A smart monitoring system and method for highway maintenance work areas

Abstract

This invention provides an intelligent monitoring system and method for highway maintenance work areas, belonging to the field of highway maintenance operation technology. One method for intelligent monitoring of highway maintenance work areas includes: collecting historical deployment data and extracting deployment features based on type to form type deployment feature data; acquiring current deployment data of the target work area and performing position deviation analysis based on the type deployment feature data to form deployment position early warning data; performing direction deviation analysis based on the deployment position early warning data and the type deployment feature data to form deployment direction early warning data; and combining the deployment position early warning data and the deployment direction early warning data to form current work deployment early warning data. This method achieves more efficient and accurate effective monitoring of highway maintenance work areas through reasonable data processing and analysis.

Description

Technical Field

[0001] This invention relates to the field of highway maintenance operation technology, and in particular to an intelligent monitoring system and method for highway maintenance operation areas. Background Technology

[0002] Currently, highway maintenance operations require the pre-development of detailed work area enclosure (channelization) plans. However, during on-site implementation, construction workers often fail to strictly adhere to the design plan when setting up safety facilities (such as cones and signs). Existing supervision methods mainly rely on manual inspections and measurements. Inspectors must meticulously verify and measure the location, spacing, and quantity of on-site facilities, resulting in low work efficiency and difficulty in identifying non-standard installations in real time, posing significant safety hazards.

[0003] Currently, there are also methods that use intelligent security facilities to remotely locate and assess their positions. While this improves the efficiency and quality of supervision to some extent, it still requires manual analysis to draw conclusions, and the problem of human factors has not been completely eliminated.

[0004] Therefore, designing a path planning method for optimizing the energy consumption of AGV clusters, and achieving more efficient and accurate supervision of highway maintenance work areas through reasonable data processing and analysis, is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides an intelligent monitoring system and method for highway maintenance work areas.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for intelligent monitoring of highway maintenance work areas is provided. The method includes: collecting historical deployment data, extracting deployment features based on type to form type deployment feature data; acquiring current deployment data of the target work area, performing position deviation analysis in conjunction with the type deployment feature data to form deployment position early warning data; performing directional deviation analysis based on the deployment position early warning data and the type deployment feature data to form deployment direction early warning data; and combining the deployment position early warning data and the deployment direction early warning data to form current work deployment early warning data.

[0007] Therefore, the above method extracts permissible deviation characteristics, including installation location and direction, from historical data for different types of deployed safety facilities. This generates deployment characteristic data for different types of facilities, enabling compliance assessments of the deployment location and direction of intelligent safety facilities within the current work area, and efficiently and accurately generating early warning data. Compared to traditional safety facility deployment methods, this method further improves the management efficiency of deployed safety facilities and provides a more accurate and efficient method for judging the correctness and compliance of intelligent safety facility deployment, thus further enhancing the quality of safety facility deployment.

[0008] Optionally, historical deployment data is collected, and deployment feature extraction based on type is performed to form type deployment feature data. This includes: extracting the actual deployment information and planned deployment information of each intelligent safety facility in different operation projects based on historical deployment data to form corresponding object facility historical deployment data; for different intelligent safety facilities of the same type, aggregating the object facility historical deployment data corresponding to different intelligent safety facilities to form object type historical deployment datasets; and performing deployment feature extraction on different object type historical deployment datasets to form corresponding type deployment feature data.

[0009] Therefore, when extracting deployment features based on historical deployment data, two main aspects need to be considered. Firstly, different safety facilities have different requirements and regulations for deployment, necessitating classification to avoid unrepresentative features. Secondly, regarding the content of feature extraction, since safety facilities primarily rely on intelligent devices to determine their precise location as designed, it's necessary to obtain the actual deployment location of the safety facilities from historical data before feature extraction, comparing it to the planned location. This deviation is then considered to extract features, providing an accurate assessment of whether subsequent safety facility deployments meet design requirements. Since historical data includes different operational projects, each involving different types of safety facilities, and multiple sets of data for each type, clustering the historical deployment data according to type can create a foundational big data set for that type of safety facility.

[0010] Optionally, deployment features are extracted from historical deployment datasets of different object types to form corresponding type deployment feature data. This includes: performing feature extraction based on location deviation from historical deployment datasets of different object types to form corresponding type location deployment feature data; performing feature extraction based on direction deviation from historical deployment datasets of intelligent security facilities of the identification type to form corresponding type direction deployment feature data; determining the corresponding type location deployment feature data as the corresponding type deployment feature data for historical deployment datasets of intelligent security facilities of the non-identification type; and combining the corresponding type location deployment feature data and type direction deployment feature data to form the corresponding type deployment feature data for historical deployment datasets of intelligent security facilities of the identification type.

[0011] Therefore, it's understandable that different types of safety facilities require different considerations during deployment, focusing on two key aspects that largely determine whether intelligent safety facilities are safe, correct, and compliant. For safety equipment that merely defines a defined area, the primary consideration is its correct location to prevent potential safety incidents caused by exceeding reasonable boundaries. For safety facilities with an identification function, both the rationality of their location and the recognizability of the markings must be considered. Therefore, their features need to take into account both location and marking orientation. Consequently, feature extraction requires first determining the type of safety facility to perform corresponding feature analysis.

[0012] Optionally, feature extraction based on location deviation is performed on historical deployment datasets of different object types to form corresponding type location deployment feature data, including: for each intelligent security facility in the historical deployment data of different object types, determining the deployment deviation distance between the actual deployment location and the planned deployment location, and the boundary distance difference between the distance from the actual deployment location to the area boundary and the distance from the planned deployment location to the area boundary, based on the actual deployment information and the planned deployment information; for each intelligent security facility in the historical deployment data of different object types, determining the corresponding type object location deviation degree based on the corresponding deployment deviation distance and boundary distance difference, wherein the type object location deviation degree is the ratio of the deployment deviation distance to the boundary distance difference; and forming a corresponding type location deviation degree range based on the type object location deviation degree corresponding to different intelligent security facilities in the historical deployment data of different object types.

[0013] Therefore, for all types of intelligent safety facilities, the location information of their deployment is the most important characteristic information for determining whether they are correctly and reasonably installed. The characteristic information of the location includes two aspects: first, how much the actual installation location deviates from the planned installation location. The degree of deviation determines whether the actual installation location meets the specifications. Second, the relative relationship between the installation location and the work boundary used for dividing and warning work areas using the installation of intelligent safety facilities. After all, the deviation distance between the actual location and the planned location only indicates the degree of deviation, but the relative relationship with the boundary further indicates whether the degree of location deviation affects the identification of the work boundary. For example, if the actual location deviates from the planned location within the allowable range but is far from the planned work boundary, it can cause errors in boundary area identification. This is similar to a car's rearview mirror; although it deviates from the planned location within the allowable range, its distance from the planned work boundary prevents the driver from immediately noticing its location. Conversely, the work boundary in the rearview mirror might be obscured by the surrounding complex environment, making it difficult to locate immediately, especially if there is dense vegetation on one side of the work boundary. This involves considering deveining the limited boundary area. Considering the correlation between these two feature parameters, the ratio of the two data points is used as the representation parameter for location features. Note that the deployment deviation distance is a parameter with a positive or negative relationship, while the boundary distance difference is a relative distance value, as its main function is to quantify the degree of deviation from the boundary. Different individual intelligent devices will obtain different parameter values. Since the actual installation location has continuity in the coverage of feature parameters, the feature parameter range is formed by accumulating these values ​​into a set range. It should also be noted that the historical data obtained are all confirmed to be reasonable and compliant with the plan, even if there are deviations. This is to provide accurate big data and thus form accurate and referable feature information.

[0014] Optionally, feature extraction based on directional deviation is performed on the historical deployment dataset of intelligent security facilities of the identifier type to form corresponding type directional deployment feature data. This includes: obtaining the actual deployment direction information and planned deployment direction information of different intelligent security facilities in the historical deployment dataset of different object types of the identifier type; determining the actual warning area range for different intelligent security facilities in the historical deployment dataset of object types based on the corresponding actual deployment information and actual deployment direction information; determining the planned warning area range for different intelligent security facilities in the historical deployment dataset of object types based on the corresponding planned deployment information and planned deployment direction information; determining the corresponding type object directional overlap area for different intelligent security facilities in the historical deployment dataset of object types based on the actual warning area range and planned warning area range; and forming the corresponding type directional overlap area range for the historical deployment dataset of object types based on the type object directional overlap area corresponding to different intelligent security facilities.

[0015] Therefore, for intelligent safety facilities with signage and indication functions, such as signs and lighting indicators, it is necessary to consider not only whether their installation location is within a reasonable range, but also whether they are in the correct directional direction. Since the directional direction has a certain area coverage, in general, the actual installation only needs to ensure that the directional direction is within a reasonable area. This reasonableness is reflected in the deviation from the planned directional range and the deviation from the planned directional distance. After all, for a given sign, its location is determined, and there is a certain viewing distance due to lighting or human visual perception. Considering these two aspects essentially defines a fan-shaped directional area. Therefore, this application uses location information to determine the degree of overlap between the planned and actual fan-shaped area to characterize whether the actual installation orientation meets the requirements. Similarly, this parameter has continuity within a range; therefore, by aggregating the overlap area values ​​of each intelligent safety facility in the dataset, a reference feature range data is formed.

[0016] Optionally, the current deployment data of the target work area is obtained, and position deviation analysis is performed in combination with type deployment characteristic data to form deployment position early warning data. This includes: extracting the current planned deployment data and the current actual deployment data of the target work area based on the current deployment data; determining the current planned deployment information of different intelligent safety facilities based on the current planned deployment data; determining the current actual deployment information of different intelligent safety facilities based on the current actual deployment data; performing position deviation analysis on different intelligent safety facilities based on the current planned deployment information and the current actual deployment information, and in combination with the type deployment characteristic data corresponding to the intelligent safety facilities, to form corresponding object deployment position early warning information; and aggregating the object deployment position early warning information corresponding to different intelligent safety facilities to form deployment position early warning data.

[0017] Therefore, after obtaining the deployment characteristic data of different types of intelligent security facilities, the characteristic data can be used to verify each intelligent security facility installed in the current project, efficiently and accurately determining whether its installation complies with specifications and requirements. Of course, to verify the installation standardization and correctness of the current intelligent security facilities, it is necessary to first obtain the planned installation data and actual installation data for the current project. Considering that the most basic requirement for different types of intelligent security facilities is the correctness and standardization of their location points, a positional deviation analysis is performed first. This ensures that the subsequent analysis of signage-type intelligent security facilities is completed more efficiently. After all, the positional deviation analysis allows for the elimination of signage-type intelligent security facilities with incorrect installation locations, avoiding repetitive analysis of subsequent orientations.

[0018] Optionally, for different intelligent security facilities, based on the current planned deployment information and the current actual deployment information, and combined with the deployment characteristic data of the corresponding intelligent security facilities, position deviation analysis is performed to form corresponding object deployment position early warning information, including: for different intelligent security facilities, based on the corresponding current planned deployment information and the current actual deployment information, determining the corresponding type object position deviation degree, and marking it as the type object current position deviation degree; extracting the type position deviation degree range from the type deployment characteristic data of the corresponding type according to the type of intelligent security facility; if the type object current position deviation degree belongs to the type position deviation degree range, then type object position specification information is formed; if the type object current position deviation degree does not belong to the type position deviation degree range, then type object position warning information is formed.

[0019] Therefore, the analysis of location deviation mainly involves determining whether the location deviation of the type of object corresponding to the installation location of the smart safety setting is within the type location deviation range determined in the deployment feature data of the corresponding type. If it is within the range, it means that the smart safety facility is in an allowed and compliant installation location. If it is not within the range, it means that the installation location of the smart installation facility is non-compliant, thus generating a warning message.

[0020] Optionally, based on the deployment location early warning data and combined with the type deployment characteristic data, directional deviation analysis is performed to form deployment direction early warning data, including: for intelligent safety facilities whose location deviation analysis results are type object location specification information and belong to the identification category, the corresponding type direction overlap area range is extracted according to the type deployment characteristic data of the corresponding type; for different intelligent safety facilities belonging to the identification category, directional deviation analysis is performed according to the corresponding type direction overlap area range, combined with the current planned deployment information and the current actual deployment information, to form deployment direction early warning data.

[0021] Therefore, the condition for directional deviation analysis is that the intelligent safety facility belongs to the type of object with an identification function. Thus, when conducting the analysis, it is necessary to first determine whether the intelligent safety facility belongs to the type of object with an identification function. In addition, it is also necessary to determine whether the intelligent safety facility meets the requirements and specifications in its installation location. Only when both conditions are met can directional analysis be carried out to obtain the final correct result on whether the intelligent safety facility is installed in accordance with the specifications and requirements.

[0022] Optionally, for different intelligent security facilities belonging to the identification category, based on the overlapping area range of the corresponding type direction, and combined with the current planned deployment information and the current actual deployment information, a directional deviation analysis is performed to generate deployment direction early warning data. This includes: determining the corresponding type object directional overlapping area based on the current planned deployment information and the current actual deployment information of the intelligent security facility, and marking it as the type object's current directional overlapping area; if the type object's current directional overlapping area falls within the type direction overlapping area range, then type object directional standard information is generated; if the type object's current directional overlapping area does not fall within the type direction overlapping area range, then type object directional warning information is generated.

[0023] Therefore, directional deviation analysis first requires determining the size of the overlapping area based on the extracted actual and planned deployment information. Only then can the determined parameter values ​​be quantitatively compared with the overlapping area range determined by the corresponding feature data to determine whether the directional installation is reasonable and compliant.

[0024] Secondly, an intelligent monitoring system for highway maintenance work areas is provided. The system is configured to perform the method described in the first aspect, including: a data acquisition unit for acquiring historical deployment data and current deployment data of the target work area; a feature extraction unit for extracting type-based deployment features from the historical deployment data acquired by the data acquisition unit to form type deployment feature data; and an early warning analysis unit for using the type deployment feature data formed by the feature extraction unit to perform position deviation analysis and direction deviation analysis on the current deployment data of the target work area acquired by the data acquisition unit to form deployment position early warning data and deployment direction early warning data, thereby forming current work deployment early warning data.

[0025] Therefore, it can be seen that the system, through the combination of different functional units, forms an overall system that can realize intelligent supervision of highway maintenance operation areas. The data acquisition unit acquires basic big data for feature extraction and current deployment data that needs to be analyzed. The feature extraction unit uses the acquired big data to form feature parameter data to assist in the analysis of deployment compliance. Then, combined with the early warning analysis unit, it can make efficient and accurate compliance judgments on the current deployment status. This can further reduce the difficulty of operation functions and labor costs, and also improve the ability and quality of supervision. It is an important material basis for achieving efficient and accurate supervision. Attached Figure Description

[0026] Figure 1 This invention provides an architectural diagram of an intelligent monitoring system for highway maintenance work areas. Figure 2 A flowchart illustrating an intelligent monitoring method for highway maintenance work areas provided in an embodiment of the present invention; Figure 3 A schematic diagram of the feature extraction process for an intelligent monitoring method for highway maintenance work areas provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an intelligent monitoring system for highway maintenance work areas provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0028] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0029] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0030] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0031] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or processing device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or processing device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0032] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0033] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0034] To facilitate understanding of the embodiments of the present invention, firstly, let's take... Figure 1 The intelligent monitoring and management system for highway maintenance work areas shown in the figure is used as an example to illustrate the intelligent monitoring and management method for highway maintenance work areas applicable to embodiments of the present invention.

[0035] For example, Figure 1 This is a schematic diagram of the architecture of an intelligent monitoring and management system for highway maintenance work areas, provided as an embodiment of the present invention.

[0036] Figure 2 This is a flowchart illustrating an intelligent monitoring method for highway maintenance work areas, provided as an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of the feature extraction process for an intelligent monitoring method for highway maintenance work areas, provided as an embodiment of the present invention.

[0038] This intelligent monitoring method for highway maintenance work areas is applicable to the above system, and the specific process is as follows: S1. Collect historical deployment data, perform type-based deployment feature extraction, and form type deployment feature data.

[0039] This process involves collecting historical deployment data and extracting deployment features based on the type of deployment to form type deployment feature data. This includes: extracting the actual deployment information and planned deployment information of each intelligent safety facility in different operational projects based on historical deployment data to form corresponding historical deployment data of the target facility; for different intelligent safety facilities of the same type, aggregating the historical deployment data of the target facility corresponding to different intelligent safety facilities to form a historical deployment dataset of the target type; and extracting deployment features from different historical deployment datasets of the target type to form corresponding type deployment feature data.

[0040] When extracting deployment features based on historical deployment data, two main aspects are considered. Firstly, different safety facilities have different deployment requirements and regulations, necessitating categorization to avoid unrepresentative features. Secondly, regarding the content of feature extraction, since safety facilities primarily rely on intelligent devices to determine their precise location as designed, it's necessary to obtain the actual deployment location of the safety facilities from historical data before feature extraction, comparing it to the planned location. This deviation is then considered to extract features, providing an accurate assessment of whether subsequent safety facility deployments meet design requirements. Because historical data encompasses different operational projects, each involving different types of safety facilities, and multiple sets of data for each type, clustering the historical deployment data according to type creates a foundational big data set for that type of safety facility.

[0041] For historical deployment datasets of different object types, deployment features are extracted to form corresponding type deployment feature data. This includes: extracting features based on location deviation from historical deployment datasets of different object types to form corresponding type location deployment feature data; extracting features based on direction deviation from historical deployment datasets of intelligent security facilities of the identification type to form corresponding type direction deployment feature data; determining the corresponding type location deployment feature data as the corresponding type deployment feature data for historical deployment datasets of intelligent security facilities of the non-identification type; and combining the corresponding type location deployment feature data and type direction deployment feature data from historical deployment datasets of intelligent security facilities of the identification type to form corresponding type deployment feature data.

[0042] It's understandable that, due to the different types of safety facilities, two key considerations are taken into account during deployment, and these two aspects largely determine whether intelligent safety facilities are safe, correct, and compliant. For safety equipment that only serves to define the location of a defined area, the main consideration is whether it is in the correct position to avoid potential safety incidents caused by exceeding reasonable boundaries. For safety facilities that serve an identification function, both the rationality of their location and the recognizability of the markings must be considered. Therefore, their features need to take into account both location and marking orientation. Consequently, when extracting features, it is necessary to first determine the type of safety facility in order to conduct corresponding feature analysis.

[0043] For historical deployment datasets of different object types, feature extraction based on location deviation is performed to form corresponding type location deployment feature data. This includes: for each intelligent security facility in the historical deployment data of different object types, determining the deployment deviation distance between the actual deployment location and the planned deployment location, and the boundary distance difference between the distance from the actual deployment location to the area boundary and the distance from the planned deployment location to the area boundary, based on the actual deployment information and the planned deployment information; for each intelligent security facility in the historical deployment data of different object types, determining the corresponding type object location deviation degree based on the corresponding deployment deviation distance and boundary distance difference, where the type object location deviation degree is the ratio of the deployment deviation distance to the boundary distance difference; and forming the corresponding type location deviation degree range for different intelligent security facilities based on the type object location deviation degree for different historical deployment data of different object types.

[0044] For all types of intelligent safety facilities, the location information of their deployment is the most important characteristic information for determining whether they are correctly and properly installed. This location information includes two aspects: firstly, the deviation between the actual installation location and the planned installation location. The degree of deviation determines whether the actual installation location meets the specifications. Secondly, the relative position of the installation location to the work boundary used for dividing and warning work areas using the intelligent safety facility. The deviation distance only indicates the degree of deviation, but the relative relationship with the boundary further indicates whether the degree of deviation affects the identification of the work boundary. For example, if the deviation distance between the actual location and the planned location is within the allowable range but far from the planned work boundary, it can cause errors in boundary area identification. This is similar to a rearview mirror on a vehicle; although it deviates from the planned location within the allowable range, it may be too far from the planned work boundary for the driver to notice immediately. Alternatively, the work boundary in the rearview mirror may be too far away and obscured by the surrounding complex environment, making it difficult to locate immediately. This is especially true when there is dense vegetation on one side of the work boundary, which necessitates deveining the limited boundary area. Considering the correlation between these two feature parameters, the ratio of the two data points is used as the characterization parameter for location features. Note that the deployment deviation distance is a parameter with a positive or negative relationship, while the boundary distance difference is a relative distance value, as its main function is to quantify the degree of deviation from the boundary. Different individual smart devices will acquire different parameter values. Since the actual installation locations have continuity within the range covered by the feature parameters, a set range is formed by accumulating these values ​​to create the feature parameter range. It should also be noted that the acquired historical data confirms that although there are deviations from the plan, the installation is still reasonable and compliant. This ensures accurate big data and the formation of accurate and referable feature information.

[0045] Feature extraction based on directional deviation is performed on the historical deployment dataset of intelligent security facilities of the identifier type to form corresponding type directional deployment feature data. This includes: obtaining the actual deployment direction information and planned deployment direction information of different intelligent security facilities in the historical deployment dataset of different object types of the identifier type; determining the actual warning area range for different intelligent security facilities in the historical deployment dataset of object types based on the corresponding actual deployment information and actual deployment direction information; determining the planned warning area range for different intelligent security facilities in the historical deployment dataset of object types based on the corresponding planned deployment information and planned deployment direction information; determining the corresponding type object directional overlap area for different intelligent security facilities in the historical deployment dataset of object types based on the actual warning area range and planned warning area range; and forming the corresponding type directional overlap area range for the historical deployment dataset of object types based on the type object directional overlap area corresponding to different intelligent security facilities.

[0046] For intelligent safety facilities with signage and indication functions, such as signs and illuminated indicators, it is necessary to consider not only whether their installation location is within a reasonable range, but also whether they are in the correct directional direction. Since the directional direction has a certain area coverage, in general, the actual installation only needs to ensure that the directional direction is within a reasonable area. This reasonableness is reflected in the deviation from the planned directional range and the deviation from the planned directional distance. After all, for a given sign, its location is determined, and there is a certain viewing distance due to lighting or human visual perception. Considering these two aspects essentially defines a fan-shaped directional area. Therefore, this application uses location information to determine the degree of overlap between the planned and actual fan-shaped area to characterize whether the actual installation orientation meets the requirements. Similarly, this parameter has continuity within a range; therefore, by aggregating the overlap area values ​​of each intelligent safety facility in the dataset, a referenceable feature range data is formed.

[0047] S2: Obtain the current deployment data of the target operation area, combine it with the type deployment characteristic data to perform position deviation analysis, and generate deployment position early warning data.

[0048] The process involves acquiring current deployment data for the target work area, performing position deviation analysis based on type deployment characteristic data, and generating deployment position early warning data. This includes: extracting the current planned deployment data and the current actual deployment data for the target work area based on the current deployment data; determining the current planned deployment information for different intelligent safety facilities based on the current planned deployment data; determining the current actual deployment information for different intelligent safety facilities based on the current actual deployment data; performing position deviation analysis on different intelligent safety facilities based on the current planned deployment information and the current actual deployment information, combined with the corresponding type deployment characteristic data, to generate corresponding object deployment position early warning information; and aggregating the object deployment position early warning information corresponding to different intelligent safety facilities to form deployment position early warning data.

[0049] After obtaining the deployment characteristic data of different types of intelligent security facilities, the characteristic data can be used to verify each intelligent security facility installed in the current project, efficiently and accurately determining whether its installation complies with specifications and requirements. Of course, to verify the installation standardization and correctness of the current intelligent security facilities, it is necessary to first obtain the planned installation data and actual installation data for the current project. Considering that the most basic requirement for different types of intelligent security facilities is the correctness and standardization of the location points, a position deviation analysis is performed first to ensure that the subsequent analysis of signage-type intelligent security facilities is completed more efficiently. After all, the position deviation analysis can be used to filter out signage-type intelligent security facilities with incorrect installation positions, avoiding repetitive analysis of the location in subsequent steps.

[0050] For different intelligent security facilities, based on the current planned deployment information and the current actual deployment information, and combined with the deployment characteristic data of the corresponding intelligent security facilities, position deviation analysis is performed to generate corresponding object deployment position early warning information. This includes: for different intelligent security facilities, determining the corresponding type object position deviation degree based on the corresponding current planned deployment information and the current actual deployment information, and marking it as the type object current position deviation degree; extracting the type position deviation degree range from the type deployment characteristic data of the corresponding type of intelligent security facility; if the type object current position deviation degree falls within the type position deviation degree range, then type object position specification information is generated; if the type object current position deviation degree does not fall within the type position deviation degree range, then type object position warning information is generated.

[0051] The analysis of location deviation mainly determines whether the location deviation of the type object corresponding to the installation location of the smart safety setting is within the type location deviation range determined in the deployment feature data of the corresponding type. If it is within the range, it means that the smart safety facility is in an allowed and compliant installation location. If it is not within the range, it means that the installation location of the smart installation facility is non-compliant, thus generating a warning message.

[0052] S3. Based on the deployment location warning data and the type deployment characteristic data, directional deviation analysis is performed to generate deployment direction warning data.

[0053] Based on the deployment location early warning data, and combined with the type deployment characteristic data, directional deviation analysis is performed to form deployment direction early warning data. This includes: for intelligent safety facilities whose location deviation analysis results are type object location specification information and belong to the identification category, the corresponding type direction overlap area range is extracted based on the type deployment characteristic data of the corresponding type; for different intelligent safety facilities belonging to the identification category, directional deviation analysis is performed based on the corresponding type direction overlap area range, and combined with the current planned deployment information and the current actual deployment information, to form deployment direction early warning data.

[0054] The condition for directional deviation analysis is that the intelligent safety facility belongs to the type of object with an identification function. Therefore, when conducting the analysis, it is necessary to first determine whether the intelligent safety facility belongs to the type of object with an identification function. In addition, it is also necessary to determine whether the intelligent safety facility meets the requirements and specifications in terms of its installation location. Only when both conditions are met can directional analysis be carried out to obtain the final correct result on whether the intelligent safety facility is installed in accordance with the specifications and requirements.

[0055] For different intelligent safety facilities belonging to the identification category, based on the overlapping area range of the corresponding type direction, and combined with the current planned deployment information and the current actual deployment information, directional deviation analysis is performed to generate deployment direction early warning data. This includes: determining the corresponding type object directional overlapping area based on the current planned deployment information and the current actual deployment information of the intelligent safety facility, and marking it as the type object's current directional overlapping area; if the type object's current directional overlapping area falls within the type direction overlapping area range, then type object directional standard information is generated; if the type object's current directional overlapping area does not fall within the type direction overlapping area range, then type object directional warning information is generated.

[0056] Directional deviation analysis first requires determining the size of the overlapping area based on the extracted actual and planned deployment information. Then, the determined parameter values ​​can be quantitatively compared with the overlapping area range determined by the corresponding feature data to determine whether the directional installation is reasonable and compliant.

[0057] S4 combines deployment location warning data and deployment direction warning data to form current operation deployment warning data.

[0058] Finally, the intelligent safety facilities that provide warning information on the location and direction of the type object are calibrated and output to form the current operation deployment early warning data.

[0059] Therefore, this method extracts permissible deviation characteristics, including installation location and direction, from historical data for different types of deployed safety facilities. This generates deployment characteristic data for different types of facilities, enabling compliance assessments of the deployment location and direction of intelligent safety facilities within the current work area, and efficiently and accurately generating early warning data. Compared to traditional safety facility deployment methods, this approach further improves the management efficiency of deployed safety facilities and provides a more accurate and efficient method for judging the correctness and compliance of intelligent safety facility deployment, thus further enhancing the quality of safety facility deployment.

[0060] Figure 4 This is a schematic diagram of an intelligent monitoring system for highway maintenance work areas provided by an embodiment of the present invention. Exemplarily, the system includes: a data acquisition unit for acquiring historical deployment data and current deployment data of the target work area; a feature extraction unit for extracting type-based deployment features from the historical deployment data acquired by the data acquisition unit to form type deployment feature data; and an early warning analysis unit for using the type deployment feature data formed by the feature extraction unit to perform position deviation analysis and direction deviation analysis on the current deployment data of the target work area acquired by the data acquisition unit, forming deployment position early warning data and deployment direction early warning data, and further forming current work deployment early warning data.

[0061] This system combines different functional units to form an integrated whole that enables intelligent supervision of highway maintenance operation areas. The data acquisition unit acquires basic big data for feature extraction and current deployment data that needs to be analyzed. The feature extraction unit uses the acquired big data to form feature parameter data to assist in the analysis of deployment compliance. Then, combined with the early warning analysis unit, it makes efficient and accurate compliance judgments on the current deployment status. This can further reduce the difficulty of operation functions and labor costs, and also improve the ability and quality of supervision. It is an important material basis for achieving efficient and accurate supervision.

[0062] It should be understood that the processor involved in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0063] It should also be understood that the memory involved in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0065] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0067] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent supervision of a highway maintenance work zone, characterized in that, The method includes: Collect historical deployment data, perform type-based deployment feature extraction, and form type deployment feature data; Acquire the current deployment data of the target operation area, and perform position deviation analysis in combination with the deployment characteristic data of the aforementioned type to generate deployment position early warning data; Based on the deployment location warning data, and combined with the deployment type characteristic data, directional deviation analysis is performed to form deployment direction warning data; By combining the deployment location warning data and the deployment direction warning data, current deployment warning data is generated.

2. The method of claim 1, wherein, The collected historical deployment data is used to extract deployment features based on type, forming type deployment feature data, including: Based on the historical deployment data, the actual deployment information and planned deployment information of each intelligent safety facility in different operation projects are extracted to form the corresponding historical deployment data of the target facilities; For different intelligent security facilities of the same type, the historical deployment data of the object facilities corresponding to the different intelligent security facilities are collected to form an object type historical deployment dataset; For different object types, historical deployment datasets are used to extract deployment features to form corresponding deployment feature data for each type.

3. The method of claim 2, wherein, The step of extracting deployment features from historical deployment datasets of different object types to form corresponding deployment feature data for each type includes: For different types of historical deployment datasets, feature extraction based on location deviation is performed to form corresponding type location deployment feature data; Feature extraction based on directional deviation is performed on the historical deployment dataset of the intelligent security facility of the object type, which is the identification class, to form the corresponding type directional deployment feature data; For the historical deployment dataset of the intelligent security facility of the non-identified type object type, the corresponding type location deployment feature data is determined as the corresponding type deployment feature data; For the historical deployment dataset of the intelligent security facility of the object type of the identification class, the corresponding type location deployment feature data and type direction deployment feature data are combined to form the corresponding type deployment feature data.

4. The method of claim 3, wherein, The step of extracting features based on location deviation from the historical deployment datasets of different object types to form corresponding type location deployment feature data includes: For each intelligent security facility in the historical deployment data of different object types, based on the actual deployment information and the planned deployment information, determine the deployment deviation distance between the actual deployment location and the planned deployment location, as well as the boundary distance difference between the distance from the actual deployment location to the area demarcation boundary and the distance from the planned deployment location to the area demarcation boundary. For each intelligent security facility in the historical deployment data of different object types, the corresponding object type position deviation is determined according to the corresponding deployment deviation distance and boundary distance difference, wherein the object type position deviation is the ratio of deployment deviation distance to boundary distance difference; Based on the historical deployment data of different object types, and according to the location deviation of the object type corresponding to different intelligent security facilities, a corresponding type location deviation range is formed.

5. The method according to claim 4, characterized in that, The step of extracting features based on directional deviation from the historical deployment dataset of the intelligent security facility of the identifier class to form corresponding type directional deployment feature data includes: For the historical deployment datasets of different object types of the intelligent security facilities whose type is an identifier, obtain the actual deployment direction information and planned deployment direction information of different intelligent security facilities; For different intelligent security facilities in the historical deployment dataset of the object type, the actual warning area range is determined based on the corresponding actual deployment information and actual deployment direction information; For different intelligent security facilities in the historical deployment dataset of the object type, the planned deployment area range is determined according to the corresponding planned deployment information and planned deployment direction information; For different intelligent security facilities in the historical deployment dataset of the object type, the corresponding overlapping area of ​​the object type direction is determined according to the actual prompt area range and the planned prompt area range; For the historical deployment dataset of the object type, a corresponding type direction overlap area range is formed based on the overlapping area of ​​the object type corresponding to different intelligent security facilities.

6. The method according to claim 4, characterized in that, The process of acquiring current deployment data for the target work area, combining it with deployment characteristic data of the aforementioned type to perform position deviation analysis, and generating deployment position early warning data includes: Based on the current deployment data of the target work area, extract the current planned deployment data and the current actual deployment data of the target work area; Based on the current planned deployment data, the current planned deployment information for different intelligent security facilities is determined; Based on the current actual deployment data, the current actual deployment information of different intelligent security facilities is determined; For different intelligent security facilities, based on the current planned deployment information and the current actual deployment information, and combined with the deployment characteristic data of the type corresponding to the intelligent security facility, position deviation analysis is performed to form corresponding object deployment position early warning information; The deployment location warning information of the objects corresponding to different intelligent security facilities is collected to form the deployment location warning data.

7. The method according to claim 6, characterized in that, For different intelligent security facilities, based on the current planned deployment information and the current actual deployment information, and combined with the deployment characteristic data of the type corresponding to the intelligent security facility, position deviation analysis is performed to form corresponding object deployment position early warning information, including: For different intelligent security facilities, based on the corresponding current planned deployment information and the current actual deployment information, the corresponding type object position deviation is determined and marked as the type object current position deviation. Based on the type of the intelligent safety facility, extract the type location deviation range from the corresponding type's type deployment feature data; If the current position deviation of the type object is within the range of the type position deviation, then the type object position specification information is formed; If the current position deviation of the type object is not within the range of the type position deviation, a type object position warning message is generated.

8. The method according to claim 7, characterized in that, The step of performing directional deviation analysis based on the deployment location warning data and the deployment type characteristic data to form deployment direction warning data includes: For the intelligent safety facility whose position deviation analysis result is the position specification information of the type object and belongs to the identification class, the corresponding type direction overlap area range is extracted according to the type deployment feature data of the corresponding type. For different intelligent safety facilities belonging to the identification category, based on the overlapping area range of the corresponding type direction, and combined with the current planned deployment information and the current actual deployment information, directional deviation analysis is performed to form the deployment direction early warning data.

9. The method according to claim 8, characterized in that, For different intelligent security facilities belonging to the identification category, based on the overlapping area range of the corresponding type direction, and combined with the current planned deployment information and the current actual deployment information, directional deviation analysis is performed to form the deployment direction early warning data, including: Based on the current planned deployment information and the current actual deployment information corresponding to the intelligent security facility, the overlapping area of ​​the corresponding type object direction is determined and marked as the current overlapping area of ​​the type object direction; If the current overlapping area of ​​the type object belongs to the overlapping area range of the type direction, then the type object direction specification information is formed; If the overlapping area of ​​the current direction of the type object does not fall within the overlapping area range of the type direction, a type object direction warning message is generated.

10. An intelligent monitoring system for highway maintenance work areas, characterized in that, The system is configured to perform the method as described in any one of claims 1-9, comprising: The data acquisition unit is used to acquire historical deployment data and current deployment data for the target operation area; The feature extraction unit is used to perform type-based deployment feature extraction on the historical deployment data acquired by the data acquisition unit to form type deployment feature data. The early warning analysis unit is used to perform position deviation analysis and direction deviation analysis on the current deployment data of the target operation area obtained by the data acquisition unit using the type deployment feature data formed by the feature extraction unit, thereby forming deployment position early warning data and deployment direction early warning data, and then forming current operation deployment early warning data.

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