Urban and rural facility construction project management system and method based on multi-source data

Abstract

The invention discloses an urban and rural facility construction project management system and method based on multi-source data, and relates to the technical field of data processing. The method comprises the steps of collecting data of different types of sensors in a target construction area, obtaining sensor data and uploading the sensor data to an edge computing node corresponding to the target construction area; the sensor data is multi-source data; the edge computing node preprocesses the received sensor data, converts the preprocessed sensor data into a preset format, obtains standard multi-source data and uploads the standard multi-source data to a cloud service platform; the cloud service platform evaluates a performance index of a target construction area based on the standard multi-source data and a preset construction safety evaluation system; the performance index is used for representing safety and environment conditions of the target construction area; according to the technical scheme provided by the invention, a comprehensive and intelligent project management system can be constructed, and the system management efficiency and the accuracy of an evaluation result are improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to an urban and rural facility construction project management system and method based on multi-source data. Background Art

[0002] With the acceleration of the urbanization process, the number of urban and rural facility construction projects is increasing day by day, and the scale is also expanding continuously. These projects cover the construction of public facilities such as residential communities, commercial centers, schools and hospitals, involving a large number of construction activities and complex management processes. The traditional project management mode mainly relies on manual monitoring and paper records, which has problems such as information lag, inaccurate data, and low decision-making efficiency, and it is difficult to meet the requirements of modern engineering construction for safety and efficiency.

[0003] Therefore, how to overcome the above-mentioned existing technical problems and defects has become a key problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide an urban and rural facility construction project management system and method based on multi-source data, so as to solve the problems raised in the above background art, build a comprehensive and intelligent project management system, and improve the system management efficiency and the accuracy of evaluation results.

[0005] The technical solution of the embodiment of this application is implemented as follows:

[0006] The embodiment of this application also provides an urban and rural facility construction project management system based on multi-source data. This system includes a data acquisition unit, a cloud service platform, and an edge computing node;

[0007] The data acquisition unit is used to collect data of different types of sensors in the target construction area, obtain sensor data and upload it to the edge computing node corresponding to the target construction area; the sensor data is multi-source data;

[0008] The edge computing node is used to preprocess the received sensor data, convert the preprocessed sensor data into a preset format, obtain standard multi-source data and upload it to the cloud service platform;

[0009] The cloud service platform is used to evaluate the performance index of the target construction area based on the standard multi-source data and a preset construction safety evaluation system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

[0010] In the above solution, the data acquisition unit includes an environmental data acquisition unit; the environmental data acquisition unit is used to collect the environmental parameters and remote sensing data of the target construction area.

[0011] In the above solution, the data acquisition unit further includes a human body data acquisition unit; the human body data acquisition unit is used to acquire the physiological data of workers in the target construction area.

[0012] An embodiment of the present application also provides a method for managing urban and rural infrastructure construction projects based on multi-source data. The method includes:

[0013] Collect data from different types of sensors in the target construction area, obtain sensor data, and upload it to the edge computing node corresponding to the target construction area; the sensor data is multi-source data;

[0014] The edge computing node preprocesses the received sensor data, converts the preprocessed sensor data into a preset format, obtains standard multi-source data, and uploads it to the cloud service platform;

[0015] The cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and a preset construction safety assessment system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

[0016] In the above solution, the sensor data includes construction environment data; collecting data from different types of sensors in the target construction area, obtaining sensor data, and uploading it to the edge computing node corresponding to the target construction area includes:

[0017] Collect the environmental parameters and remote sensing data of the target construction area to obtain construction environment data;

[0018] Upload the construction environment data to the edge computing node corresponding to the target construction area.

[0019] In the above solution, the sensor data further includes physiological data; collecting data from different types of sensors in the target construction area, obtaining sensor data, and uploading it to the edge computing node corresponding to the target construction area further includes:

[0020] Collect the physiological data of workers in the target construction area;

[0021] Upload the physiological data to the edge computing node corresponding to the target construction area.

[0022] In the above solution, the evaluation indicators of the construction safety assessment system include construction site indicators and construction personnel indicators; the cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and a preset construction safety assessment system, including:

[0023] The cloud service platform quantifies the construction site indicators in the construction safety assessment system based on the construction environment data in the standard multi-source data;

[0024] Quantify the construction personnel indicators in the construction safety assessment system based on the physiological data in the standard multi-source data;

[0025] Calculate the performance index of the target construction area based on the quantification results of the construction site indicators and the construction personnel indicators.

[0026] In the above solution, the quantification formula of the construction site indicator is expressed as:

[0027]

[0028] Among them, Q j,t represents the quantification result of the construction site indicator E j at time t, V ij represents the contribution weight of sensor i to the indicator E j SM i,t represents the standardized data of sensor i at time t;

[0029] The quantification formula of the construction personnel indicator is expressed as:

[0030]

[0031] Among them, R k,t represents the quantification result of the construction personnel indicator E k at time t, V lk represents the contribution weight of sensor l to the indicator E k SM l,t represents the standardized data of sensor l at time t;

[0032] The calculation formula of the performance index is expressed as:

[0033]

[0034] Among them, P t represents the performance index of the construction area at time t, N represents the number of construction site indicators, ω1 represents the overall weight of the construction site indicators, M represents the number of construction personnel indicators, and ω2 represents the medium weight of the construction personnel indicators.

[0035] In the above solution, the method further includes:

[0036] Determine the communication cost between each construction area in at least one construction area and each preset edge node in at least one preset edge node to obtain a cost calculation result;

[0037] Construct a weighted undirected graph based on the cost calculation result; the nodes in the weighted undirected graph represent preset edge nodes or construction areas, and the edges represent the communication cost between the construction area and the preset edge node;

[0038] Using the Floyd-Warshall algorithm, calculate the shortest paths between all node pairs in the weighted undirected graph;

[0039] Determine the shortest path of the target construction area from the shortest paths between all node pairs;

[0040] Take the preset edge nodes corresponding to the shortest path of the target construction area as the edge computing nodes corresponding to the target construction area.

[0041] In the above solution, the edge computing node preprocesses the received sensor data and converts the preprocessed sensor data into a preset format, including:

[0042] The edge computing node performs data cleaning on the received sensor data; the data cleaning includes removing noise and filling in missing values;

[0043] Perform data standardization processing on the cleaned sensor data;

[0044] Convert the data after standardization processing into JSON format.

[0045] The urban and rural facility construction project management system and method based on multi-source data provided by the embodiments of the present application collect multi-source sensor data and convert it into a unified format, providing a unified standard interface for data from different sources, thereby being able to break data islands, achieve seamless docking across departments and systems, and ensure the consistency and integrity of information; further, by introducing the Internet of Things and edge computing technologies, instant transmission and processing of data are realized, problems are quickly discovered and solved, potential risks are reduced. At the same time, since edge nodes are introduced for analysis and data processing work, the data processing burden on the cloud platform can be reduced, and at the same time, the local response speed and the reliability of response results can be improved; by quantitatively evaluating the safety and environmental conditions at the construction site and calculating the performance index, the safety and environmental protection performance of the current construction site can be comprehensively and intuitively characterized, thereby providing data support for subsequent resource allocation and project management work distribution, and further improving the efficiency, scientificity and accuracy of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic structural diagram of a multi-source data-based urban and rural facility construction project management system provided by an embodiment of the present application

[0047] Figure 2 It is a schematic flowchart of a multi-source data-based urban and rural facility construction project management method provided by an embodiment of the present application;

[0048] Figure 3Schematic diagram of the weighted undirected graph in the urban and rural infrastructure construction project management method based on multi-source data in the embodiment of the present application;

[0049] Figure 4 Schematic flowchart of S202 in the urban and rural infrastructure construction project management method based on multi-source data in the embodiment of the present application. Detailed implementation manners

[0050] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0051] Urban and rural infrastructure construction project management refers to a series of activities such as planning, organizing, directing, coordinating, and controlling public infrastructure construction projects in cities and rural areas, which may include the construction of public buildings and service facilities such as roads, bridges, water supply and drainage systems, power supply, communication networks, schools, and hospitals; effective project management is crucial for ensuring the successful completion of urban and rural infrastructure construction.

[0052] In the prior art, there are a large number of ongoing projects in urban and rural infrastructure construction projects. To ensure the monitoring of the construction safety of the construction areas of ongoing projects, various types of sensors can be deployed at the construction sites to collect multi-source data such as environmental parameters, equipment status, and personnel behavior in real time, and the situation of the construction sites can also be analyzed through data such as remote sensing images; however, data from different sources are stored separately in various departments or systems, lacking unified standards and interfaces, resulting in difficulties in data sharing and making it difficult to form a global perspective, thus leading to a serious data island phenomenon in the analysis results of the construction situation; at the same time, in the face of massive and complex data, existing tools can often only provide simple statistical analysis, making it difficult to mine deep information value and affecting the scientificity and accuracy of decision-making.

[0053] Based on this, in various embodiments of the present application, by collecting multi-source sensor data and converting it into a unified format, a unified standard interface is provided for data from different sources, thereby being able to break data islands, achieve seamless docking across departments and systems, and ensure the consistency and integrity of information; further, by introducing Internet of Things and edge computing technologies, instant transmission and processing of data are realized, problems are quickly discovered and solved, and potential risks are reduced. At the same time, since edge nodes are introduced for analysis and data processing work, the data processing burden on the cloud platform can be reduced, and the local response speed and the reliability of response results can be improved simultaneously; by quantitatively evaluating the safety and environmental conditions at the construction site and calculating the performance index, the safety and environmental performance of the current construction site can be comprehensively and intuitively characterized, thereby providing data support for subsequent resource allocation and project management work distribution, and further improving the efficiency, scientificity, and accuracy of project management.

[0054] An application embodiment provides a management system for urban and rural infrastructure construction projects based on multi-source data, as Figure 1 shown, the system includes a data acquisition unit 101, a cloud service platform 102, and an edge computing node 103;

[0055] The data acquisition unit 101 is used to collect data of different types of sensors in the target construction area, obtain the sensor data, and upload it to the edge computing node corresponding to the target construction area; the sensor data is multi-source data.

[0056] The edge computing node 102 is used to preprocess the received sensor data, convert the preprocessed sensor data into a preset format, obtain standard multi-source data, and upload it to the cloud service platform.

[0057] The cloud service platform 103 is used to evaluate the performance index of the target construction area based on the standard multi-source data and a preset construction safety evaluation system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

[0058] In actual application, the cloud service platform 103 can also directly obtain the data of the data acquisition unit 101.

[0059] In one embodiment, the data acquisition unit 101 may include an environmental data acquisition unit; the environmental data acquisition unit is used to collect the environmental parameters and remote sensing data of the target construction area.

[0060] In practical applications, the environmental data acquisition unit may include sensors installed at the construction site for real-time collection of environmental parameters, such as temperature sensors, humidity sensors, light intensity sensors, air quality sensors, etc.; the environmental data acquisition unit may also include sensors mounted on drones, such as remote sensing sensors carried on drones, for collecting remote sensing images of the construction site.

[0061] In one embodiment, the data acquisition unit may further include a human body data acquisition unit; the human body data acquisition unit is used to collect the physiological data of workers in the target construction area.

[0062] In practical applications, the data acquisition unit may adopt intelligent wearable devices. Specifically, it may adopt intelligent bracelets configured with sensor modules capable of collecting human physiological data (such as heart rate, body temperature, etc.).

[0063] In one embodiment, the sensor data includes construction environment data; the data acquisition unit 101 may specifically be used for:

[0064] Collect the environmental parameters and remote sensing data of the target construction area to obtain construction environment data;

[0065] Upload the construction environment data to the edge computing node corresponding to the target construction area.

[0066] In one embodiment, the sensor data may further include physiological data; the data acquisition unit 101 may also be used for:

[0067] Collect the physiological data of workers in the target construction area;

[0068] Upload the physiological data to the edge computing node corresponding to the target construction area.

[0069] In one embodiment, the evaluation indicators of the construction safety assessment system include construction site indicators and construction personnel indicators; the cloud service platform 103 may specifically be used for:

[0070] The cloud service platform quantifies the construction site indicators in the construction safety assessment system based on the construction environment data in the standard multi-source data;

[0071] Quantify the construction personnel indicators in the construction safety assessment system based on the physiological data in the standard multi-source data;

[0072] Calculate the performance index of the target construction area based on the quantification results of the construction site indicators and construction personnel indicators.

[0073] In one embodiment, the quantification formula of the construction site indicators is expressed as:

[0074]

[0075] Among them, Q j,t represents the quantization result of the construction site index E j at time t, V ij represents the contribution weight of sensor i to the index E j and SM i,t represents the standardized data of sensor i at time t;

[0076] The quantization formula of the construction worker index is expressed as:

[0077]

[0078] Among them, R k,t represents the quantization result of the construction worker index E k at time t, V lk represents the contribution weight of sensor l to the index E k and SM l,t represents the standardized data of sensor l at time t;

[0079] The calculation formula of the performance index is expressed as:

[0080]

[0081] Among them, P t represents the performance index of the construction area at time t, N represents the number of construction site indexes, ω1 represents the overall weight of the construction site indexes, M represents the number of construction worker indexes, and ω2 represents the overall weight of the construction worker indexes.

[0082] In an embodiment, the system may further include a node determination unit; the node determination unit is used for:

[0083] Determine the communication cost between each construction area in at least one construction area and each preset edge node in at least one preset edge node to obtain a cost calculation result;

[0084] Based on the cost calculation result, construct an undirected weighted graph; the nodes in the undirected weighted graph represent preset edge nodes or construction areas, and the edges represent the communication costs between construction areas and preset edge nodes;

[0085] Use the Floyd-Warshall algorithm to calculate the shortest path between all node pairs in the undirected weighted graph;

[0086] Determine the shortest path of the target construction area from the shortest paths between all node pairs;

[0087] Take the preset edge node corresponding to the shortest path of the target construction area as the edge computing node corresponding to the target construction area.

[0088] In one embodiment, the edge computing node 102 can specifically be used for:

[0089] The edge computing node performs data cleaning on the received sensor data; the data cleaning includes removing noise and filling missing values;

[0090] Perform data standardization processing on the cleaned sensor data;

[0091] Convert the data after the standardization processing into JSON format.

[0092] Based on the above system architecture, an embodiment of the present application further provides a method for managing urban and rural infrastructure construction projects based on multi-source data, as Figure 2 shown, the method may include S201 to S203. The following will explain S201 to S203 in detail with specific embodiments.

[0093] S201: Collect data from different types of sensors in the target construction area, obtain sensor data, and upload it to the edge computing node corresponding to the target construction area; the sensor data is multi-source data.

[0094] In practical applications, collecting data from different types of sensors in the target construction area may include collecting data from environmental sensors for monitoring the construction site environment.

[0095] Based on this, in one embodiment, the sensor data includes construction environment data; collecting data from different types of sensors in the target construction area, obtaining sensor data, and uploading it to the edge computing node corresponding to the target construction area includes:

[0096] Collect environmental parameters and remote sensing data of the target construction area to obtain construction environment data;

[0097] Upload the construction environment data to the edge computing node corresponding to the target construction area.

[0098] In practical applications, environmental parameters can be collected in real time through environmental sensors set at the construction site, such as environmental sensors like temperature sensors, humidity sensors, light intensity sensors, air quality sensors, etc.; remote sensing images of the construction site can also be collected through remote sensing sensors mounted on unmanned aerial vehicles, such as remote sensing sensors like multispectral cameras, thermal infrared cameras, lidar, etc.

[0099] In actual application, data from different types of sensors in the target construction area are collected, and it may also include collecting sensor data for monitoring the health status of on-site construction workers.

[0100] Based on this, in one embodiment, the sensor data further includes physiological data; collecting data from different types of sensors in the target construction area, obtaining the sensor data and uploading it to the edge computing node corresponding to the target construction area may further include:

[0101] Collecting the physiological data of workers in the target construction area;

[0102] Uploading the physiological data to the edge computing node corresponding to the target construction area.

[0103] In actual application, the physiological data of construction workers can be collected through intelligent wearable devices. Specifically, a smart bracelet configured with a sensor module capable of collecting human physiological data (such as heart rate, body temperature, etc.) can be used to collect physiological data.

[0104] In actual application, before uploading the sensor data to the edge computing node, the edge computing node associated with the target construction area can be determined first.

[0105] Based on this, in one embodiment, the method further includes:

[0106] Determining the communication cost between each construction area in at least one construction area and each preset edge node in at least one preset edge node to obtain a cost calculation result;

[0107] Based on the cost calculation result, constructing a weighted undirected graph; the nodes in the weighted undirected graph represent preset edge nodes or construction areas, and the edges represent the communication cost between the construction area and the preset edge node;

[0108] Using the Floyd-Warshall algorithm to calculate the shortest path between all node pairs in the weighted undirected graph;

[0109] Determining the shortest path of the target construction area from the shortest paths between all node pairs;

[0110] Taking the preset edge node corresponding to the shortest path of the target construction area as the edge computing node corresponding to the target construction area.

[0111] In actual application, determining the communication cost between each construction area in at least one construction area and each preset edge node in at least one preset edge node can be understood as calculating the initial communication cost from each construction area to each preset edge node according to preset conditions. Here, the preset conditions can be understood as preset factors for calculating the communication cost, such as geographical distance, bandwidth, latency, etc. Which preset conditions are specifically set can be determined according to the actual application scenario, and the embodiments of the present application do not make limitations.

[0112] Exemplarily, geographical distance, available bandwidth, and network latency are used as factors for calculating the initial communication cost in the preset conditions.

[0113] First, define variables and symbols.

[0114] Specifically, define the set of construction areas as V R , V R which contains m construction areas; define the set of preset edge nodes as V N , V N which contains n preset edge nodes; define C r,n as the initial communication cost from construction area r to preset edge node n, where r ∈ V R , n ∈ V N ; define D[i][j] as the initial matrix of the shortest path length from node i to node j, and define P[i][j] as the matrix of the predecessor nodes on the shortest path from node i to node j. Here, the matrix of predecessor nodes is used to record path information.

[0115] Then, calculate the initial communication cost from each construction area to each preset edge node. Specifically, use the following formula to calculate the initial communication cost:

[0116] C rn = ω1·d(r,n) + ω2·b(r,n) + ω3·l(r,n);

[0117] where d(r,n) represents the geographical distance, b(r,n) represents the available bandwidth, l(r,n) represents the network latency, and ω1, ω2, and ω3 respectively represent the weights of each factor.

[0118] In actual application, when constructing a weighted undirected graph based on the cost calculation result, all construction areas and preset edge nodes can be regarded as nodes in the graph. Among them, the nodes representing construction areas are regarded as area nodes, the nodes representing preset edge nodes are regarded as calculation nodes, and each edge represents the cost from the area node to the calculation node. Construct a weighted undirected graph containing all area nodes and all calculation nodes, and then obtain an initialized distance matrix.

[0119] Specifically, a weighted undirected graph G including m construction areas and n preset edge nodes is constructed, G = (V, E), where V = V R ∪V N , E represents the set of edges, and then an initial distance matrix is constructed using the weighted undirected graph to obtain the initial matrix of the shortest path lengths; Exemplarily, there are currently two construction areas R1 and R2 in the area to be detected, and three preset edge nodes N1, N2, and N3 are configured. The constructed weighted undirected graph is as shown in Figure 3 . Among them, the number on each edge represents the cost of that path.

[0120] The distance matrix is initialized using the following formula:

[0121]

[0122] That is, during initialization, if there is a direct connection between two nodes, the result is the corresponding communication cost, otherwise it is set to ∞; for the path from a node to itself, the initialization result is 0.

[0123] In practical applications, after obtaining the initialized distance matrix, the Floyd-Warshall algorithm can be used to find the shortest paths between all node pairs by iteratively updating the initialized distance matrix.

[0124] In practical applications, calculating the shortest paths between all node pairs of the weighted undirected graph can be understood as taking a computing node and a regional node as a node pair, and then calculating the shortest paths between all node pairs in the weighted undirected graph.

[0125] In practical applications, determining the shortest path of the target construction area from the shortest paths between all node pairs can be understood as determining the node pairs whose construction area is the target construction area from all node pairs, and then taking the corresponding shortest path as the shortest path of the target construction area, and taking the preset edge node corresponding to this shortest path as the edge computing node corresponding to the target construction area.

[0126] Here, since the Floyd-Warshall algorithm is used to determine the edge computing node, it is possible to take into account the actual physical and logical connections. Compared with simply using the geographical location to determine the matching edge node, multiple weight parameters (such as transmission delay, bandwidth, security) can be introduced when constructing the graph model, so as to achieve multi-dimensional optimization and improve the reliability of the edge computing node determination result; Further, since the Floyd-Warshall algorithm calculates the shortest paths between all nodes, backup paths can be pre-planned and quickly switched when the main path fails, enhancing the reliability and fault tolerance of the system and ensuring the reliability and timeliness of the information feedback at the construction site.

[0127] S202: The edge computing node preprocesses the received sensor data, converts the preprocessed sensor data into a preset format, obtains standard multi-source data and uploads it to the cloud service platform.

[0128] In one embodiment, if Figure 4 As shown, the edge computing node preprocesses the received sensor data and converts the preprocessed sensor data into a preset format, that is, S202, which may specifically include:

[0129] S401: The edge computing node performs data cleaning on the received sensor data; the data cleaning includes removing noise and filling missing values;

[0130] S402: performing data standardization processing on the cleaned sensor data;

[0131] S403: Convert the standardized data into JSON format.

[0132] In practical applications, when removing noise, it is possible to remove outliers or data points that are beyond a reasonable range; for example, for PM2.5 concentration, if the data at a certain moment is much higher than the data at other moments, it may be due to noise caused by sensor failure or other reasons, and the data at that moment needs to be removed; when filling missing values, interpolation and other methods can be used to fill in the missing data points. For example, linear interpolation can be used for filling.

[0133] In practical applications, the data can be standardized using methods such as Z-score, so that data of different dimensions can be converted to the same scale.

[0134] S203: The cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and the preset construction safety assessment system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

[0135] In one embodiment, the evaluation indicators of the construction safety assessment system include construction site indicators and construction personnel indicators; the cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and the preset construction safety assessment system, including:

[0136] The cloud service platform quantifies the construction site indicators in the construction safety assessment system based on the construction environment data in the standard multi-source data;

[0137] quantifying the construction personnel indicators in the construction safety assessment system based on the physiological data in the standard multi-source data;

[0138] Calculate the performance index of the target construction area based on the quantification results of the construction site indicators and construction personnel indicators.

[0139] In practical applications, construction site indicators and construction personnel indicators can be pre-configured to build a construction safety assessment system; here, the construction site indicators and construction personnel indicators can be configured according to the types of sensors deployed at the construction site; for example, sensors used to monitor the environment at the construction site include quality control sensors, noise meters, and remote sensing sensors, and the construction site indicators can be configured as air quality indicators, noise level indicators, and air pollutant distribution indices that can be determined based on remote sensing images; sensors used to monitor the physical conditions of construction personnel at the construction site include heart rate monitors and temperature monitors, and the construction personnel indicators can be configured as heart rate indicators, body temperature indicators, and the wearing rate of smart wearable devices.

[0140] Here, due to the systematic integration and quantification of standard multi-source data from multiple sensors, it is possible to accurately evaluate the environmental conditions of the construction site (such as air quality, noise level) and monitor the health and safety indicators of on-site construction personnel (such as heart rate, wearing of protective equipment), thereby ensuring comprehensive and reliable monitoring of the construction environment and the safety of construction personnel, enabling comprehensive and accurate identification of potential risks in the construction area, taking preventive measures in a timely manner, significantly improving the management level and safety of the project, and ensuring the smooth progress of the project and compliance with high-standard safety requirements.

[0141] In one embodiment, the quantification formula for the construction site indicators can be expressed as:

[0142]

[0143] where Q j,t represents the quantification result of indicator E j at time t, V ij represents the contribution weight of sensor i to indicator E j and SM i,t represents the standardized data of sensor i at time t;

[0144] The quantification formula for the construction personnel indicators is expressed as:

[0145]

[0146] where R k,t represents the quantification result of indicator E k at time t, V lk represents the contribution weight of sensor l to indicator E k and SM l,t represents the standardized data of sensor l at time t;

[0147] The calculation formula of the performance index is expressed as:

[0148]

[0149] where P t represents the performance index of the construction area at time t, N represents the number of construction site indicators, ω1 represents the overall weight of the construction site indicators, M represents the number of construction personnel indicators, and ω2 represents the overall weight of the construction personnel indicators.

[0150] In summary, the urban and rural facility construction project management method based on multi-source data provided by the embodiments of the present application collects multi-source sensor data, converts it into a unified format, and provides a unified standard interface for data from different sources, thereby being able to break data islands, achieve seamless docking across departments and systems, and ensure the consistency and integrity of information; further, by introducing the Internet of Things and edge computing technologies, real-time transmission and processing of data are achieved, problems are quickly discovered and solved, potential risks are reduced. At the same time, since edge nodes are introduced for analysis and data processing work, the data processing burden on the cloud platform can be reduced, and the local response speed and the reliability of response results can be improved simultaneously; by quantitatively evaluating the safety and environmental conditions at the construction site and calculating the performance index, the safety and environmental protection performance of the current construction site can be comprehensively and intuitively characterized, thereby providing data support for subsequent resource allocation and project management work distribution, and further improving the efficiency, scientificity, and accuracy of project management.

[0151] It should be noted that when the urban and rural facility construction project management system based on multi-source data provided in the above embodiments conducts urban and rural facility construction project management based on multi-source data, only the above division of each program module is used for illustration. In actual application, the above processing can be allocated to different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the above-described processing. In addition, the urban and rural facility construction project management system based on multi-source data provided in the above embodiments and the embodiments of the urban and rural facility construction project management method based on multi-source data belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0152] It should be noted that "first", "second", etc. are used to distinguish similar objects and do not have to be used to describe a specific order or sequence.

[0153] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0154] The above is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A management system for urban and rural infrastructure construction projects based on multi-source data, characterized in that: The system includes a data acquisition unit, a cloud service platform and an edge computing node; The data acquisition unit is used to collect data from different types of sensors in the target construction area, obtain sensor data and upload it to the edge computing node corresponding to the target construction area; the sensor data is multi-source data; The edge computing node is used to preprocess the received sensor data, convert the preprocessed sensor data into a preset format, obtain standard multi-source data and upload it to the cloud service platform; The cloud service platform is used to evaluate the performance index of the target construction area based on the standard multi-source data and the preset construction safety assessment system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

2. The system according to claim 1, characterized in that The data acquisition unit includes an environmental data acquisition unit; the environmental data acquisition unit is used to collect environmental parameters and remote sensing data of the target construction area.

3. The system according to claim 2, characterized in that The data collection unit also includes a human body data collection unit; the human body data collection unit is used to collect physiological data of workers in the target construction area.

4. A method for managing urban and rural infrastructure construction projects based on multi-source data, characterized in that: The method comprises: Collect data from different types of sensors in the target construction area, obtain sensor data and upload it to an edge computing node corresponding to the target construction area; the sensor data is multi-source data; The edge computing node preprocesses the received sensor data, converts the preprocessed sensor data into a preset format, obtains standard multi-source data and uploads it to the cloud service platform; The cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and the preset construction safety assessment system; the performance index is used to characterize the safety and environmental conditions of the target construction area.

5. The method according to claim 4, characterized in that The sensor data includes construction environment data; the data of different types of sensors in the target construction area are collected, the sensor data is obtained and uploaded to the edge computing node corresponding to the target construction area, including: Collect environmental parameters and remote sensing data of the target construction area to obtain construction environment data; The construction environment data is uploaded to an edge computing node corresponding to the target construction area.

6. The method according to claim 5, characterized in that The sensor data also includes physiological data; the data of different types of sensors in the target construction area are collected, the sensor data is obtained and uploaded to the edge computing node corresponding to the target construction area, and further includes: Collect physiological data of workers in the target construction area; The physiological data is uploaded to an edge computing node corresponding to the target construction area.

7. The method according to claim 6, characterized in that The evaluation indicators of the construction safety assessment system include construction site indicators and construction personnel indicators; the cloud service platform evaluates the performance index of the target construction area based on the standard multi-source data and the preset construction safety assessment system, including: The cloud service platform quantifies the construction site indicators in the construction safety assessment system based on the construction environment data in the standard multi-source data, and quantifies the construction personnel indicators in the construction safety assessment system based on the physiological data in the standard multi-source data; Based on the quantified results of the construction site index and the construction personnel index, a performance index of the target construction area is calculated.

8. The method according to claim 7, characterized in that The quantitative formula of the construction site index is expressed as: Among them, Q j,t Indicates the construction site index E j The quantization result at time t, V ij Indicates sensor i for index E j Contribution weight, SM i,t represents the normalized data of sensor i at time t; The quantitative formula of the construction personnel index is expressed as: Among them, R k,t Indicates the construction personnel index E k The quantization result at time t, V lk Indicates the sensor l for index E k Contribution weight, SM l,t represents the normalized data of sensor l at time t; The calculation formula of the performance index is expressed as: Among them, P t represents the performance index of the construction area at time t, N represents the number of construction site indicators, ω1 represents the overall weight of the construction site indicators, M represents the number of construction personnel indicators, and ω2 represents the median weight of the construction personnel indicators.

9. The method according to claim 6, characterized in that The method further comprises: Determine the communication cost between each construction area in at least one construction area and each preset edge node in at least one preset edge node to obtain a cost calculation result; Based on the cost calculation result, a weighted undirected graph is constructed; the nodes in the weighted undirected graph represent preset edge nodes or construction areas, and the edges represent the communication costs between the construction areas and the preset edge nodes; Using the Floyd-Warshall algorithm, the shortest paths between all pairs of nodes in the weighted undirected graph are calculated; Determine the shortest path to the target construction area from the shortest paths between all node pairs; The preset edge node corresponding to the shortest path of the target construction area is used as the edge computing node corresponding to the target construction area.

10. The method according to claim 4, characterized in that The edge computing node preprocesses the received sensor data and converts the preprocessed sensor data into a preset format, including: The edge computing node performs data cleaning on the received sensor data; the data cleaning includes removing noise and filling missing values; Perform data standardization on the cleaned sensor data; Convert the standardized data into JSON format.

Images