Urban pipe network informatization method and system based on Internet of Things and GIS
By using an information model that combines the Internet of Things and GIS, the problems of high labor costs and low management efficiency in urban pipeline network management have been solved, enabling efficient and comprehensive pipeline network management and rapid response to abnormal situations in non-standard pipeline networks.
Patent Information
- Application Number
- CN202511074265.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-01
- Publication Date
- 2025-11-21
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing technologies in urban pipeline network management suffer from high labor costs, low management efficiency, and an inability to achieve comprehensive overall management.
An information model based on the Internet of Things (IoT) and GIS is adopted. Real-time operation data of the urban pipeline network is acquired through the IoT and displayed in the GIS model to achieve multimodal management, including safety early warning, drainage planning and flood forecasting, and to assist the expert group in handling abnormal situations of non-standard pipeline networks.
It reduced labor costs, improved management efficiency and comprehensiveness, and enhanced the timeliness of response to abnormal situations in non-standard pipeline networks and the efficiency of expert group decision-making.
Smart Images
Figure CN120996778A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to an information-based method and system for urban pipeline networks based on IoT + GIS. Background Technology
[0002] Currently, the management of urban pipeline networks often requires on-site inspections by staff, which results in high labor costs and low management efficiency. Furthermore, staff cannot comprehensively manage urban pipeline networks from a holistic perspective, reducing the overall scope of management.
[0003] With the further acceleration of urbanization, the scale of urban pipeline networks is expanding rapidly, and traditional urban pipeline network management methods may be unable to meet the demand.
[0004] Therefore, a solution is urgently needed. Summary of the Invention
[0005] One objective of this invention is to provide an information-based method for urban pipeline networks based on the Internet of Things (IoT) and GIS. This method activates the information model of the urban pipeline network, which is a GIS model that continuously displays real-time operational data of the urban pipeline network acquired through the IoT. Based on this information model, multimodal management of the urban pipeline network can be achieved, enabling online remote management without the need for on-site inspections by staff, thus reducing labor costs and improving management efficiency. Secondly, the information model can display the operational status of the urban pipeline network from a global perspective, allowing for comprehensive management of the urban pipeline network and enhancing the overall comprehensiveness of management.
[0006] This invention provides an urban pipeline network informatization method based on the Internet of Things (IoT) and GIS, comprising:
[0007] Activate the information model of the urban pipeline network; the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things.
[0008] Based on an information-based model, urban pipeline networks are managed in a multimodal manner.
[0009] Optionally, based on an information model, multimodal management of the urban pipeline network can be implemented, including:
[0010] Based on information technology models, safety early warnings are provided for urban pipeline networks.
[0011] And / or,
[0012] Based on an information-based model, drainage planning is carried out for urban pipe networks;
[0013] And / or,
[0014] Flood prevention forecasting is performed on urban pipe networks based on information technology models.
[0015] Optionally, the urban pipeline network informatization method based on IoT + GIS also includes:
[0016] When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group is assisted in responding to and handling these anomalies based on the information model.
[0017] Optionally, the auxiliary expert group handles abnormal situations in non-standard pipeline networks based on an information model, including:
[0018] The information model is sliced to obtain sliced models; the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks.
[0019] Integrate the slice model into the expert group's online meeting;
[0020] When at least one expert presents a decision on the slice model during an online meeting, the content already presented on the slice model is optimized in real time.
[0021] Replace the demoed content on the slice model with the real-time optimized demoed content;
[0022] Based on the decision guidance tree, and according to the optimized presentation content, decision guidance is provided to each expert in the online meeting;
[0023] Once the experts have decided on the response and handling strategies during the online meeting, they will conduct online response and handling of non-standard pipeline abnormalities based on these strategies.
[0024] Optionally, the real-time optimization of the demonstrated content on the slice model includes:
[0025] Sort multiple content items in the demonstrated content in chronological order to obtain a content item sequence;
[0026] When there is no first-criteria association between the first and last two content items in the content item sequence, the content item sequence is clustered based on the sequence clustering constraint to obtain at least one content item cluster;
[0027] When the content item clusters are not unique, traverse the i-th content item cluster in the content item sequence in turn; where i varies from N to 2; N is the total number of content item clusters;
[0028] Each time the traversal is performed, the spatiotemporal relationship between the first display area of the content item in the i-th content item cluster on the slice model and the second display area of the content item in the 1-th content item cluster in the content item sequence on the slice model is extracted to obtain multiple feature values.
[0029] Based on each feature value, construct a feature description vector of the relative positional relationship;
[0030] The system queries the timing library for canceling the display to determine the timing of the feature description vector. When the sum of the timing and the historical timing exceeds a threshold, it stops traversing the next content item cluster and cancels the display of the content items in the first content item cluster from the slice model. The historical timing is the product of the timing determined by querying the timing library for canceling the display when traversing other content item clusters in the past and the weight of the corresponding traversal order.
[0031] Otherwise, continue traversing the next content item cluster;
[0032] The sequence clustering constraints include:
[0033] There is at least one second-standard association relationship between any two adjacent content items in the same content item cluster; wherein the association level of the second-standard association relationship is higher than that of the first-standard association relationship.
[0034] as well as,
[0035] The display areas of each content item in the same content item cluster do not overlap on the slice model.
[0036] Optionally, the decision guidance based on the decision guidance tree, according to the optimized presented content, guides the decision-making of each expert in the online meeting, including:
[0037] Analyze the optimized and presented content to determine the decision-making progress;
[0038] Identify the first non-leaf node that matches the decision-making progress from the decision-guiding tree;
[0039] Identify the other multiple second non-leaf nodes between the first non-leaf node and the leaf node on the branch path where the first non-leaf node is located in the decision guidance tree;
[0040] Traverse each second non-leaf node in ascending order of node level;
[0041] During each traversal, based on the second non-leaf node encountered, generate the requirements for the guidance object, the guidance prompts, and the expected guidance effect;
[0042] From among the experts in the online meeting, select the experts whose profiles best match the requirements of the facilitators and use them as the facilitators;
[0043] Based on the guidance prompts, provide corresponding prompts to the guided individuals;
[0044] When the feedback information of the guided object meets the expected guidance effect, continue traversing the next second non-leaf node.
[0045] This invention provides an urban pipeline network information system based on the Internet of Things (IoT) and Geographic Information System (GIS), comprising:
[0046] The activation module is used to activate the information model of the urban pipeline network; the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things.
[0047] The management module is used for multimodal management of urban pipeline networks based on an information model.
[0048] Optionally, the management module, based on an information model, performs multimodal management of the urban pipeline network, including:
[0049] Based on information technology models, safety early warnings are provided for urban pipeline networks.
[0050] And / or,
[0051] Based on an information-based model, drainage planning is carried out for urban pipe networks;
[0052] And / or,
[0053] Flood prevention forecasting is performed on urban pipe networks based on information technology models.
[0054] Optional, the urban pipeline network information system based on IoT + GIS also includes:
[0055] Auxiliary modules are used for:
[0056] When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group is assisted in responding to and handling these anomalies based on the information model.
[0057] Optionally, the auxiliary module assists the expert group in handling abnormal situations in non-standard pipeline networks based on an information model, including:
[0058] The information model is sliced to obtain sliced models; the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks.
[0059] Integrate the slice model into the expert group's online meeting;
[0060] When at least one expert presents a decision on the slice model during an online meeting, the content already presented on the slice model is optimized in real time.
[0061] Replace the demoed content on the slice model with the real-time optimized demoed content;
[0062] Based on the decision guidance tree, and according to the optimized presentation content, decision guidance is provided to each expert in the online meeting;
[0063] Once the experts have decided on the response and handling strategies during the online meeting, they will conduct online response and handling of non-standard pipeline abnormalities based on these strategies.
[0064] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.
[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0066] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0067] Figure 1 This is a schematic diagram of an urban pipeline network informatization method based on Internet of Things + GIS in an embodiment of the present invention;
[0068] Figure 2 This is a schematic diagram of an urban pipeline information system based on Internet of Things (IoT) and GIS in an embodiment of the present invention. Detailed Implementation
[0069] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0070] This invention provides an information-based method for urban pipeline networks based on the Internet of Things (IoT) and GIS, such as... Figure 1 As shown, it includes:
[0071] S1. Activate the information model of the urban pipeline network; whereby the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things.
[0072] S2. Based on the information model, conduct multimodal management of urban pipeline networks.
[0073] Based on GIS technology, a GIS model of the urban pipe network is constructed; real-time operation data of the urban pipe network is obtained based on the Internet of Things, including at least water flow, pressure, temperature, water quality and water level, which can be obtained through cloud platform + IoT sensors deployed at different locations of the urban pipe network; the real-time operation data is mapped into the GIS model to obtain an information model; based on the information model, multimodal management of the urban pipe network is carried out.
[0074] This application activates an information model for the urban pipeline network. This information model is a GIS model that continuously displays real-time operational data of the urban pipeline network acquired based on the Internet of Things. Based on this information model, multimodal management of the urban pipeline network can be carried out, enabling online remote management of the urban pipeline network without the need for on-site inspections by staff, thus reducing labor costs and improving management efficiency. Secondly, the information model can display the operational status of the urban pipeline network from a global perspective, allowing for comprehensive management of the urban pipeline network and improving the comprehensiveness of management.
[0075] In one embodiment, multimodal management of urban pipeline networks is performed based on an information model, including:
[0076] Based on information technology models, safety early warnings are provided for urban pipeline networks.
[0077] And / or,
[0078] Based on an information-based model, drainage planning is carried out for urban pipe networks;
[0079] And / or,
[0080] Flood prevention forecasting is performed on urban pipe networks based on information technology models.
[0081] Multimodal management is divided into three modes: safety early warning, drainage planning, and flood prevention early warning. When conducting safety early warning, information models can be analyzed to determine the operational risks of the urban pipe network and issue warnings to staff. When conducting drainage planning, drainage planning and scheduling can be carried out based on the drainage volume and drainage pressure of different pipes. When conducting flood prevention forecasting, information models can be analyzed in combination with meteorological forecasts and other data to predict flood prevention.
[0082] In one embodiment, the urban pipeline network informatization method based on IoT + GIS further includes:
[0083] S3. When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group will be assisted in responding to and handling the non-standard pipeline network anomalies based on the information model.
[0084] Non-standard pipeline network anomalies refer to unconventional, complex, or highly serious operational anomalies in urban pipeline networks. When the information model identifies non-standard pipeline network anomalies, an expert group needs to intervene to decide how to handle them. The expert group provides assistance during intervention to improve the timeliness of response to non-standard pipeline network anomalies.
[0085] In one embodiment, in S3, the expert group assists in handling abnormal situations in non-standard pipeline networks based on an information model, including:
[0086] S31. Slice the information model to obtain sliced models; wherein, the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks.
[0087] S32. Integrate the slice model into the expert group's online meeting;
[0088] S33. When at least one expert is demonstrating a decision on the slice model in an online meeting, the demonstrated content on the slice model is optimized in real time.
[0089] S34. Replace the previously demonstrated content on the slice model with the real-time optimized previously demonstrated content;
[0090] S35. Based on the decision guidance tree, provide decision guidance to each expert in the online meeting according to the optimized presentation content;
[0091] S36. When the experts have decided on the response and handling strategies in the online meeting, online response and handling shall be carried out based on the response and handling strategies for abnormal situations in non-standard pipeline networks.
[0092] The model slice only displays real-time progress information on non-standard pipeline network anomalies, including at least: real-time anomaly status, impact type, impact range, location of on-site personnel available for dispatch, and activated emergency response plans. After connecting the slice model to the online meeting, each expert in the expert group can view or operate it. During this viewing and operation, discussions will take place on how to handle non-standard pipeline network anomalies. Each expert can demonstrate their handling plan or related suggestions on the slice model, including at least demonstrating how to dispatch on-site personnel and how to control on-site equipment. The demonstration will generate "demonstrated content," which is what is displayed on the slice model. The content generated by expert decision-making demonstrations includes at least personnel scheduling plans and on-site equipment control plans. However, due to the unique nature of non-standard pipeline network anomalies, discussions can be lengthy, and the demonstrated content on the slice model accumulates continuously. Therefore, real-time optimization is necessary to replace the demonstrated content on the slice model with the optimized content. This allows experts to view only the optimized content. Secondly, to further improve the efficiency of expert group discussions, a decision guidance tree is used to guide experts in online meetings based on the optimized demonstrated content. After the decision is made, the response strategy is directly based on the response strategy to address non-standard pipeline network anomalies online.
[0093] In one embodiment, step S33, which involves real-time optimization of the demonstrated content on the slice model, includes:
[0094] S331. Sort multiple content items in the demonstrated content in chronological order to obtain a content item sequence;
[0095] In S331, the demonstrated content contains multiple content items, which are sorted chronologically according to the order in which they were generated, to obtain a sequence of content items.
[0096] S332. When there is no first standard association relationship between the first and last two content items in the content item sequence, perform sequence clustering on the content item sequence based on the sequence clustering constraint to obtain at least one content item cluster.
[0097] In S332, the first standard association relationship is a low-level content association relationship, such as: decision topic association (both decisions are about how to schedule on-site personnel), decision object association (scheduling decisions for the same on-site personnel), decision type association (there is a causal relationship between decision types, such as a previous decision causing a problem and a further decision to solve that problem), etc. After chronological sorting, the first content item in the content item sequence is the earliest generated, and the last content item is the latest generated. If there is no first standard association relationship between the two, it means that there may be content items in the content item sequence that need to be removed from display to avoid excessive content display causing experts to have difficulty viewing and to manually remove the display. Therefore, the next operation is triggered, namely sequence clustering. During sequence clustering, the content item clusters are obtained under the constraints of sequence clustering. The content item clusters contain consecutive content items in the content item sequence, and the first content item falls into the first content item cluster.
[0098] S333. When the content item cluster is not unique, iterate through the i-th content item cluster in the content item sequence in turn; where i changes from N to 2; N is the total number of content item clusters;
[0099] In S333, under the constraint of sequence clustering, if the content item cluster is not unique, it means that at least one content item cluster needs to have all its content items displayed canceled. When canceling the display, the content items in the earliest generated content item cluster are given priority. Therefore, during traversal, i is set to change from N to 2.
[0100] S334. Each time the traversal is performed, the spatiotemporal relationship between the first display area of the content item in the i-th content item cluster on the slice model and the second display area of the content item in the 1st content item cluster in the content item sequence on the slice model is extracted to obtain multiple feature values.
[0101] In S334, each content item will have a display area on the slice model; after extracting the spatiotemporal relationship, the obtained multiple feature values include at least: the distance between the first display area and the second display area, the degree of overlap between the first display area and the second display area, and the number of times the expert operates on the second display area and then immediately operates on the first display area.
[0102] S335. Based on each feature value, construct a feature description vector of the relative positional relationship;
[0103] In S335, feature description vectors are constructed in vector form based on each feature value;
[0104] S336. Query the cancellation display timing degree library to determine the timing degree of the feature description vector. When the sum of the timing degree and the historical timing degree exceeds the threshold, stop traversing the next content item cluster and cancel the display of the content items in the first content item cluster from the slice model. The historical timing degree is the product of the timing degree determined by querying the cancellation display timing degree library when traversing other content item clusters in the past and the weight of the corresponding traversal order.
[0105] In S336, the timing degree of the undo display timing library contains timing degrees corresponding to different feature description vectors. These multiple feature values jointly reflect a situation: the maturity level of the timing for undoing the display of all content items in the first content item cluster. The higher the maturity level, the greater the timing degree corresponding to the constructed feature description vector. For example, multiple feature values such as the distance between the first and second display areas being 15 cm, 20 cm, and 30 cm respectively, the overlap between the first and second display areas being 20% and 60% respectively, and the number of times an expert operates on the second display area followed immediately by operating on the first display area being 0, indicate that the distance between the first and second display areas is relatively large, and the overlap between the first and second display areas (expert...) The second display area, which is newly demonstrated by experts, partially covers the first display area. Since experts no longer operate the second and first display areas sequentially, the timing maturity level is high, corresponding to a timing score of 80. The threshold can be 60. When the sum of the current timing score and the historical timing score exceeds the threshold, traversal stops, and the content items in the first content cluster are removed from the slice model. If no other content clusters have been traversed historically, the historical timing score is 0. If they have been traversed, the timing score is determined during traversal, multiplied by the weight of the traversal order, and the product is the historical timing score. The smaller the traversal order, the more the content clusters composed of newly demonstrated content items by experts are traversed, and the greater the value of the timing maturity level, and the greater the corresponding weight.
[0106] S337. Otherwise, continue traversing the next content item cluster;
[0107] The sequence clustering constraints include:
[0108] There is at least one second-standard association relationship between any two adjacent content items in the same content item cluster; wherein the association level of the second-standard association relationship is higher than that of the first-standard association relationship.
[0109] as well as,
[0110] The display areas of each content item in the same content item cluster do not overlap on the slice model.
[0111] In the above constraints, the second standard association relationship is a content association relationship at a high level of association, such as: decision content association (e.g., multiple processing solutions for a decision direction), decision thinking association, etc.; if there is at least one second standard association relationship between any two adjacent content items in the same content item cluster, and the display areas of each content item in the same content item cluster do not overlap on the slice model, it indicates that each content item in the content cluster is the content generated by experts in the same decision-making stage, and it is necessary to display them together, so they can be merged into a cluster.
[0112] This invention introduces a first standard association relationship, triggering further sequence clustering operations, reducing system workload. During sequence clustering, when content item clusters are not unique, triggering further traversal operations further reduces system workload. The more easily experts can determine the timing maturity reflected by multiple feature values in a content item cluster, the faster i changes from N to 2, allowing for quicker determination of the timing for canceling the display of content items in the first content item cluster, improving the efficiency of timing determination. An cancellation display timing degree library is introduced to quickly determine the timing degree of feature description vectors, improving system efficiency. When the sum of the timing degree and historical timing degree exceeds a threshold, the content items in the first content item cluster are canceled from the slice model, improving the accuracy of content item cancellation display timing. The introduction of sequence clustering constraints ensures that the division of content item clusters can be used to determine whether there are content items that need to be canceled, making it highly intelligent.
[0113] Secondly, when the various technical features of the present invention work together, they can accurately and in real time optimize the demonstrated content on the slice model. Experts can devote themselves to decision-making demonstrations without having to manually select and remove irrelevant content, which greatly improves the decision-making efficiency of experts and the timeliness of responding to abnormal situations in non-standard pipeline networks. At the same time, it is also more humanized and intelligent.
[0114] In one embodiment, step S35, based on the decision guidance tree and the optimized presented content, provides decision guidance to experts in the online meeting, including:
[0115] S351. Analyze the optimized and demonstrated content to determine the decision-making progress;
[0116] S352. Determine the first non-leaf node that matches the decision progress from the decision guidance tree;
[0117] S353. Determine the other multiple second non-leaf nodes between the first non-leaf node and the leaf node on the branch path where the first non-leaf node is located in the decision guidance tree;
[0118] S354. Traverse each second non-leaf node in ascending order of node level;
[0119] S355. During each traversal, based on the second non-leaf node reached, generate the requirements for the guidance object, the guidance prompts, and the expected guidance effect.
[0120] S356. Select the experts whose profiles best match the requirements of the facilitators from among the experts in the online meeting, and use them as the facilitators.
[0121] S357. Based on the guidance prompts, provide appropriate prompts to the guided object;
[0122] S358. When the information reflected after the guidance of the guided object meets the expected guidance effect, continue to traverse the next second non-leaf node.
[0123] The decision guidance tree contains tree nodes, connected to branch paths. Each branch path has multiple non-leaf nodes, and the terminal of each branch path is a leaf node. Each tree node represents a decision objective. Multiple decision progressions that can be implemented to achieve the objective are set on different non-leaf nodes on different branch paths. The order of decision progressions is reflected in the order of the non-leaf nodes. Leaf nodes represent the decision effects after the objective is achieved. The demonstrated content reflects the decision progression, and the decision guidance tree has a first non-leaf node that matches it. Guidance can be based on other decision progressions that follow this first non-leaf node, i.e., based on the second non-leaf node. The higher the node level, the later the decision progression. Therefore, the tree traverses each node in ascending order of level. The second non-leaf node; during each traversal, based on the traversed second non-leaf node, the requirements for the guidance target, the guidance prompts, and the expected guidance effect are generated. The requirements for the guidance target refer to who needs guidance as reflected in the decision-making progress. The guidance prompts refer to how to guide as reflected in the decision-making progress. The expected guidance effect is what kind of reaction the guidance target will produce as reflected in the decision-making progress to achieve the expected result. Experts have personnel profiles, including at least: position, area of expertise, etc. The expert whose personnel profile best matches the requirements for the guidance target is selected from the experts in the online meeting and serves as the guidance target. Based on the guidance prompts, the guidance target is given corresponding prompts. When the guidance target's post-guidance feedback meets the expected guidance effect, the next second non-leaf node is traversed.
[0124] This invention, based on a decision guidance tree, provides rapid and accurate decision guidance to experts in online meetings, greatly improving their decision-making efficiency and the timeliness of responding to abnormal situations in non-standard pipeline networks. It is also more user-friendly and intelligent.
[0125] This invention provides an urban pipeline network information system based on the Internet of Things (IoT) and GIS, such as... Figure 2 As shown, it includes:
[0126] Activation module 1 is used to activate the information model of the urban pipeline network; the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things.
[0127] Management Module 2 is used for multimodal management of urban pipeline networks based on an information model.
[0128] The management module, based on an information model, performs multimodal management of the urban pipeline network, including:
[0129] Based on information technology models, safety early warnings are provided for urban pipeline networks.
[0130] And / or,
[0131] Based on an information-based model, drainage planning is carried out for urban pipe networks;
[0132] And / or,
[0133] Flood prevention forecasting is performed on urban pipe networks based on information technology models.
[0134] The urban pipeline network information system based on IoT + GIS also includes:
[0135] Auxiliary modules are used for:
[0136] When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group is assisted in responding to and handling these anomalies based on the information model.
[0137] The auxiliary module assists the expert group in handling abnormal situations in non-standard pipeline networks based on an information model, including:
[0138] The information model is sliced to obtain sliced models; the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks.
[0139] Integrate the slice model into the expert group's online meeting;
[0140] When at least one expert presents a decision on the slice model during an online meeting, the content already presented on the slice model is optimized in real time.
[0141] Replace the demoed content on the slice model with the real-time optimized demoed content;
[0142] Based on the decision guidance tree, and according to the optimized presentation content, decision guidance is provided to each expert in the online meeting;
[0143] Once the experts have decided on the response and handling strategies during the online meeting, they will conduct online response and handling of non-standard pipeline abnormalities based on these strategies.
[0144] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A method for information management of urban pipeline networks based on the Internet of Things (IoT) and GIS, characterized in that, include: Activate the information model of the urban pipeline network; the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things. Based on an information-based model, urban pipeline networks are managed in a multimodal manner.
2. The urban pipeline network informatization method based on IoT + GIS as described in claim 1, characterized in that, Based on an information-based model, multimodal management of urban pipeline networks is implemented, including: Based on information technology models, safety early warnings are provided for urban pipeline networks. And / or, Based on an information-based model, drainage planning is carried out for urban pipe networks; And / or, Flood prevention forecasting is performed on urban pipe networks based on information technology models.
3. The urban pipeline network informatization method based on IoT + GIS as described in claim 1, characterized in that, Also includes: When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group is assisted in responding to and handling these anomalies based on the information model.
4. The urban pipeline network informatization method based on Internet of Things + GIS as described in claim 3, characterized in that, The expert support team addresses and handles abnormal situations in non-standard pipeline networks based on an information model, including: The information model is sliced to obtain sliced models; the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks. Integrate the slice model into the expert group's online meeting; When at least one expert presents a decision on the slice model during an online meeting, the content already presented on the slice model is optimized in real time. Replace the demoed content on the slice model with the real-time optimized demoed content; Based on the decision guidance tree, and according to the optimized presentation content, decision guidance is provided to each expert in the online meeting; Once the experts have decided on the response and handling strategies during the online meeting, they will conduct online response and handling of non-standard pipeline abnormalities based on these strategies.
5. The urban pipeline network informatization method based on Internet of Things + GIS as described in claim 4, characterized in that, The real-time optimization of the demonstrated content on the slice model includes: Sort multiple content items in the demonstrated content in chronological order to obtain a content item sequence; When there is no first-criteria association between the first and last two content items in the content item sequence, the content item sequence is clustered based on the sequence clustering constraint to obtain at least one content item cluster; When the content item clusters are not unique, traverse the i-th content item cluster in the content item sequence in turn; where i varies from N to 2; N is the total number of content item clusters; Each time the traversal is performed, the spatiotemporal relationship between the first display area of the content item in the i-th content item cluster on the slice model and the second display area of the content item in the 1-th content item cluster in the content item sequence on the slice model is extracted to obtain multiple feature values. Based on each feature value, construct a feature description vector of the relative positional relationship; The system queries the timing library for canceling the display to determine the timing of the feature description vector. When the sum of the timing and the historical timing exceeds a threshold, it stops traversing the next content item cluster and cancels the display of the content items in the first content item cluster from the slice model. The historical timing is the product of the timing determined by querying the timing library for canceling the display when traversing other content item clusters in the past and the weight of the corresponding traversal order. Otherwise, continue traversing the next content item cluster; The sequence clustering constraints include: There is at least one second-standard association relationship between any two adjacent content items in the same content item cluster; wherein the association level of the second-standard association relationship is higher than that of the first-standard association relationship. as well as, The display areas of each content item in the same content item cluster do not overlap on the slice model.
6. The urban pipeline network informatization method based on Internet of Things + GIS as described in claim 4, characterized in that, The decision guidance tree, based on the optimized presented content, guides the decision-making of experts in the online meeting, including: Analyze the optimized and presented content to determine the decision-making progress; Identify the first non-leaf node that matches the decision-making progress from the decision-guiding tree; Identify the other multiple second non-leaf nodes between the first non-leaf node and the leaf node on the branch path where the first non-leaf node is located in the decision guidance tree; Traverse each second non-leaf node in ascending order of node level; During each traversal, based on the second non-leaf node encountered, generate the requirements for the guidance object, the guidance prompts, and the expected guidance effect; From among the experts in the online meeting, select the experts whose profiles best match the requirements of the facilitators and use them as the facilitators; Based on the guidance prompts, provide corresponding prompts to the guided individuals; When the feedback information of the guided object meets the expected guidance effect, continue traversing the next second non-leaf node.
7. An urban pipeline network information system based on Internet of Things + GIS, characterized in that, include: The activation module is used to activate the information model of the urban pipeline network; the information model is a GIS model that continuously displays real-time operation data of the urban pipeline network obtained based on the Internet of Things. The management module is used for multimodal management of urban pipeline networks based on an information model.
8. The urban pipeline network information system based on IoT + GIS as described in claim 7, characterized in that, The management module, based on an information model, performs multimodal management of the urban pipeline network, including: Based on information technology models, safety early warnings are provided for urban pipeline networks. And / or, Based on an information-based model, drainage planning is carried out for urban pipe networks; And / or, Flood prevention forecasting is performed on urban pipe networks based on information technology models.
9. The urban pipeline network information system based on IoT + GIS as described in claim 7, characterized in that, Also includes: Auxiliary modules are used for: When the information model indicates that there are non-standard pipeline network anomalies in the city, the expert group is assisted in responding to and handling these anomalies based on the information model.
10. The urban pipeline network information system based on Internet of Things + GIS as described in claim 9, characterized in that, The auxiliary module assists the expert group in handling abnormal situations in non-standard pipeline networks based on an information model, including: The information model is sliced to obtain sliced models; the sliced models are the parts of the information model that continuously display real-time progress information of abnormal situations in non-standard pipeline networks. Integrate the slice model into the expert group's online meeting; When at least one expert presents a decision on the slice model during an online meeting, the content already presented on the slice model is optimized in real time. Replace the demoed content on the slice model with the real-time optimized demoed content; Based on the decision guidance tree, and according to the optimized presentation content, decision guidance is provided to each expert in the online meeting; Once the experts have decided on the response and handling strategies during the online meeting, they will conduct online response and handling of non-standard pipeline abnormalities based on these strategies.