A mobile terminal-based engineering supervision whole-process digital management and control method

Abstract

The application relates to the technical field of engineering supervision, and discloses a mobile terminal-based whole-process digital management and control method for engineering supervision, which monitors construction deviation events of a current engineering node, acquires a problem tracking record set and a rectification resource available capacity set related to an engineering project, and extracts a node problem characteristic parameter group; according to the node problem characteristic parameter group and the rectification resource available capacity set, a problem processing priority index is determined, and an initial rectification scheme item is generated based on the node problem characteristic parameter group; the initial rectification scheme item is subjected to resource constraint verification, an executable rectification scheme item is obtained, and the executable rectification scheme item is issued to terminals of all parties of the project; rectification process state information flow is analyzed, and a rectification effect evaluation value is obtained; the node problem characteristic parameter group is subjected to parameter weighted updating according to the rectification effect evaluation value, an updated problem characteristic parameter group is obtained, the problem closed loop efficiency and the engineering quality management and control level are improved, the rectification scheme is automatically pushed to all parties of the project, and linkage management and control of progress and supervision is realized.

Description

Technical Field

[0001] This invention relates to the field of engineering supervision technology, and more specifically, to a mobile-based digital management and control method for the entire engineering supervision process. Background Technology

[0002] Tracking and rectifying engineering milestone issues refers to the technology of achieving closed-loop management and continuous optimization of engineering quality and safety issues through the identification, analysis, solution generation, and effect evaluation of deviation events during construction. It is a key link in ensuring the timely and high-quality delivery of engineering projects.

[0003] Existing technologies often rely on on-site engineers to manually record issues or use simple spreadsheets to track problems, and then allocate rectification resources based on experience. However, the response to problem rectification in existing technologies is often affected by human factors, resulting in low timeliness. In addition, single-dimensional problem assessment contains little information, affecting the accuracy of determining rectification priorities, while multi-dimensional comprehensive analysis contains more information, leading to low efficiency in generating rectification plans and affecting the overall project progress and the rationality of resource allocation. Summary of the Invention

[0004] This invention provides a mobile-based digital management and control method for the entire process of engineering supervision. By using a mobile device, it achieves the technical effect of intelligent assessment of problem priorities and iterative optimization of rectification plans based on multi-dimensional parameter fusion. This significantly improves the efficiency of problem closure and the level of engineering quality control while ensuring the feasibility of rectification plans. The method is automatically pushed to all parties involved in the project, realizing the linkage management of progress and supervision.

[0005] To achieve the above objectives, this invention provides a mobile-based digital management and control method for the entire engineering supervision process, comprising:

[0006] Based on the mobile terminal's response to the detected construction deviation event of the current project node, the system obtains the problem tracking record set and the available capacity set of rectification resources for the related project. Based on the problem tracking record set, the system extracts the problem feature parameter set for the current project node as the node problem feature parameter set. Based on the node problem feature parameter group and the available capacity set of rectification resources, the problem handling priority index of the current project node is determined. When the problem handling priority index is higher than the preset rectification initiation threshold, an initial rectification plan item is generated based on the node problem feature parameter group. Based on the available capacity set of rectification resources, the initial rectification plan item is checked for resource constraints to obtain an executable rectification plan item. The executable rectification plan item is then sent to the terminals of all parties involved in the project, and the rectification process status information flow is collected. The rectification process status information flow is analyzed to determine the achievement of stage goals, and the rectification effect evaluation value is obtained. If the rectification effect evaluation value is determined to be less than the preset closed-loop threshold, the node problem feature parameter group is updated with parameter weighting based on the rectification effect evaluation value to obtain the updated problem feature parameter group. The rectification plan generation step is then iteratively executed based on the updated problem feature parameter group.

[0007] Further, based on the problem tracking record set, a set of problem feature parameters for the current project node is extracted as the node problem feature parameter set, including: The problem tracking record set is subjected to noise filtering to obtain a filtered problem tracking record set; The filtered problem tracking record set is decomposed into multi-dimensional attributes to obtain a problem type vector, an impact range vector, and a time urgency vector. The problem type vector, the scope of influence vector, and the time urgency vector are normalized and fused to obtain a fused problem feature matrix; The fused problem feature matrix is ​​input into the node problem mapping channel to obtain the node problem feature parameter group.

[0008] Further, based on the node problem characteristic parameter set and the available capacity set of rectification resources, the problem processing priority index of the current project node is determined, including: Calculate the quantitative value of the problem severity of the current engineering node based on the node problem characteristic parameter group; The available capacity set of the rectification resources is decomposed into three dimensions to obtain the available capacity factors of manpower, materials, and time. Calculate the overall sufficiency of rectification resources based on the available manpower capacity factor, the available material capacity factor, and the available time capacity factor; Based on the nonlinear coupling relationship between the quantitative value of the severity of the problem and the overall sufficiency of the rectification resources, the problem handling priority index is determined.

[0009] Further, based on the node problem characteristic parameter set, the quantification value of the problem severity of the current project node is calculated, including: Extract the core influencing factors from the feature parameter set of the node problem, and use them as the target influencing factor set; The target influencing factor set is weighted to obtain an influence weight vector; The severity of the problem is quantified by weighting and summing the target impact factor set according to the impact weight vector.

[0010] Furthermore, an initial rectification plan item is generated based on the node problem feature parameter group, including: The node problem feature parameter group is matched with the standard rectification case library to obtain a candidate rectification case set; The candidate rectification case set is screened for timeliness to obtain the effective rectification case set.

[0011] Furthermore, an initial rectification plan item is generated based on the node problem feature parameter group, including: Extract the sequence of rectification measures from the set of effective rectification cases as the basic sequence of rectification measures; The basic rectification measures sequence is adapted based on the node problem characteristic parameter group to obtain the initial rectification plan item.

[0012] Further, based on the available capacity set of rectification resources, resource constraint verification is performed on the initial rectification plan item to obtain an executable rectification plan item, including: Analyze the resource requirement parameter set of the initial rectification plan item; The resource demand parameter set and the available capacity set of the rectification resources are compared item by item to obtain the resource gap vector; In response to the determination that there is a resource gap item exceeding the tolerance in the resource gap vector, the redundancy compression process is performed on the sequence of rectification measures in the initial rectification plan item to obtain the compressed rectification measure sequence; The executable rectification plan item is regenerated based on the compressed rectification measure sequence and the available capacity set of rectification resources.

[0013] Furthermore, the redundancy compression process performed on the sequence of rectification measures in the initial rectification plan item to obtain a compressed sequence of rectification measures includes: A correlation analysis was performed on the sequence of rectification measures to obtain the measure coupling matrix; Redundant rectification measures are identified based on the aforementioned measures coupling matrix; The redundant rectification measures are removed from the rectification measure sequence to obtain the compressed rectification measure sequence.

[0014] Furthermore, the rectification process status information flow is analyzed to determine the achievement degree of stage goals, resulting in a rectification effect evaluation value, including: Key nodes are extracted from the status information flow of the rectification process to obtain a set of stage target statuses; Calculate the target deviation matrix of the stage target state set based on the preset rectification target set; The target deviation matrix is ​​input into the effect evaluation function to obtain the rectification effect evaluation value.

[0015] Further, the node problem feature parameter group is updated by weighting the parameters based on the rectification effect evaluation value to obtain the updated problem feature parameter group, including: The parameter adjustment weight vector is determined based on the difference between the rectification effect evaluation value and the preset closed-loop threshold. The adjusted weight vector of the parameters is weighted and the node problem feature parameter group is weighted to obtain the adjusted problem feature parameter group. The adjusted problem feature parameter group is validated. If the validation passes, the adjusted problem feature parameter group is used as the updated problem feature parameter group.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a mobile-based digital management and control method for the entire process of engineering supervision. It monitors construction deviation events at current engineering nodes, obtains a set of problem tracking records and available rectification resources for related engineering projects, and extracts a set of node problem characteristic parameters. Based on these parameters, it determines a problem handling priority index and generates initial rectification plans. The initial rectification plans are then validated against resource constraints to obtain executable plans, which are then distributed to all project stakeholders. The rectification process status information flow is analyzed to obtain a rectification effectiveness evaluation value. Based on this evaluation value, the node problem characteristic parameters are updated with weighted parameters to obtain an updated set. This improves problem closure efficiency and engineering quality control, automatically pushes rectification plans to all project stakeholders, and achieves coordinated management of progress and supervision. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The diagram illustrates a process flow diagram of a mobile-based digital management and control method for the entire engineering supervision process, according to an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0019] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application 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. Therefore, they should not be construed as limitations on this application.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, an embodiment of the present invention discloses a mobile-based digital management and control method for the entire process of engineering supervision, including: S110: Based on the mobile terminal's response to the detected construction deviation event of the current engineering node, obtain the problem tracking record set and the available capacity set of rectification resources for the related engineering project, and extract the problem feature parameter set for the current engineering node according to the problem tracking record set, as the node problem feature parameter set; S120: Determine the problem handling priority index of the current project node based on the node problem feature parameter group and the available capacity set of rectification resources. When the problem handling priority index is higher than the preset rectification initiation threshold, generate an initial rectification plan item based on the node problem feature parameter group. S130: Based on the available capacity set of rectification resources, perform resource constraint verification on the initial rectification plan item to obtain an executable rectification plan item, distribute the executable rectification plan item to the terminals of all parties involved in the project, and collect the rectification process status information flow; S140: Analyze the stage target achievement degree of the rectification process status information flow to obtain the rectification effect evaluation value; S150: If the rectification effect evaluation value is determined to be less than the preset closed-loop threshold, the node problem feature parameter group is updated with parameter weighting based on the rectification effect evaluation value to obtain the updated problem feature parameter group, and the rectification plan generation step is iteratively executed based on the updated problem feature parameter group.

[0024] In some embodiments of this application, based on the problem tracking record set, a set of problem feature parameters for the current project node is extracted as a node problem feature parameter set, including: The problem tracking record set is subjected to noise filtering to obtain a filtered problem tracking record set; The filtered problem tracking record set is decomposed into multi-dimensional attributes to obtain a problem type vector, an impact range vector, and a time urgency vector. The problem type vector, the scope of influence vector, and the time urgency vector are normalized and fused to obtain a fused problem feature matrix; The fused problem feature matrix is ​​input into the node problem mapping channel to obtain the node problem feature parameter group.

[0025] In this embodiment, an engineering node refers to a construction process unit in an engineering project that has independent acceptance conditions, such as a concrete pouring node or a steel structure installation node. Construction deviation events are monitored in real time through IoT sensors and on-site inspection terminals. For example, a deviation event is triggered when the concrete strength rebound value is lower than 90% of the design value. The problem tracking record set contains the processing data of all historical deviation events of the project and is stored in the engineering management platform database. The available capacity set of rectification resources includes resource dimension data such as the number of currently schedulable construction teams, the amount of available building materials in stock, and the remaining construction period days. Noise filtering is performed using a data cleaning algorithm to remove invalid records in the problem tracking record set with a field missing rate exceeding 30%, such as deleting historical records that lack problem descriptions or responsible persons. After filtering, the problem tracking record set retains complete and valid problem entries. Multidimensional attribute decomposition uses natural language processing technology to perform semantic analysis on the problem description text and extract structured attributes. The problem type vector contains four dimensions: quality, safety, schedule, and cost. For example, the vector corresponding to the problem "concrete cracks" is (1, 0, 0, 0). The impact scope vector includes three levels: single-entity, local, and global. For example, "single column" corresponds to (1, 0, 0). The time urgency vector includes three levels: urgent, moderate, and lenient. For example, "affecting the next process" corresponds to (1, 0, 0). Normalization and fusion processing concatenates the three vectors and normalizes them to the 0-1 interval using maximum and minimum values, eliminating differences in the scale of different attributes. The fused problem feature matrix organizes the normalized multi-dimensional data into a matrix form, with rows representing different problem instances and columns representing feature dimensions. The node problem mapping channel is a pre-trained multilayer perceptron model. The input is the problem feature matrix, and the output is a set of node problem feature parameters. The output dimension is set to 10 dimensions, covering the key representation information of the problem. This model is trained based on more than 1000 historical problem records to ensure mapping accuracy.

[0026] The beneficial effects of the above technical solutions are: noise filtering improves data quality, multi-dimensional attribute decomposition realizes the structuring of unstructured information, normalization fusion eliminates the influence of units, node problem mapping channels mine deep features through machine learning, and the extracted parameter sets comprehensively and accurately describe the essence of the problem, providing high-quality input for subsequent priority evaluation.

[0027] In some embodiments of this application, the problem processing priority index of the current engineering node is determined based on the node problem characteristic parameter set and the available capacity set of rectification resources, including: Calculate the quantitative value of the problem severity of the current engineering node based on the node problem characteristic parameter group; The available capacity set of the rectification resources is decomposed into three dimensions to obtain the available capacity factors of manpower, materials, and time. Calculate the overall sufficiency of rectification resources based on the available manpower capacity factor, the available material capacity factor, and the available time capacity factor; Based on the nonlinear coupling relationship between the quantitative value of the severity of the problem and the overall sufficiency of the rectification resources, the problem handling priority index is determined.

[0028] In this embodiment, the preset rectification initiation threshold is set to 60. This value is determined based on engineering project management experience; issues with a priority exceeding 60 require immediate initiation of the rectification process. The available rectification resource capacity set includes three types of resources: the available manpower capacity factor is the current number of available construction workers divided by the standard configuration number of workers. For example, if there are 12 available workers and the standard configuration is 15 workers, the factor is 12÷15=0.8. The available material capacity factor is the available building material inventory divided by the planned demand. For example, if there are 100 tons in inventory and 120 tons are needed, the factor is 100÷120≈0.83. The available time capacity factor is the remaining construction period days divided by the planned number of days. For example, if there are 5 days remaining and 7 days are planned, the factor is 5÷7≈0.71. The overall sufficiency of rectification resources is calculated using a weighted summation. The nonlinear coupling relationship adopts an S-shaped function mapping. When the severity quantification value is high and the resource sufficiency is low, the priority index rises sharply. For example, when the severity is 75 and the sufficiency is 0.782, the priority index is 75×(2-0.782)=91.35 points. This design reflects the multiplication effect of problem priority when resources are scarce, ensuring that critical issues still receive the highest priority when resources are limited.

[0029] The beneficial effects of the above technical solution are: the dimensional decomposition clearly quantifies the availability of various resources, the weighted product model comprehensively reflects the overall resource sufficiency, the nonlinear coupling mechanism accurately depicts the dynamic changes in priority under resource constraints, avoids the distortion problem of linear evaluation when resources are extremely scarce, and improves the rationality of resource allocation.

[0030] In some embodiments of this application, the severity quantification value of the current engineering node is calculated based on the node problem characteristic parameter set, including: Extract the core influencing factors from the feature parameter set of the node problem, and use them as the target influencing factor set; The target influencing factor set is weighted to obtain an influence weight vector; The severity of the problem is quantified by weighting and summing the target impact factor set according to the impact weight vector.

[0031] In this embodiment, the node problem feature parameter group contains 10 dimensions of information. The core impact factors are extracted by selecting the four factors that contribute most to the severity: problem type factor, impact scope factor, time urgency factor, and associated risk factor. The target impact factor set is, for example, (0.8, 0.6, 0.9, 0.7). Weight allocation is determined based on regression analysis of historical problem rectification data. For example, in 1000 historical records, when the problem type is safety-related, the rectification difficulty is on average 30% higher, so it is assigned a weight of 0.35; impact scope weight 0.25; time urgency weight 0.25; associated risk weight 0.15, forming an impact weight vector of (0.35, 0.25, 0.25, 0.15). This quantitative value objectively reflects the severity of the problem and provides core input for priority calculation. The selection and weight allocation of core impact factors are recalibrated quarterly based on new data to ensure the assessment model remains up-to-date.

[0032] The beneficial effects of the above technical solution are: the extraction of core influencing factors focuses on key representation dimensions, the weight allocation is based on historical data analysis and is scientific and objective, the weighted summation achieves multi-factor comprehensive evaluation, and the quantitative value provides a standardized measure of severity, thus freeing problem classification from subjective arbitrariness and improving the accuracy and consistency of evaluation.

[0033] In some embodiments of this application, an initial remediation plan item is generated based on the node problem feature parameter group, including: The node problem feature parameter group is matched with the standard rectification case library to obtain a candidate rectification case set; The candidate rectification case set is screened for timeliness to obtain the effective rectification case set.

[0034] In some embodiments of this application, an initial remediation plan item is generated based on the node problem feature parameter group, including: Extract the sequence of rectification measures from the set of effective rectification cases as the basic sequence of rectification measures; The basic rectification measures sequence is adapted based on the node problem characteristic parameter group to obtain the initial rectification plan item.

[0035] In this embodiment, the standard rectification case library contains over 500 historical successful rectification cases, each containing the correspondence between problem feature parameters and rectification measures. Similarity matching employs a cosine similarity algorithm, calculating the cosine of the angle between the current problem's feature vector and the feature vectors of each case in the library. The top 10 cases with a similarity greater than 0.7 are selected to form a candidate rectification case set. Timeliness screening removes cases older than two years from the case library, resulting in a valid rectification case set; for example, selecting 6 recent cases from 10. The rectification measure sequence consists of standard rectification steps common to the case set, such as a six-step sequence of "problem confirmation → cause analysis → scheme design → resource allocation → construction rectification → acceptance evaluation." Adaptability adjustments modify the standard sequence based on the specific characteristics of the current problem. For example, for the "insufficient concrete strength" problem, a "re-inspection of test blocks under the same conditions" step is added after "cause analysis," and the specific process parameters for "grouting reinforcement" are specified in "construction rectification." The initial rectification plan is ultimately output in the form of structured data, including a list of rectification measures, the person responsible for each measure, the required resources, the planned duration, and other information, forming an initial draft of an executable plan.

[0036] The beneficial effects of the above technical solutions are: case library similarity matching allows for rapid location based on historical experience; timeliness screening ensures that the solutions comply with current standards; extraction of common measure sequences provides a standardized process framework; and adaptability adjustment makes the solutions fit the specific characteristics of the current problem, significantly improving the efficiency and relevance of solution generation.

[0037] In some embodiments of this application, resource constraint verification is performed on the initial rectification plan item based on the available capacity set of rectification resources to obtain an executable rectification plan item, including: Analyze the resource requirement parameter set of the initial rectification plan item; The resource demand parameter set and the available capacity set of the rectification resources are compared item by item to obtain the resource gap vector; In response to the determination that there is a resource gap item exceeding the tolerance in the resource gap vector, the redundancy compression process is performed on the sequence of rectification measures in the initial rectification plan item to obtain the compressed rectification measure sequence; The executable rectification plan item is regenerated based on the compressed rectification measure sequence and the available capacity set of rectification resources.

[0038] In this embodiment, the resource requirement parameter set is extracted from the initial rectification plan item, including manpower requirements (e.g., 5 welders and 2 electricians needed), material requirements (3 tons of steel and 5 tons of cement), and equipment requirements (1 crane). Each requirement is compared with the available capacity set. For example, if there are 3 available welders, there is a shortage of 2; if there are 2.5 tons of available steel, there is a shortage of 0.5 tons, forming a resource gap vector (2 people, 0.5 tons). The tolerance is set at 10% of the resource gap, meaning a manpower gap of 2 people or less and a material gap of 0.3 tons or less is tolerated; exceeding these values ​​is considered unacceptable. When regenerating an executable rectification plan item, the resource requirements are recalculated based on the compressed measure sequence. For example, removing surface polishing saves 1 welder and 0.2 tons of steel. The new plan's requirements match the available capacity, reducing the manpower gap to 1 person, which is within the tolerance range, and the plan is deemed executable. This mechanism ensures that the final issued plan is truly feasible and avoids rectification stalls due to insufficient resources.

[0039] The beneficial effects of the above technical solution are: item-by-item comparison accurately identifies resource gaps, tolerance setting provides flexible judgment criteria, redundancy compression eliminates duplicate measures to reduce resource demand, and the regeneration mechanism realizes dynamic matching between the solution and resources, effectively avoiding project delays caused by unexecutable solutions.

[0040] In some embodiments of this application, the step of performing redundancy compression processing on the sequence of rectification measures in the initial rectification plan item to obtain a compressed sequence of rectification measures includes: A correlation analysis was performed on the sequence of rectification measures to obtain the measure coupling matrix; Redundant rectification measures are identified based on the aforementioned measures coupling matrix; The redundant rectification measures are removed from the rectification measure sequence to obtain the compressed rectification measure sequence.

[0041] In this embodiment, the rectification measure sequence includes six measures: "problem confirmation, cause analysis, scheme design, construction preparation, rectification construction, and acceptance evaluation." Correlation analysis assesses the functional overlap between measures. For example, the correlation between "construction preparation" and "rectification construction" is 0.3, indicating normal connection; the correlation between "cause analysis" and "scheme design" is 0.4, indicating logical dependency; and the correlation between "rectification construction" and "acceptance evaluation" is 0.1, indicating sequential relationship. The measure coupling matrix is ​​a 6×6 symmetric matrix, where matrix element values ​​represent the correlation strength between measures, ranging from 0 to 1. When identifying redundant rectification measures, a correlation threshold of 0.6 is set. If the correlation between two measures exceeds 0.6, functional redundancy is considered present. For example, if the correlation between "quality inspection" and "acceptance evaluation" reaches 0.75, and their functions overlap, "quality inspection" is identified as redundant. After removing this redundant item from the sequence, the compressed rectification measure sequence becomes five items, eliminating duplicate work. The coupling matrix threshold of 0.6 was determined based on historical implementation data analysis. When the correlation exceeds 0.6, merging measures can save more than 15% of the construction period without affecting the rectification effect. Compression processing ensures that the solution is streamlined and efficient, avoiding resource waste in repetitive steps.

[0042] The beneficial effects of the above technical solution are: correlation analysis quantifies the coupling strength between measures, the coupling matrix provides a basis for redundancy identification, threshold determination automatically locates duplicate measures, elimination operations simplify the rectification process, compressed sequences reduce resource consumption and construction period, improve the efficiency and economy of rectification plan execution, and at the same time ensure the integrity of core rectification functions.

[0043] In some embodiments of this application, the rectification process status information flow is parsed to determine the achievement degree of stage goals, thereby obtaining a rectification effect evaluation value, including: Key nodes are extracted from the status information flow of the rectification process to obtain a set of stage target statuses; Calculate the target deviation matrix of the stage target state set based on the preset rectification target set; The target deviation matrix is ​​input into the effect evaluation function to obtain the rectification effect evaluation value.

[0044] In this embodiment, the rectification process status information flow consists of daily text descriptions, on-site photos, and testing data reported by the responsible entity's terminal. Key nodes are extracted by identifying milestone events mentioned in the information through natural language processing, such as "cause analysis completed," "construction started," and "acceptance application," dividing the rectification process into four stages: problem confirmation, solution execution, process acceptance, and final acceptance. The stage target status set records the completion status of each stage; for example, the solution execution stage is marked as "completed, taking 3 days." The preset rectification target set includes the planned completion time and quality requirements for each stage; for example, the solution execution stage is planned for 2 days, and the quality requirement is "meets design requirements." When calculating the target deviation matrix, the time deviation is the actual number of days minus the planned number of days, and the quality deviation is evaluated through expert scoring; for example, the time deviation for the solution execution stage is 1 day, and the quality deviation is 0.2 (out of 1 point). The effectiveness evaluation function uses a weighted average model, with weights of 0.1, 0.4, 0.2, and 0.3 for the four stages, a weight of 0.6 for time deviation, and a weight of 0.4 for quality deviation. The evaluation value is calculated as (1 - weighted deviation of each stage) × 100. For example, if the total deviation is 0.15, the evaluation value for rectification effectiveness is 85 points. This evaluation value objectively reflects the overall rectification effect and provides a quantitative basis for determining whether the rectification is closed-loop.

[0045] The beneficial effects of the above technical solution are: key node extraction enables structured decomposition of the rectification process, target deviation matrix quantifies the execution deviation of each stage, effect evaluation function integrates time and quality dimensions, and evaluation value provides standardized effect measurement standard, so that rectification acceptance is free from subjective judgment and the objectivity and accuracy of evaluation are improved.

[0046] In some embodiments of this application, the node problem feature parameter group is updated by weighting the parameters according to the rectification effect evaluation value to obtain the updated problem feature parameter group, including: The parameter adjustment weight vector is determined based on the difference between the rectification effect evaluation value and the preset closed-loop threshold. The adjusted weight vector of the parameters is weighted and the node problem feature parameter group is weighted to obtain the adjusted problem feature parameter group. The adjusted problem feature parameter group is validated. If the validation passes, the adjusted problem feature parameter group is used as the updated problem feature parameter group.

[0047] In this embodiment, the preset closed-loop threshold is set to 80 points, the rectification effect evaluation value is, for example, 75 points, and the difference is -5 points. The parameter adjustment weight vector is determined according to the direction of the difference. If the evaluation value is lower than the threshold, it indicates that the rectification effect is not good, and the weight of the problem parameters needs to be strengthened to improve the priority of the next round. The weight vector is set, for example, (1.2, 1.1, 1.0, etc.), and each component of the node problem feature parameter group is multiplied by the corresponding weight. After adjustment, the values ​​of each component of the problem feature parameter group increase accordingly. For example, the original severity component 0.75 becomes 0.75 × 1.2 = 0.9. The validity check checks whether the adjusted parameters are within a reasonable range. For example, the severity component should not exceed 1.0. If it exceeds, it is truncated to 1.0. The logical relationship between parameters is checked. For example, when the problem type is quality-related, the time urgency should not exceed 0.9. Otherwise, the check fails. After the check passes, the updated problem feature parameter group replaces the original parameter group and is used for the generation of the next round of rectification plan. This mechanism realizes dynamic parameter optimization based on execution effect, enabling the system to have the ability to learn and improve, and avoiding repeated and inefficient rectification.

[0048] The beneficial effects of the above technical solution are: difference-driven weight adjustment enhances parameter sensitivity when the effect is not good; weighted operation realizes feedback adjustment of problem characteristics; validity verification ensures parameter rationality; update mechanism forms a closed-loop learning circuit, continuously improving the ability to identify problems and generate solutions, and avoiding rectification from falling into an inefficient cycle.

[0049] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0050] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0051] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mobile-based digital management and control method for the entire process of engineering supervision, characterized in that, include: Based on the mobile terminal's response to the detected construction deviation event of the current project node, the system obtains the problem tracking record set and the available capacity set of rectification resources for the related project. Based on the problem tracking record set, the system extracts the problem feature parameter set for the current project node as the node problem feature parameter set. Based on the node problem feature parameter group and the available capacity set of rectification resources, the problem handling priority index of the current project node is determined. When the problem handling priority index is higher than the preset rectification initiation threshold, an initial rectification plan item is generated based on the node problem feature parameter group. Based on the available capacity set of rectification resources, the initial rectification plan item is checked for resource constraints to obtain an executable rectification plan item. The executable rectification plan item is then sent to the terminals of all parties involved in the project, and the rectification process status information flow is collected. The rectification process status information flow is analyzed to determine the achievement of stage goals, and the rectification effect evaluation value is obtained. If the rectification effect evaluation value is determined to be less than the preset closed-loop threshold, the node problem feature parameter group is updated with parameter weighting based on the rectification effect evaluation value to obtain the updated problem feature parameter group. The rectification plan generation step is then iteratively executed based on the updated problem feature parameter group.

2. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 1, characterized in that, Based on the problem tracking record set, extract the problem feature parameter set for the current project node as the node problem feature parameter set, including: The problem tracking record set is subjected to noise filtering to obtain a filtered problem tracking record set; The filtered problem tracking record set is decomposed into multi-dimensional attributes to obtain a problem type vector, an impact range vector, and a time urgency vector. The problem type vector, the scope of influence vector, and the time urgency vector are normalized and fused to obtain a fused problem feature matrix; The fused problem feature matrix is ​​input into the node problem mapping channel to obtain the node problem feature parameter group.

3. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 1, characterized in that, Based on the node problem characteristic parameter set and the available capacity set of rectification resources, the problem processing priority index of the current project node is determined, including: Calculate the quantitative value of the problem severity of the current engineering node based on the node problem characteristic parameter group; The available capacity set of the rectification resources is decomposed into three dimensions to obtain the available capacity factors of manpower, materials, and time. Calculate the overall sufficiency of rectification resources based on the available manpower capacity factor, the available material capacity factor, and the available time capacity factor; Based on the nonlinear coupling relationship between the quantitative value of the severity of the problem and the overall sufficiency of the rectification resources, the problem handling priority index is determined.

4. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 3, characterized in that, Based on the node problem characteristic parameter set, calculate the quantitative value of the problem severity of the current project node, including: Extract the core influencing factors from the feature parameter set of the node problem, and use them as the target influencing factor set; The target influencing factor set is weighted to obtain an influence weight vector; The severity of the problem is quantified by weighting and summing the target impact factor set according to the impact weight vector.

5. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 1, characterized in that, An initial rectification plan item is generated based on the node problem feature parameter group, including: The node problem feature parameter group is matched with the standard rectification case library to obtain a candidate rectification case set; The candidate rectification case set is screened for timeliness to obtain the effective rectification case set.

6. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 5, characterized in that, An initial rectification plan item is generated based on the node problem feature parameter group, including: Extract the sequence of rectification measures from the set of effective rectification cases as the basic sequence of rectification measures; The basic rectification measures sequence is adapted based on the node problem characteristic parameter group to obtain the initial rectification plan item.

7. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 1, characterized in that, Based on the available capacity set of rectification resources, resource constraint verification is performed on the initial rectification plan item to obtain an executable rectification plan item, including: Analyze the resource requirement parameter set of the initial rectification plan item; The resource demand parameter set and the available capacity set of the rectification resources are compared item by item to obtain the resource gap vector; In response to the determination that there is a resource gap item exceeding the tolerance in the resource gap vector, the redundancy compression process is performed on the sequence of rectification measures in the initial rectification plan item to obtain the compressed rectification measure sequence; The executable rectification plan item is regenerated based on the compressed rectification measure sequence and the available capacity set of rectification resources.

8. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 7, characterized in that, The redundancy compression process performed on the sequence of rectification measures in the initial rectification plan item yields a compressed sequence of rectification measures, including: A correlation analysis was performed on the sequence of rectification measures to obtain the measure coupling matrix; Redundant rectification measures are identified based on the aforementioned measures coupling matrix; The redundant rectification measures are removed from the rectification measure sequence to obtain the compressed rectification measure sequence.

9. The method for digital management and control of the entire engineering supervision process based on mobile terminals according to claim 1, characterized in that, The rectification process status information flow is analyzed to determine the achievement of stage goals, resulting in a rectification effectiveness evaluation value, including: Key nodes are extracted from the status information flow of the rectification process to obtain a set of stage target statuses; Calculate the target deviation matrix of the stage target state set based on the preset rectification target set; The target deviation matrix is ​​input into the effect evaluation function to obtain the rectification effect evaluation value.

10. The method for full-process digital management and control of engineering supervision based on mobile terminals according to claim 1, characterized in that, The node problem feature parameter group is updated by weighting the parameters based on the rectification effect evaluation value, resulting in the updated problem feature parameter group, including: The parameter adjustment weight vector is determined based on the difference between the rectification effect evaluation value and the preset closed-loop threshold. The adjusted weight vector of the parameters is weighted and the node problem feature parameter group is weighted to obtain the adjusted problem feature parameter group. The adjusted problem feature parameter group is validated. If the validation passes, the adjusted problem feature parameter group is used as the updated problem feature parameter group.

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