Road maintenance construction equipment operation safety evaluation method and system

Abstract

The invention discloses a road maintenance construction equipment operation safety evaluation method and system, and relates to the technical field of equipment safety evaluation, and the method comprises the steps: collecting equipment multi-source operation state data, and carrying out the normalization processing, and forming a standardized working condition vector sequence; by taking each time sequence vector in the working condition vector sequence as a graph node, constructing a weighted adjacency matrix to generate a working condition evolution state graph; constructing a task feature vector according to the demand of a to-be-executed task, matching atlas nodes, calculating a path comprehensive score, and screening an optimal evolution path; all nodes in the optimal evolution path are mapped to a construction area, and the comprehensive score of the path is corrected by fusing environmental factors; and grading and classifying the corrected score and outputting a safety level and a control strategy. According to the invention, through dynamic risk correction and evolution path pre-judgment, closed-loop intelligent decision-making of construction safety is realized, and early warning timeliness and environmental adaptability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment safety evaluation, and in particular to a method and system for evaluating the operation safety of construction equipment for highway maintenance. Background Art

[0002] Safety assessment technology for highway maintenance and construction equipment has been gradually evolving towards intelligent systems in recent years. Existing technologies primarily rely on IoT sensors to collect real-time equipment operating parameters (such as vibration spectrum, oil temperature, and current fluctuations), combined with threshold warning mechanisms to enable basic fault diagnosis. With the penetration of machine learning technology, some research is attempting to classify and assess equipment status using static models such as support vector machines (SVMs) and random forests. Furthermore, the introduction of digital twin technology enables visual analysis of equipment operating scenarios, mapping physical entity states through 3D modeling.

[0003] However, the current technical focus is still on the offline health assessment of a single device, lacking in-depth exploration of the adaptability of dynamic construction tasks and the evolution laws of multi-source temporal states; existing methods mostly use discrete state snapshot analysis, and a collaborative evaluation system covering the entire chain of "equipment-task-environment" has not yet been established, making it difficult to support real-time safety decisions under complex working conditions. Summary of the Invention

[0004] The present invention relates to the technical field of safety evaluation of highway maintenance and construction equipment. In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing construction equipment safety evaluation method has the problems of lack of temporal evolution correlation in discrete state evaluation and the inability of static models to dynamically couple task requirements and geographical environment risks.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating the operational safety of construction equipment for highway maintenance, comprising obtaining multi-source operational status data of the construction equipment during task execution, normalizing the multi-source operational status data, and obtaining a sequence of operating condition vectors;

[0008] Each time series vector in the working condition vector sequence is used as a graph node to establish a directed graph structure and construct a weighted adjacency matrix to generate a working condition evolution state map;

[0009] Constructing a task feature vector according to the requirements of the task to be executed, matching and scoring the working condition evolution map with the task feature vector, constructing a path task matching function, calculating the comprehensive score of the path, and selecting the optimal evolution path;

[0010] Map each node in the optimal evolution path to the actual construction area, construct an environmental risk correction function, correct the path comprehensive score, and obtain the environmental correction score;

[0011] The environmental correction score is classified according to the preset score segment interval, the corresponding operation safety level label is output, and corresponding control strategy suggestions are output according to different levels.

[0012] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance according to the present invention, the steps of obtaining multi-source operation status data of the construction equipment during task execution, normalizing the multi-source operation status data, and obtaining a working condition vector sequence are as follows:

[0013] Synchronously trigger and collect data from sensors deployed on construction equipment to obtain multi-source operating status data of construction equipment during task execution;

[0014] Align the multi-source operation status data according to the unified timestamp and perform normalization processing to generate a multi-source operation status data matrix;

[0015] Normalizing each parameter in the original matrix of multi-source operation status data to generate a multi-source operation status data matrix;

[0016] During the task execution process, the multi-source operation status data matrix is ​​vectorized to form a multi-source operation status data vector sequence;

[0017] A data consistency check is performed on each moment vector of the multi-source operating status data vector sequence and sorted by time to form an operating condition vector sequence.

[0018] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance described in the present invention, the following specific steps are used to establish a directed graph structure and a weighted adjacency matrix by using each time series vector in the working condition vector sequence as a graph node, and to generate a working condition evolution state map:

[0019] The working condition vectors are extracted one by one in ascending order of time and defined as graph nodes to form a graph node set;

[0020] Based on the time relationship and state difference between two adjacent graph nodes in the graph node set, the state transition weight is calculated to generate the graph edge set and corresponding weights;

[0021] Merge the graph node set and the graph edge set to construct a directed graph structure;

[0022] Map the edge weights in the directed graph to a matrix form according to the node index position to generate a weighted adjacency matrix;

[0023] The graph node set, graph edge set, directed graph structure, and weighted adjacency matrix are uniformly encapsulated to generate a working condition evolution state graph.

[0024] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance described in the present invention, the following specific steps are taken: constructing a task feature vector according to the requirements of the task to be executed, matching and scoring the working condition evolution map with the task feature vector, and constructing a path task matching function.

[0025] According to the specific construction information of the task to be executed and the working condition vector, they are normalized into the task feature vector;

[0026] Based on the working condition evolution state graph, all path combinations of fixed depth are extracted from the graph node set to form a construction path set as the task matching scoring object set;

[0027] Construct a path task matching function, match and score the task feature vector with the working condition vector corresponding to each node of each path in the path set of the task matching scoring object one by one, and record the task matching score of each path;

[0028] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance according to the present invention, the steps of calculating the comprehensive score of the paths and selecting the optimal evolution path are as follows:

[0029] Calculate the path condition change rate based on the state difference between consecutive nodes in each path;

[0030] Calculate the comprehensive path score of each path based on the task matching score and the rate of change of working conditions between nodes in the path;

[0031] The path with the highest score in the comprehensive path score is selected as the optimal evolution path for task execution.

[0032] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance described in the present invention, the following specific steps are used to map each node in the optimal evolution path to the actual construction area and construct an environmental risk correction function:

[0033] Each working condition vector node in the optimal evolution path of task execution is mapped to the construction time segment in the task plan in chronological order to form a time mapping sequence of the optimal evolution path of task execution;

[0034] Based on the time mapping sequence of the optimal evolution path of task execution, environmental factor information is obtained to form a construction environmental factor matrix;

[0035] According to the mapping relationship between the working condition vector nodes and the environmental factors of the construction environment factor matrix in the same time segment in the optimal evolution path of task execution, an environmental adaptation combination sequence is constructed. Based on the interference degree characteristics of the environmental factors in the environmental adaptation combination sequence, an exponential environmental risk correction function is established to obtain the environmental risk correction values ​​of all nodes in the optimal evolution path.

[0036] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance according to the present invention, the comprehensive path score is corrected to obtain the environmentally corrected score. The specific steps are as follows:

[0037] Arrange the environmental risk correction values ​​of all nodes in the optimal evolutionary path of task execution in path order to obtain the environmental risk correction value sequence of the optimal evolutionary path of task execution;

[0038] Based on the correspondence between the comprehensive path score of each node in the optimal evolutionary path of task execution and the environmental risk correction value of each node in the optimal evolutionary path, a path environment correction score sequence is formed;

[0039] Calculate the average of the scores of each node in the path environment-corrected score sequence as the environment-corrected score of the optimal evolutionary path corresponding to the task execution.

[0040] As a preferred embodiment of the method for evaluating the operation safety of construction equipment for highway maintenance according to the present invention, the environmentally corrected scores are classified according to preset score segments and the corresponding operation safety level labels are output. The specific steps are as follows:

[0041] Compare the modified score of the optimal evolutionary path environment of task execution with the preset score segment interval, determine the score interval and assign a safety level label;

[0042] As a preferred solution of the method for evaluating the operation safety of construction equipment for highway maintenance according to the present invention, the specific steps of outputting corresponding control strategy suggestions according to different levels are as follows:

[0043] Based on the operation safety level label and according to the preset operation control strategy table, the control strategy suggestion that matches the operation safety level label is output.

[0044] In a second aspect, the present invention provides a construction equipment operation safety evaluation system for highway maintenance, comprising a data normalization module for acquiring multi-source operation status data of construction equipment during task execution, normalizing the multi-source operation status data, and acquiring a working condition vector sequence;

[0045] A graph construction module is used to use each time series vector in the operating condition vector sequence as a graph node, establish a directed graph structure and construct a weighted adjacency matrix to generate an operating condition evolution state graph;

[0046] The path matching decision module is used to construct a task feature vector based on the requirements of the task to be executed, match and score the working condition evolution map with the task feature vector, construct a path task matching function, calculate the comprehensive path score, and select the optimal evolution path;

[0047] The path score correction module is used to map each node in the optimal evolution path to the actual construction area, construct an environmental risk correction function, correct the path comprehensive score, and obtain the environmentally corrected score;

[0048] The control strategy recommendation module is used to classify the environmental correction scores according to the preset score segmentation interval, output the corresponding operation safety level label, and output corresponding control strategy recommendations based on different levels.

[0049] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for evaluating the operation safety of construction equipment for highway maintenance as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for evaluating the operation safety of construction equipment for highway maintenance as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: by constructing a weighted adjacency matrix through time series vectors to generate a working condition evolution state map, quantitative prediction of the dynamic evolution path of the equipment operation state is achieved; by coupling task feature vector matching with the environmental risk correction function, dynamic safety adaptability in complex construction scenarios is achieved; by generating a hierarchical control strategy through preset scoring intervals, a closed-loop linkage between risk decision-making and execution control is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 The figure is a flow chart of a method for evaluating the operation safety of construction equipment for highway maintenance.

[0054] Figure 2 This is a schematic diagram of a highway maintenance construction equipment operation safety evaluation system.

[0055] Figure 3 Construct a schematic diagram for the operating condition evolution state map.

[0056] Figure 4 Schematic diagram of the path comprehensive scoring and environmental correction process. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0060] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for evaluating the operation safety of construction equipment for highway maintenance, comprising the following steps:

[0061] S1: Obtain multi-source operating status data of construction equipment during task execution, normalize the multi-source operating status data, and obtain a working condition vector sequence.

[0062] Synchronously trigger and collect data from sensors installed on construction equipment to obtain multi-source operating status data of construction equipment during task execution.

[0063] Furthermore, before the task is executed, the electrical parameters, mechanical parameters, thermal parameters and control behavior parameters of the construction equipment during the task execution are obtained by performing data collection operations on the sensors deployed on the construction equipment.

[0064] The synchronous trigger acquisition operation is as follows:

[0065] The sensing parameters are time-sequence aligned according to a unified timestamp to form multi-source operating status data of the construction equipment during task execution.

[0066] Furthermore, the electrical parameters, mechanical parameters, thermal parameters and control behavior parameters of the construction equipment during task execution are aligned according to a unified timestamp to construct multi-source operation status data.

[0067] Each parameter in the multi-source operating status data is normalized separately to generate a multi-source operating status data matrix.

[0068] Furthermore, maximum and minimum normalization processing is performed on each electrical parameter, mechanical parameter, thermal parameter and control behavior parameter in the multi-source operating status data to generate a multi-source operating status data matrix.

[0069] For example, each indicator is normalized to the [0,1] interval according to the historical maximum and minimum values, thereby obtaining a normalized multi-source operating status data matrix.

[0070] During the task execution process, the multi-source operation status data matrix is ​​vectorized to generate a multi-source operation status data vector sequence.

[0071] Furthermore, the multi-source operation status data matrix is ​​segmented and reconstructed using the sliding time window method to construct a multi-source operation status data time series set. Each time window in the multi-source operation status data time series set is converted into an ordered vector in chronological order to form a multi-source operation status data vector sequence.

[0072] A data consistency check is performed on each moment vector of the multi-source operating status data vector sequence and sorted by time to form an operating condition vector sequence.

[0073] Furthermore, taking the multi-source operating status data vector sequence as input, the electrical parameter sequence, mechanical parameter sequence, thermal parameter sequence and control behavior parameter sequence of the construction equipment during the task execution are extracted according to the dimensions, and based on the electrical parameter sequence, mechanical parameter sequence, thermal parameter sequence and control behavior parameter sequence of the construction equipment during the task execution, the maximum and minimum values ​​of each parameter dimension in the historical window are respectively counted to construct a parameter normalization interval table. Subsequently, each dimension parameter in the multi-source operating status data vector sequence is subjected to maximum and minimum normalization processing according to the corresponding maximum and minimum values ​​of the parameter in the parameter normalization interval table to generate a normalized multi-source operating status data vector sequence, and the vectors at each moment in the normalized multi-source operating status data vector sequence are checked for consistency according to the data continuity rule, for example, sampling points with a mutation rate exceeding 50% are eliminated; finally, the normalized multi-source operating status data vector sequence that meets the continuity rule is arranged in chronological order to generate a working condition vector sequence of the construction equipment during the task execution.

[0074] S2: Each time series vector in the working condition vector sequence is used as a graph node, a directed graph structure is established, and a weighted adjacency matrix is ​​constructed to generate a working condition evolution state map.

[0075] The operating condition vectors are extracted one by one in chronological order and defined as graph nodes to form a graph node set.

[0076] Furthermore, each operating condition vector in the operating condition vector sequence is extracted one by one in chronological order, and each operating condition vector is taken as an independent node to form a graph node set.

[0077] Based on the time relationship and state difference between two adjacent graph nodes in the graph node set, the state transition weight is calculated, and the graph edge set and corresponding weights are generated.

[0078] Furthermore, the graph edge weight is calculated according to the Euclidean distance between the time sequence relationship between each two adjacent graph nodes in the graph node set and the state vector.

[0079] Based on the state difference and time change relationship between the working condition vectors, the similarity weight of the state transition is constructed and expressed as:

[0080] ;

[0081] in, Represents a slave node To Node The similarity weight of the state transition, Indicates the first The working condition vector at the moment, Indicates the first The working condition vector at the moment, Represents the temperature factor, which ranges from (0, +∞) and is obtained by comparing different The changing trend of state transfer weight under the value, Status To status The time difference experienced.

[0082] Construct the graph edge set and corresponding weights of construction equipment during task execution.

[0083] The graph node set and the graph edge set are structurally merged to construct a directed graph structure.

[0084] Furthermore, the graph node set and the graph edge set are structurally connected to construct a directed graph structure of the construction equipment during the task execution process.

[0085] Map the edge weights in the directed graph to a matrix form according to the node index position to generate a weighted adjacency matrix.

[0086] Furthermore, the weight of each edge in the directed graph structure is filled into the matrix according to the index positions of the starting point and the end point in the graph node set to generate a weighted adjacency matrix.

[0087] The graph node set, graph edge set, directed graph structure and weighted adjacency matrix are uniformly encapsulated to generate a state graph of the working condition evolution of construction equipment during task execution.

[0088] Furthermore, the graph node set, graph edge set, directed graph structure and weighted adjacency matrix of construction equipment are taken as input and uniformly processed through structured encapsulation to generate a state graph of the working condition evolution of construction equipment during task execution.

[0089] S3: Construct a task feature vector based on the requirements of the task to be executed, match and score the working condition evolution map with the task feature vector, construct a path task matching function, calculate the comprehensive score of the path, and select the optimal evolution path.

[0090] The specific construction information and working condition vector of the task to be executed are normalized into a task feature vector.

[0091] Furthermore, a task feature vector with the same dimension as the working condition vector is constructed based on the construction plan document and the information such as the construction goals, construction objects, load requirements, operation stability indicators and duration indicators of the tasks to be executed in the historical task database, and the maximum and minimum method is used to normalize the various indicators in the task feature vector. For example, the load stability index is normalized to the interval [0.0, 1.0] to form a task feature vector.

[0092] Based on the working condition evolution state graph, all path combinations of fixed depth are extracted from the graph node set to form a construction path set as the task matching scoring object set.

[0093] Furthermore, based on the state graph of the working condition evolution of construction equipment during the task execution, a depth-first traversal is performed from any node in the graph node set at a fixed depth and according to the fixed depth parameter. Under the constraint of the directed edge structure of the graph, all continuous directed paths starting from the starting point and with a length not exceeding the specified depth are extracted. Each sequence of working condition nodes obtained that contains a time order and non-repeated nodes constitutes a path combination. All path combinations are combined to construct a path collection as a task matching scoring object collection.

[0094] A path-task matching function is constructed based on the multidimensional feature similarity between the working condition vector of the constructed path node and the task feature vector. The task feature vector and the working condition vector corresponding to each node of each path in the path set of the task matching scoring object are matched and scored one by one, and the task matching score of each path is recorded.

[0095] Furthermore, the task feature vector and the working condition vector corresponding to each node in each path in the path set are scored node by node through the path task matching function to obtain the task matching score.

[0096] Among them, the path task matching function is fitted in the form of an exponential decay function of the characteristic differences between the task feature vector and the working condition vector in each dimension, and the expression is:

[0097] ;

[0098] in, Represents the score value of the path task matching function, represents the number of dimensions of the task feature vector, Indicates the The weight coefficient of the feature dimension ranges from [0,1]. Indicates the The sensitivity factor of the feature dimension ranges from (0, +∞). Indicates the The characteristic value of the node in the working path in the dimension, Indicates the The expected feature value of the task to be performed in the dimension, Indicates the actual status and task requirements in the task path For example, if the feature value of a node indicates that the current speed of the device in the running path is 40 km / h, then =40, the expected speed is 50 km / h, then =50.

[0099] It should be noted that By analyzing a large amount of historical task execution data, regression analysis is used to calculate the sensitivity of each dimension to changes in the score, and weights are assigned accordingly. Using weights in the range of [0,1] can avoid numerical instability caused by excessive differences in dimension scores.

[0100] Record the task matching score of each task path.

[0101] The path condition change rate is calculated based on the state difference between consecutive nodes in each task path.

[0102] Furthermore, the path condition change rate between adjacent nodes is expressed as:

[0103] ;

[0104] in, Indicates the Hedi The rate of change of working conditions between two nodes, Indicates the The working condition vector of each node, Indicates the The working condition vector of each node, Indicates the Hedi The time difference between two nodes.

[0105] Among them, the task path The overall operating condition change rate expression is:

[0106] ;

[0107] in, Indicates the task path The overall operating condition change rate, Indicates the task path The total number of nodes in the.

[0108] The comprehensive path score of each path is calculated based on the task matching score and the rate of change of working conditions between nodes in the task path.

[0109] Furthermore, the expression for calculating the comprehensive score of the path is:

[0110] ;

[0111] in, Indicates the task path of Path comprehensive score, Indicates the task path of Task matching score, Represents the weighted coefficient of the task matching score, with a value range of [0,1]. The weighted coefficient representing the rate of change of the operating condition, with a value range of [0,1].

[0112] It should be noted that =1, and It collects historical task data and uses regression analysis to evaluate task matching scores. and the rate of change of operating conditions The weight of the impact on the final performance indicator.

[0113] By calculating the comprehensive scores of all task paths, the path with the highest score is selected as the optimal evolution path.

[0114] S4: Map each node in the optimal evolution path to the actual construction area, construct an environmental risk correction function, correct the comprehensive score of the path, and obtain the environmental correction score.

[0115] Each working condition vector node in the optimal evolution path of task execution is mapped to the construction time segment in the task plan in chronological order to form a time mapping sequence of the optimal evolution path of task execution.

[0116] Based on the time mapping sequence of the optimal evolution path of task execution, the environmental factor information is obtained to form the construction environment factor matrix.

[0117] Furthermore, based on each construction time segment in the optimal evolution path time mapping sequence corresponding to task execution, environmental factor information such as terrain slope, surface humidity, wind speed and direction, particulate matter concentration, and geological disturbance index in the corresponding construction area is obtained to construct a construction environment factor matrix.

[0118] According to the mapping relationship between the working condition vector nodes and the environmental factors in the construction environment factor matrix in the same time segment in the optimal evolution path of task execution, an environmental adaptation combination sequence is constructed, and an exponential environmental risk correction function is established based on the interference degree characteristics of the environmental factors in the environmental adaptation combination sequence.

[0119] Furthermore, each working condition vector node in the optimal evolutionary path of task execution is combined with the environmental factor of the same time segment in the construction environment factor matrix to form the optimal evolutionary path environment adaptation combination sequence corresponding to task execution. Then, based on the degree of interference of each environmental factor in the optimal evolutionary path environment adaptation combination sequence corresponding to task execution on the equipment operation stability, an environmental risk correction function is constructed, which is expressed as:

[0120] ;

[0121] in, represents the optimal evolution path The corresponding environmental risk correction value, Indicates the number of environmental factors involved in the risk modification calculation, Indicates the The weight coefficient of each environmental factor ranges from [-5,5]. represents the optimal evolution path In the Environmental evaluation value under each environmental factor dimension, Indicates the multiplication operation of the exponential terms affecting all environmental factors;

[0122] It should be noted that It uses a large amount of historical task execution data to use multivariate regression method to calculate the environmental factors The correlation with the equipment operation stability index and the value range is limited to [-5,5] to avoid certain environmental factors from having an extreme impact on the results.

[0123] Among them, the environmental assessment value The expression is:

[0124] ;

[0125] in, For the The sensitivity coefficient of each environmental factor is in the range of (0,10], is the standardized environmental factor value.

[0126] It should be noted that The regression modeling method is used to calculate the contribution of each environmental factor to the risk change, and the contribution is mapped to The value range is (0,10) to control the variation of the exponential function term during the calculation process, and avoid interference with subsequent scoring calculations caused by values ​​that are too large or too small.

[0127] For example, for each node The mapped construction area collects multiple environmental factors. The actual observed value of the environmental factor is ,right After the maximum and minimum normalization, the standardized environmental factor value is obtained .

[0128] The environmental risk correction values ​​of all nodes in the optimal evolutionary path of task execution are arranged in path order to obtain the environmental risk correction value sequence of the optimal evolutionary path of task execution.

[0129] Based on the correspondence between the comprehensive path score of each node in the optimal evolutionary path of task execution and the environmental risk correction value of each node in the optimal evolutionary path, a path environment-corrected score sequence is formed.

[0130] Furthermore, for each node in the optimal evolutionary path corresponding to the task execution, the path comprehensive score of the corresponding node in the path comprehensive score and the environmental risk correction value corresponding to the node in the environmental risk correction function sequence of the optimal evolutionary path corresponding to the task execution are obtained respectively, forming the environmentally corrected score sequence of the optimal evolutionary path corresponding to the task execution, which is expressed as:

[0131] ;

[0132] in, Indicates the The environmentally corrected score of each node is Indicated as the first in the comprehensive score of the path The comprehensive score of the path of each node, It is represented as the optimal evolution path corresponding to the task execution in the environmental risk correction function sequence. Environmental risk correction value of each node.

[0133] Calculate the average value of the environment-corrected scores of each node in the path environment-corrected score sequence, and use the average value of the path environment-corrected score sequence as the environment-corrected score of the optimal evolutionary path corresponding to the task execution.

[0134] S5: Classify the environmentally corrected scores according to the preset score segmentation intervals, output the corresponding operation safety level label, and output corresponding control strategy recommendations based on different levels.

[0135] The optimal evolutionary path environment-corrected score of the task execution is compared with the preset score segmentation interval to determine the score interval of the optimal evolutionary path environment-corrected score and assign a safety level label.

[0136] Furthermore, the preset scoring segment intervals are multiple continuous and non-overlapping real closed intervals set for the corrected scoring of the optimal evolutionary path environment for task execution, and the interval threshold is the boundary value between each adjacent scoring interval, with a value range of [0,1].

[0137] The boundary value between each adjacent scoring interval is determined according to the project objectives and safety requirements.

[0138] The corrected score of the optimal evolutionary path environment corresponding to the task execution is compared with the preset score segmentation interval.

[0139] For example, when the corrected score of the optimal evolutionary path environment corresponding to task execution is greater than 0.85, it is in the highest score range; when the corrected score of the optimal evolutionary path environment corresponding to task execution is between 0.55 and 0.70, it is in the middle score range; when the corrected score of the optimal evolutionary path environment corresponding to task execution is less than 0.55, it is in the lowest score range.

[0140] The corresponding operation safety level label is assigned to the score segment interval where the score is corrected according to the optimal evolution path environment corresponding to the task execution.

[0141] For example, the operation safety level label corresponding to the highest scoring interval is safe, the operation safety level label corresponding to the middle scoring interval is low risk, and the operation safety level label corresponding to the lowest scoring interval is high risk.

[0142] Based on the operation safety level label, the preset operation control strategy table is searched and the control strategy suggestion that matches the operation safety level label is output.

[0143] It should be noted that the operation control strategy table is formulated based on the analysis results of historical operation data, equipment operation specifications, accident cases and expert knowledge base.

[0144] Furthermore, based on the operation safety level label, the record row corresponding to the operation safety level label is searched in the operation control strategy table. The operation control strategy table presets the control strategy recommendation content corresponding to each level label. For example, when the operation safety level label is safe, the corresponding strategy is "immediate shutdown and alarm", the lower risk corresponds to "reduce load and start inspection process", and the higher risk corresponds to "maintain current status and continue monitoring". After finding the matching row, the control strategy recommendation field is extracted as the final output; if the operation safety level label does not find a match in the strategy table, the default strategy recommendation is output, such as "manual review" or "maintain current status and alarm" to realize the control strategy recommendation output based on the operation safety level label.

[0145] This embodiment also provides a highway maintenance construction equipment operation safety evaluation system, including:

[0146] The data normalization module is responsible for obtaining multi-source operating status data of construction equipment during task execution, normalizing the multi-source operating status data, and obtaining a working condition vector sequence;

[0147] Graph construction module: This module uses each time series vector in the working condition vector sequence as a graph node, establishes a directed graph structure and constructs a weighted adjacency matrix to generate a working condition evolution state graph;

[0148] Path matching decision module: This module constructs a task feature vector based on the requirements of the task to be executed, matches and scores the working condition evolution map with the task feature vector, constructs a path-task matching function, calculates the comprehensive path score, and selects the optimal evolution path;

[0149] Path score correction module: This module is responsible for mapping each node in the optimal evolution path to the actual construction area, constructing an environmental risk correction function, and correcting the path comprehensive score to obtain the environmentally corrected score;

[0150] The control strategy recommendation module classifies the environmental correction scores according to the preset score segmentation intervals, outputs the corresponding operation safety level label, and outputs corresponding control strategy recommendations based on different levels.

[0151] This embodiment also provides a computer device, which is suitable for a method for evaluating the operation safety of construction equipment for highway maintenance, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for evaluating the operation safety of construction equipment for highway maintenance as proposed in the above embodiment.

[0152] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0153] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements a method for evaluating the operation safety of construction equipment for highway maintenance as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0154] In summary, the present invention achieves quantitative prediction of the dynamic evolution path of the equipment operating status by: constructing a weighted adjacency matrix based on time series vectors to generate a working condition evolution state map; achieving dynamic safety adaptability in complex construction scenarios by coupling task feature vector matching with environmental risk correction functions; and achieving closed-loop linkage between risk decision-making and execution control by generating a hierarchical control strategy through preset scoring intervals.

[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the operational safety of construction equipment for highway maintenance, characterized by: include, Acquire multi-source operating status data of construction equipment during task execution, normalize the multi-source operating status data, and obtain a working condition vector sequence; Each time series vector in the working condition vector sequence is used as a graph node to establish a directed graph structure and construct a weighted adjacency matrix to generate a working condition evolution state map; Construct a task feature vector based on the requirements of the task to be executed, match and score the working condition evolution map with the task feature vector, construct a path task matching function, calculate the comprehensive score of the path, and select the optimal evolution path; Map each node in the optimal evolution path to the actual construction area, construct an environmental risk correction function, correct the path comprehensive score, and obtain the environmental correction score; The environmentally corrected scores are classified according to the preset score segmentation intervals, and the corresponding operation safety level labels are output. Corresponding control strategy recommendations are output according to different levels.

2. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 1, characterized in that: The method of obtaining multi-source operating status data of the construction equipment during the task execution process, normalizing the multi-source operating status data, and obtaining a working condition vector sequence comprises the following specific steps: Synchronously trigger and collect data from sensors deployed on construction equipment to obtain multi-source operating status data of construction equipment during task execution; Align the multi-source operation status data according to the unified timestamp and perform normalization processing to generate a multi-source operation status data matrix; During the task execution process, the multi-source operation status data matrix is ​​vectorized to generate a multi-source operation status data vector sequence; A data consistency check is performed on each moment vector of the multi-source operating status data vector sequence and sorted by time to form an operating condition vector sequence.

3. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 2, characterized in that: The method uses each time series vector in the working condition vector sequence as a graph node, establishes a directed graph structure and constructs a weighted adjacency matrix to generate a working condition evolution state map. The specific steps are: The working condition vectors are extracted one by one in chronological order and defined as graph nodes to form a graph node set; Based on the time relationship and state difference between two adjacent graph nodes in the graph node set, the state transition weight is calculated to generate the graph edge set and corresponding weights; Merge the graph node set and the graph edge set to construct a directed graph structure, and map the edge weights in the directed graph to a matrix form according to the node index position to generate a weighted adjacency matrix. The graph node set, graph edge set, directed graph structure and weighted adjacency matrix are uniformly encapsulated to generate a state graph of the working condition evolution of construction equipment during task execution.

4. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 3, characterized in that: The working condition evolution graph and the task feature vector are matched and scored to construct a path task matching function. The specific steps are: According to the specific construction information of the task to be executed and the working condition vector, they are normalized into the task feature vector; Based on the working condition evolution state graph, all path combinations of fixed depth are extracted from the graph node set, and the path set is constructed as the task matching scoring object set; Construct a path task matching function, match and score the task feature vector with the working condition vector corresponding to each node of each path in the task matching scoring object set one by one, and record the task matching score of each path.

5. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 4, characterized in that: The calculation path comprehensive score is used to select the optimal evolution path. The specific steps are: Calculate the path condition change rate based on the state difference between consecutive nodes in each path; Calculate the comprehensive path score of each path based on the task matching score and the path condition change rate; The path with the highest score in the comprehensive path score is selected as the optimal evolution path for task execution.

6. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 5, characterized in that: The specific steps of mapping each node in the optimal evolution path to the actual construction area and constructing the environmental risk correction function are as follows: Each working condition vector node in the optimal evolution path of task execution is mapped to the construction time segment in the task plan in chronological order to form a time mapping sequence of the optimal evolution path of task execution; Obtain environmental factor information and construct a construction environmental factor matrix; According to the mapping relationship between the working condition vector nodes and the environmental factors of the construction environment factor matrix in the same time segment in the optimal evolution path of task execution, an environmental adaptation combination sequence is constructed. Based on the interference degree characteristics of the environmental factors in the environmental adaptation combination sequence, an exponential environmental risk correction function is established to obtain the environmental risk correction values ​​of all nodes in the optimal evolution path.

7. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 6, characterized in that: The path comprehensive score is corrected to obtain the environmentally corrected score. The specific steps are: Arrange the environmental risk correction values ​​of all nodes in the optimal evolutionary path of task execution in path order to obtain the environmental risk correction value sequence of the optimal evolutionary path of task execution; Based on the correspondence between the comprehensive path score of each node in the optimal evolutionary path of task execution and the environmental risk correction value of each node in the optimal evolutionary path, a path environment correction score sequence is formed; Calculate the average of the scores of each node in the path environment-corrected score sequence as the environment-corrected score of the optimal evolutionary path corresponding to the task execution.

8. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 7, characterized in that: The environmental correction score is classified into different levels and the corresponding operation safety level label is output. The specific steps are: The optimal evolutionary path environment-corrected score of task execution is compared with the preset score segmentation interval to determine the score interval of the optimal evolutionary path environment-corrected score and assign a safety level label.

9. A method for evaluating the operation safety of construction equipment for highway maintenance according to claim 8, characterized in that: The specific steps of outputting corresponding control strategy suggestions according to different levels are as follows: Based on the operation safety level label and according to the preset operation control strategy table, the control strategy suggestion that matches the operation safety level label is output.

10. A highway maintenance construction equipment operation safety evaluation system based on a highway maintenance construction equipment operation safety evaluation method according to any one of claims 1 to 9, characterized in that: Including data processing module, graph construction module, path matching module, path scoring module and strategy recommendation module; The data processing module is used to obtain multi-source operating status data of the construction equipment during the task execution process, normalize the multi-source operating status data, and obtain a working condition vector sequence; The graph construction module is used to use each time series vector in the operating condition vector sequence as a graph node, establish a directed graph structure and construct a weighted adjacency matrix to generate an operating condition evolution state graph; The path matching module is used to construct a task feature vector according to the requirements of the task to be executed, match and score the working condition evolution map with the task feature vector, construct a path task matching function, calculate the comprehensive score of the path, and select the optimal evolution path; The path scoring module is used to map each node in the optimal evolution path to the actual construction area, construct an environmental risk correction function, correct the path comprehensive score, and obtain the environmental correction score; The strategy recommendation module is used to classify the environmental correction score according to the preset score segmentation interval, output the corresponding operation safety level label, and output corresponding control strategy recommendations according to different levels.

Images