the judgment result of step S222, if the first application maintenance of a certain historical record belongs to the scheduled road hazard maintenance, the record is skipped and not added to the candidate analysis list. If the first application maintenance of a certain historical record does not belong to the scheduled road hazard maintenance, that is, the asphalt application in the record did not cause road maintenance due to quality problems, the record is added to the candidate analysis list. Step S224, randomly extract the records in the candidate analysis list and record them as the first historical records. Specifically, a record is randomly selected from the candidate analysis list and marked as the first historical record for performance analysis and scene feature association. This implementation method ensures that the records in the candidate analysis list are those that perform well in actual applications and do not have maintenance activities caused by quality problems by excluding road maintenance records caused by asphalt performance quality problems. The performance indicator parameters and scene feature parameters in these records can better reflect the reasonable application and performance of asphalt, ensuring the accuracy and reliability of the analysis.

[0037] Step S300, traverse and match the first salient scene feature in the target scene feature information to obtain first target feature information. Specifically, the first salient scene feature is compared with the data in the target scene feature information library one by one to find matching items, and the matched feature information is extracted as the first target feature information for subsequent analysis.

[0038] Step S400, compare the first target feature information with the first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index. Specifically, based on conventional road scene features, a predetermined scene feature information database is established, including scene features under various conventional conditions and their corresponding benchmark values. The first target feature information is compared with the first predetermined feature information in the predetermined scene feature information (feature information corresponding to the first significant scene feature), and a deviation value is calculated to obtain a first comparison deviation index.

[0039] Step S500, adjusting the first predetermined performance limit corresponding to the first performance indicator using the first comparison deviation index as an adjustment coefficient to obtain a first target performance limit. Specifically, the adjustment coefficient is determined according to the size of the first comparison deviation index. The larger the deviation, the higher the adjustment coefficient, indicating a greater improvement in performance requirements. The first predetermined performance limit corresponding to the first performance indicator is adjusted using the adjustment coefficient to obtain a first target performance limit applicable to the target scene. The first predetermined performance limit refers to the performance limit corresponding to the predetermined scene feature information under the first performance indicator; the first target performance limit refers to the performance limit corresponding to the first performance indicator adjusted according to the target scene feature information.

[0040] Step S600, based on the first target performance limit, an asphalt preparation optimization target is established, and according to the asphalt preparation optimization target, a preparation optimization analysis is performed in the asphalt preparation optimization space to obtain the optimal asphalt preparation strategy. Specifically, based on the first target performance limit, the optimization target of asphalt preparation is set, that is, the best preparation solution that meets the performance limit is found. According to factors such as asphalt preparation process, raw material selection, additive type and dosage, an optimization space for asphalt preparation is constructed. Using an optimization algorithm, an iterative search is performed in the asphalt preparation optimization space to find the optimal asphalt preparation strategy.

[0041] In a possible implementation, step S600 further includes step S610, reading the indicator threshold of the asphalt preparation control indicator, and forming the asphalt preparation optimization space based on the indicator threshold. Specifically, the threshold range (allowed maximum and minimum values) of each key control indicator in the asphalt preparation process, such as temperature, mixing time, additive ratio, etc., is read from the user interface. According to these indicator thresholds, a multidimensional asphalt preparation parameter space is constructed, and each dimension represents a control indicator, and its value range is the threshold range of the indicator. Step S620, obtaining the first asphalt preparation strategy in the asphalt preparation optimization space. Specifically, a point is randomly selected in the asphalt preparation optimization space as the first asphalt preparation strategy. The first asphalt preparation strategy is a specific asphalt preparation plan, which includes specific values ​​of each control indicator. Step S630, traverse the first asphalt preparation strategy in the asphalt preparation database to obtain the most similar asphalt preparation strategy, and obtain the performance test results of the most similar asphalt road prepared based on the most similar asphalt preparation strategy. Specifically, the first asphalt preparation strategy is subjected to Euclidean distance similarity calculation with each strategy in the asphalt preparation database (a database storing historical asphalt preparation strategies and their corresponding asphalt road performance test results), and the strategy with the highest similarity is selected as the most similar asphalt preparation strategy. The performance test results of the asphalt road prepared by the most similar strategy, such as durability, flatness, skid resistance, etc., are read from the database. Step S640, judging whether the performance test results meet the asphalt preparation optimization target; Step S650, if so, adding the first asphalt preparation strategy to the candidate preparation strategy list. Specifically, the performance test results of the most similar asphalt preparation strategy are compared with the asphalt preparation optimization target to judge whether the requirements are met. If the performance test results meet the optimization target, the first asphalt preparation strategy is added to the candidate preparation strategy list, which is a list storing all asphalt preparation strategies that meet the optimization target. Step S660, the candidate preparation strategy list is compared and screened to obtain the optimal asphalt preparation strategy. Specifically, each strategy in the candidate preparation strategy list is evaluated for environmental protection, including energy consumption, emission types and amounts, etc. The candidate preparation strategies are ranked based on the comprehensive performance test results and environmental protection level, and the top ranked strategy is selected as the optimal asphalt preparation strategy. This implementation method effectively narrows the search scope and improves the efficiency of finding the optimal strategy by constructing an optimization space and searching in it, combined with the performance prediction of the most similar strategy in historical data. At the same time, the introduction of environmental protection level assessment as a screening condition not only ensures the performance requirements of asphalt roads, but also takes into account the environmental protection of the preparation process, meets the requirements of sustainable development, and achieves the technical effect of scientifically and comprehensively determining the optimal asphalt preparation strategy.

[0042] In a possible implementation, step S610 further includes step S611, extracting the first indicator among the asphalt preparation control indicators, and obtaining the first indicator parameter threshold of the first indicator, wherein the asphalt preparation control indicators include material type, shear rate, temperature and time, and the material type at least includes base asphalt type, modifier type, emulsifier type and stabilizer type. Specifically, the first indicator is identified from the numerous control indicators of asphalt preparation, and the first indicator is an arbitrarily selected control indicator, such as "material type", "shear rate", "temperature" or "time". For the indicator "material type", it is further subdivided into base asphalt type, modifier type, emulsifier type and stabilizer type, and these sub-items together constitute the detailed parameter set of "material type". For the first indicator, its corresponding parameter threshold is obtained from the expert system, that is, the maximum value, minimum value or specific range allowed by the first indicator. Step S612, the indicator threshold is obtained based on the first indicator parameter threshold, and the asphalt preparation optimization space is established. Specifically, the parameter thresholds of all the first indicators obtained in step S611 are integrated to form a complete parameter threshold set, that is, if the first indicator is "material type", this set includes the allowed list or range of base asphalt types, modifier types, etc. Similarly, the parameter thresholds of other control indicators (shear rate, temperature, time) are obtained, and the parameter thresholds of all control indicators are combined to form a multidimensional asphalt preparation parameter space, that is, the asphalt preparation optimization space. Each point in this space represents a possible asphalt preparation strategy, which is composed of the specific values ​​of all control indicators. This implementation method ensures the comprehensiveness and accuracy of the optimization space by gradually extracting and integrating the parameter thresholds of the asphalt preparation control indicators.

[0043] Step S700, the target asphalt is prepared and controlled according to the optimal asphalt preparation strategy. Specifically, the optimal asphalt preparation strategy is converted into specific preparation parameters and operating instructions. These parameters and instructions are implemented on the asphalt production line to accurately control the raw material ratio, temperature control, mixing time, additive addition and other links to ensure the production of modified emulsified asphalt that meets the target performance limits. The embodiment of the present application adopts the method of obtaining multi-dimensional scene feature information of the target application road, extracting asphalt performance indicators that have a significant correlation with these features, and adjusting the performance limits accordingly. Finally, the optimal asphalt preparation strategy is obtained through preparation optimization analysis and other technical means, thereby achieving precise customization of the performance of modified emulsified asphalt, ensuring that the product performance is highly matched with the road scene requirements, and improving the pertinence and effectiveness of road construction and maintenance.

[0044] In a possible implementation, the method further includes step S800, obtaining a target performance test result of a target prepared asphalt, wherein the target prepared asphalt refers to an asphalt obtained by controlling the preparation of the target asphalt according to the optimal asphalt preparation strategy. Specifically, a sample is taken from the asphalt prepared according to the optimal asphalt preparation strategy as a test sample. The performance items to be tested are determined according to the first target performance limit (such as viscosity, stability, high temperature resistance, etc.), and the test samples are tested for various performance indicators using test equipment and methods. The results of each performance test are recorded to form a target performance test report. Step S900, according to the target performance test result, the preparation inspection of the target prepared asphalt is carried out for the first target performance limit. Specifically, the target performance test result is compared with the first target performance limit to check whether all performance requirements are met. If there is a deviation between the test result and the limit, the size and cause of the deviation are evaluated to determine whether the preparation strategy needs to be adjusted or retested. According to the comparison and evaluation results, the inspection conclusion is recorded, including whether it is qualified, existing problems, and possible improvement measures. If the test result is unqualified, the test result will be fed back to the asphalt preparation process, and the preparation strategy will be adjusted as needed, and the preparation and inspection will be repeated. This implementation method verifies whether the asphalt product prepared according to the optimal asphalt preparation strategy meets the predetermined performance requirements by conducting actual performance tests on the asphalt product prepared according to the optimal asphalt preparation strategy, ensuring that the final asphalt product can truly meet the performance requirements in the target scenario.

[0045] In a possible implementation, step S800 further includes step S810, extracting the target stability test results from the target performance test results. Specifically, access the database storing the target performance test results, filter out the test results related to the target stability according to the test item identifier in the test results, extract the filtered stability test results (stability index, aging time, etc.), and prepare for further analysis. Step S820, read the predetermined feedback storage coefficient, and analyze the target stability test results based on the predetermined feedback storage coefficient to obtain the target maximum storage time. Specifically, read the predetermined feedback storage coefficient from the expert system, the predetermined feedback storage coefficient is a coefficient used to convert the stability test results into storage time, and is determined based on expert opinions. Perform mathematical operations (such as multiplication, etc.) on the target stability test results and the predetermined feedback storage coefficient to obtain the longest safe storage time of the target prepared asphalt under specific conditions. Step S830, use the target maximum storage time as a storage reference for the target prepared asphalt. Specifically, the target maximum storage time is recorded in the system database, and storage suggestions are provided to relevant personnel (such as production personnel, warehouse managers, etc.) based on the target maximum storage time, including storage conditions, storage period, etc. In the actual storage process of asphalt, monitoring is carried out according to the target maximum storage time to ensure that the asphalt is properly stored within the storage period. This implementation method provides a scientific basis for the storage of asphalt by calculating the target maximum storage time, avoiding the deterioration or performance degradation of asphalt due to long storage time, and improving storage safety.

[0046] In the above, refer to Figure 1 A method for preparing a modified emulsified asphalt according to an embodiment of the present invention is described in detail. Figure 2 A system for preparing modified emulsified asphalt according to an embodiment of the present invention is described.

[0047] A system for preparing modified emulsified asphalt according to an embodiment of the present invention is used to solve the technical problem that the existing modified emulsified asphalt preparation cannot be flexibly adjusted according to the specific road scene, resulting in the deviation of the prepared modified emulsified asphalt from the actual demand in terms of performance, so as to ensure that the product performance is highly matched with the road scene requirements and improve the pertinence and effectiveness of road construction and maintenance. A system for preparing modified emulsified asphalt includes: a target scene feature information acquisition module 10, a first performance index extraction module 20, a first target feature information matching module 30, a feature information comparison module 40, a first target performance limit acquisition module 50, a preparation optimization analysis module 60, and a preparation control module 70.

[0048] The target scene feature information acquisition module 10 is used to acquire the target scene feature information, and the target scene feature information refers to the multi-dimensional scene feature information of the target application road, and the target application road refers to the target asphalt demand application road; the first performance index extraction module 20 is used to extract the first performance index in the predetermined asphalt performance index, and the first performance index has a first correlation with the first significant scene feature; the first target feature information matching module 30 is used to traverse and match the first significant scene feature in the target scene feature information to obtain the first target feature information; the feature information comparison module 40 is used to compare the first target feature information with the first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index; the first target performance limit acquisition module 50 is used to adjust the first predetermined performance limit corresponding to the first performance indicator with the first comparison deviation index as the adjustment coefficient to obtain the first target performance limit; the preparation optimization analysis module 60 is used to establish an asphalt preparation optimization target based on the first target performance limit, and perform preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy; the preparation control module 70 is used to control the preparation of the target asphalt according to the optimal asphalt preparation strategy.

[0049] The specific configuration of the target scene feature information acquisition module 10 will be described in detail below. As described above, the target scene feature information acquisition module 10 may further include: a target scene feature information construction unit for the target scene feature information to include multiple scene features of predetermined dimensions, wherein the predetermined dimensions include a climate dimension, a traffic load dimension, a pavement structure dimension, and a construction method dimension.

[0050] The specific configuration of the first performance indicator extraction module 20 will be described in detail below. As described above, the first performance indicator extraction module 20 may further include: a predetermined screening constraint reading unit for reading predetermined screening constraints; a first historical record extraction unit for extracting a first historical record in the historical asphalt application record according to the predetermined screening constraints, the first historical record including a first historical performance indicator parameter and a first historical scene feature parameter of the first historical asphalt; a first historical performance parameter extraction unit for extracting a first historical performance parameter corresponding to the first performance indicator in the first historical performance indicator parameter; a first significant scene feature acquisition unit for taking the first historical performance parameter as a dependent variable and the first historical scene feature parameter as an independent variable, analyzing the correlation analysis result between the dependent variable and the independent variable, and obtaining the first significant scene feature.

[0051] Among them, the first history record extraction unit may further include: an arbitrary history record acquisition subunit for acquiring arbitrary history records in the historical asphalt application records, wherein the arbitrary history records include arbitrary historical application maintenance records of any historical asphalt; a judgment subunit for judging whether the first application maintenance in the arbitrary historical application maintenance record belongs to the predetermined road hazard maintenance; a candidate analysis list adding subunit for adding the arbitrary history record to the candidate analysis list if it does not belong; a record random extraction subunit for randomly extracting records in the candidate analysis list and recording them as the first history records.

[0052] The specific configuration of the preparation optimization analysis module 60 will be described in detail below. As described above, the preparation optimization analysis module 60 may further include: an asphalt preparation optimization space building unit for reading the index threshold of the asphalt preparation control index, and building the asphalt preparation optimization space based on the index threshold; a first asphalt preparation strategy acquisition unit for acquiring the first asphalt preparation strategy in the asphalt preparation optimization space; a database traversal unit for traversing the first asphalt preparation strategy in the asphalt preparation database to obtain the most similar asphalt preparation strategy, and obtaining the performance test result of the most similar asphalt road prepared based on the most similar asphalt preparation strategy; a judgment unit for judging whether the performance test result reaches the asphalt preparation optimization goal; a candidate preparation strategy list adding unit for adding the first asphalt preparation strategy to the candidate preparation strategy list if it reaches the goal; and a comparison and screening unit for comparing and screening the candidate preparation strategy list to obtain the optimal asphalt preparation strategy.

[0053] Wherein, the asphalt preparation optimization space formation unit may further include: a first indicator parameter threshold value acquisition subunit is used to extract the first indicator in the asphalt preparation control indicator and obtain the first indicator parameter threshold value of the first indicator, wherein the asphalt preparation control indicator includes material type, shear rate, temperature and time, and the material type includes at least the type of base asphalt, the type of modifier, the type of emulsifier and the type of stabilizer; the indicator threshold value acquisition subunit is used to obtain the indicator threshold value based on the first indicator parameter threshold value and form the asphalt preparation optimization space;

[0054] Among them, the system may further include: a target performance test result acquisition module for acquiring the target performance test result of the target prepared asphalt, the target prepared asphalt refers to the asphalt obtained by controlling the preparation of the target asphalt according to the optimal asphalt preparation strategy; a first target performance limit preparation inspection module for performing the first target performance limit preparation inspection on the target prepared asphalt according to the target performance test result.

[0055] Among them, the target performance test result acquisition module may further include: a target stability test result extraction unit for extracting the target stability test result from the target performance test result; a target maximum storage time acquisition unit for reading a predetermined feedback storage coefficient, and analyzing the target stability test result based on the predetermined feedback storage coefficient to obtain the target maximum storage time; a storage reference generation unit for using the target maximum storage time as a storage reference for the target asphalt preparation.

[0056] A system for preparing modified emulsified asphalt provided in an embodiment of the present invention can execute a method for preparing modified emulsified asphalt provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0057] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0058] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for preparing modified emulsified asphalt, characterized in that: include: Acquire target scene feature information, wherein the target scene feature information refers to multi-dimensional scene feature information of a target application road, and the target application road refers to a target asphalt demand application road; Extracting a first performance indicator from among the predetermined asphalt performance indicators, wherein the first performance indicator has a first correlation with a first significant scene feature; Traversing and matching the first significant scene feature in the target scene feature information to obtain first target feature information; Comparing the first target feature information with first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index; Using the first comparison deviation index as an adjustment coefficient, adjusting a first predetermined performance limit corresponding to the first performance indicator to obtain a first target performance limit; Establishing an asphalt preparation optimization target based on the first target performance limit, and performing preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy; Controlling the preparation of the target asphalt according to the optimal asphalt preparation strategy; Reading the index threshold of the asphalt preparation control index, and establishing the asphalt preparation optimization space based on the index threshold; Obtaining a first asphalt preparation strategy in the asphalt preparation optimization space; Traversing the first asphalt preparation strategy in an asphalt preparation database to obtain the most similar asphalt preparation strategy, and obtaining a performance test result of the most similar asphalt road prepared based on the most similar asphalt preparation strategy; Determining whether the performance test results meet the asphalt preparation optimization goal; If it is achieved, adding the first asphalt preparation strategy to the candidate preparation strategy list; Comparatively screening the candidate preparation strategy list to obtain the optimal asphalt preparation strategy; Extracting a first indicator from the asphalt preparation control indicators, and obtaining a first indicator parameter threshold of the first indicator; Obtaining the index threshold based on the first index parameter threshold, and establishing the asphalt preparation optimization space; Among them, the asphalt preparation control indicators include material type, shear rate, temperature and time, and the material type at least includes base asphalt type, modifier type, emulsifier type and stabilizer type.

2. A method for preparing a modified emulsified asphalt according to claim 1, characterized in that: The target scene feature information includes multiple scene features of predetermined dimensions, and the predetermined dimensions include a climate dimension, a traffic load dimension, a pavement structure dimension, and a construction method dimension.

3. The method for preparing a modified emulsified asphalt according to claim 1, characterized in that: include: Read the predefined filter constraints; Extracting a first historical record from the historical asphalt application records according to the predetermined screening constraint, the first historical record including a first historical performance index parameter and a first historical scenario characteristic parameter of the first historical asphalt; Extracting a first historical performance parameter corresponding to the first performance indicator from the first historical performance indicator parameters; Taking the first historical performance parameter as a dependent variable and taking the first historical scene feature parameter as an independent variable; The correlation analysis result between the dependent variable and the independent variable is analyzed to obtain the first significant scene feature.

4. A method for preparing a modified emulsified asphalt according to claim 3, characterized in that: include: Obtain any historical record in the historical asphalt application record, wherein the any historical record includes any historical application and maintenance record of any historical asphalt; determining whether a first application maintenance in any historical application maintenance record belongs to a predetermined road hazard maintenance; If not, adding the arbitrary historical records to a candidate analysis list; A record in the candidate analysis list is randomly extracted and recorded as the first historical record.

5. The method for preparing a modified emulsified asphalt according to claim 1, characterized in that: Also includes: Obtaining a target performance test result of a target prepared asphalt, wherein the target prepared asphalt refers to asphalt obtained by controlling the preparation of the target asphalt according to the optimal asphalt preparation strategy; The target prepared asphalt is subjected to preparation inspection of the first target performance limit according to the target performance test results.

6. The method for preparing a modified emulsified asphalt according to claim 5, characterized in that: Also includes: Extracting target stability test results from the target performance test results; Reading a predetermined feedback storage coefficient, and analyzing the target stability test result based on the predetermined feedback storage coefficient to obtain a target maximum storage time; The target maximum storage time is used as a storage reference for the target asphalt preparation.

7. A system for preparing modified emulsified asphalt, characterized in that: The system is used to implement the method for preparing a modified emulsified asphalt according to any one of claims 1 to 6, and the system comprises: A target scene feature information acquisition module, wherein the target scene feature information acquisition module is used to acquire target scene feature information, wherein the target scene feature information refers to multi-dimensional scene feature information of a target application road, wherein the target application road refers to a target asphalt demand application road; A first performance index extraction module, the first performance index extraction module is used to extract a first performance index among the predetermined asphalt performance indexes, the first performance index having a first correlation with a first significant scene feature; A first target feature information matching module, the first target feature information matching module is used to traverse and match the first significant scene feature in the target scene feature information to obtain first target feature information; A feature information comparison module, the feature information comparison module is used to compare the first target feature information with first predetermined feature information in the predetermined scene feature information to obtain a first comparison deviation index; A first target performance limit value acquisition module, the first target performance limit value acquisition module is used to adjust the first predetermined performance limit value corresponding to the first performance indicator by using the first comparison deviation index as an adjustment coefficient to obtain a first target performance limit value; A preparation optimization analysis module, wherein the preparation optimization analysis module is used to establish an asphalt preparation optimization target based on the first target performance limit, and to perform preparation optimization analysis in the asphalt preparation optimization space according to the asphalt preparation optimization target to obtain an optimal asphalt preparation strategy; A preparation control module is used to control the preparation of the target asphalt according to the optimal asphalt preparation strategy.

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