GIS-based intelligent inspection command method and system

Abstract

Provided in the present invention are a GIS-based intelligent inspection command method and system. The method comprises: step 1, dynamically acquiring site information of an inspection site at which inspection command needs to be performed; step 2, on the basis of GIS technology and the site information, constructing an inspection GIS model corresponding to the inspection site; step 3, on the basis of the inspection GIS model, determining an appropriate inspection command policy; and step 4, on the basis of the inspection command policy, correspondingly commanding at least one first inspector at the inspection site. In the GIS-based intelligent inspection command method and system of the present invention, a GIS model is constructed on the basis of site information of an inspection site, an appropriate inspection command policy is formulated on the basis of the GIS model, and inspectors are commanded correspondingly, and thus it is not necessary to deploy an inspection commander at the inspection site, thereby reducing labor costs; moreover, the occurrence of problems such as incomplete and irrational command that may exist in manual inspection command is avoided.

Description

A GIS-based intelligent inspection and command method and system Technical Field

[0001] This invention relates to the field of GIS technology, and in particular to a GIS-based intelligent inspection and command method and system. Background Technology

[0002] Currently, when inspection personnel conduct inspections at the inspection site, it is necessary to set up inspection commanders to give corresponding instructions to the inspection personnel based on the equipment conditions at the inspection site. However, the inspection sites are generally large, requiring multiple inspection commanders, which results in high labor costs. In addition, manual inspection command may lead to problems such as incomplete or unreasonable command.

[0003] Therefore, a solution is urgently needed. Summary of the Invention

[0004] This invention provides a GIS-based intelligent inspection command method and system. Based on the on-site information of the inspection site, a GIS model is constructed. Based on the GIS model, appropriate inspection command strategies are formulated, and corresponding commands are given to the inspection personnel. There is no need to set up inspection command personnel on-site, which reduces labor costs. In addition, it avoids the problems of incomplete and unreasonable command that may occur when manual inspection command is carried out.

[0005] This invention provides a GIS-based intelligent inspection and command method, comprising:

[0006] Step 1: Obtain on-site information of the inspection site where inspection command is required;

[0007] Step 2: Based on GIS technology, construct a GIS model corresponding to the inspection site according to the on-site information;

[0008] Step 3: Based on the GIS model, formulate appropriate inspection command strategies;

[0009] Step 4: Based on the inspection command strategy, give corresponding commands to the multiple first inspection personnel in the inspection site.

[0010] Preferably, step 3: Based on the GIS model, formulate a suitable inspection command strategy, including:

[0011] Training inspection command strategy formulation model;

[0012] Based on the inspection command strategy formulation model, and according to the GIS model, a suitable inspection command strategy is formulated.

[0013] Preferably, the training patrol command strategy formulation model includes:

[0014] Acquire multiple first-stage formulation processes for manually developing inspection command strategies;

[0015] The first reliability of the first formulation process is verified, and at the same time, the second reliability of the corresponding process source of the first formulation process is verified.

[0016] When all verifications are successful, the first formulation process will be used as the second formulation process.

[0017] Integrate the various second-stage formulation processes to obtain training samples;

[0018] Based on a preset model training algorithm, the training samples are used to train the model and obtain a patrol command strategy formulation model.

[0019] Preferably, verifying the first reliability of the first formulation process includes:

[0020] Obtain the person who made the decision-making process corresponding to the first decision-making process;

[0021] Obtain the first experience value corresponding to the person making the decision, and at the same time, obtain the decision weight of the person making the decision in the first decision process;

[0022] Assign the first experience value to the specified weight to obtain the second experience value;

[0023] By summing up each of the second empirical values, the first reliability of the first formulation process is obtained;

[0024] If the first reliability is greater than or equal to the preset first reliability threshold, the first reliability of the first formulation process is verified.

[0025] Otherwise, it failed.

[0026] Preferably, the second reliability verification of the process source corresponding to the first formulation process includes:

[0027] Based on a preset process source association library, multiple associated process sources are determined that correspond to the process source of the first defined process;

[0028] Obtain the first credit value corresponding to the source of the association process, and at the same time, obtain the association weight corresponding to the association relationship between the source of the association process and the source of the process.

[0029] Assign the first credit value the associated weight to obtain the second credit value;

[0030] The third credit score is obtained by averaging the second credit score.

[0031] Obtain the fourth credit value corresponding to the source of the process;

[0032] The third credit value and the fourth credit value are summed to obtain the second reliability of the process source corresponding to the first formulation process;

[0033] If the second reliability is greater than or equal to the preset second reliability threshold, the second reliability of the corresponding process source of the first formulation process is verified.

[0034] Otherwise, it failed.

[0035] Preferably, the GIS-based intelligent inspection and command method also includes:

[0036] After giving instructions to the first inspection personnel, obtain the first field equipment that the first inspection personnel will inspect at the inspection site.

[0037] Obtain the inspection experience value of the first inspection personnel corresponding to the first field equipment;

[0038] Obtain the inspection experience value threshold and the necessary values ​​for inspection specification verification corresponding to the first field equipment;

[0039] If the inspection experience value is less than or equal to the inspection experience value threshold and / or the inspection specification verification necessity value is greater than or equal to the preset inspection specification verification necessity value threshold, obtain the preset dynamic distribution map of the guidance trolley, and determine the guidance trolley closest to the first field equipment from the dynamic distribution map of the guidance trolley;

[0040] Obtain the docking node corresponding to the guide vehicle, and dock with the guide vehicle through the docking node;

[0041] After docking is completed, the guide trolley is controlled to move to the first field equipment. At the same time, the first inspection personnel are informed to wait for the guide trolley to arrive at the first field equipment before starting the inspection.

[0042] When the guide trolley arrives at the first field device, the guide trolley is controlled to dynamically collect three-dimensional information within a preset range around it;

[0043] Based on the aforementioned three-dimensional information, a site three-dimensional model corresponding to the aforementioned range is constructed;

[0044] Based on the preset behavior acquisition dynamic control model, the guide vehicle is dynamically controlled to acquire behavior according to the on-site three-dimensional model;

[0045] The inspection behavior of the first inspection personnel collected by the guidance vehicle is obtained when the guidance vehicle is dynamically controlled to collect behavior data.

[0046] Obtain the inspection non-standard behavior database corresponding to the first field equipment, and match the inspection behavior with the first inspection non-standard behavior in the inspection non-standard behavior database;

[0047] If a match is found, the first non-standard inspection behavior that matches is taken as the second non-standard inspection behavior, and the first inspection personnel who generated the matched inspection behavior is taken as the second inspection personnel.

[0048] Obtain the severity value corresponding to the second non-standard inspection behavior;

[0049] If the severity value is less than or equal to a preset severity value threshold, obtain the reminder method corresponding to the second non-standard inspection behavior, and based on the reminder method, control the guidance trolley to give the second inspection personnel a corresponding reminder;

[0050] Otherwise, obtain the response and command strategy corresponding to the second non-standard inspection behavior, and based on the response and command strategy, control the guidance trolley to give corresponding response and command to the first inspection personnel;

[0051] At the same time, we will obtain the dynamic information extraction strategy and risk assessment model corresponding to the second non-standard inspection behavior;

[0052] Based on the aforementioned information dynamic extraction strategy, target information is dynamically extracted from the on-site information;

[0053] Based on the risk assessment model, and according to the target information, the risk assessment is performed on the inspection sub-site corresponding to the first field equipment in the inspection site to obtain the risk value;

[0054] If the risk value is greater than or equal to the preset risk threshold, the guide trolley will be immediately controlled to lead the first inspection personnel away from the inspection site.

[0055] Preferably, the GIS-based intelligent inspection and command method also includes:

[0056] When the first inspection personnel inputs a guidance request while inspecting the second field equipment, the expert node set corresponding to the second field equipment is obtained, and the expert node set includes: multiple first expert nodes;

[0057] Obtain the level of expertise of the first expert node corresponding to the second field device;

[0058] The first expert node is traversed in descending order of its level of expertise.

[0059] Obtain the node status corresponding to the first expert node encountered in each iteration, where the node status includes: idle and busy;

[0060] When the node corresponding to the first expert node is idle each time it is traversed, the corresponding first expert node will be used as the second expert node.

[0061] When the node status corresponding to the first expert node is busy each time it is traversed, obtain the execution progress of the task being executed by the corresponding first expert node;

[0062] Retrieve the importance and execution progress of the task type - accessible value library;

[0063] Based on the execution progress-interventionable value library, determine the interventionable value corresponding to the execution progress;

[0064] If the importance is less than or equal to a preset importance threshold and the intervention value is less than or equal to a preset intervention value threshold, the corresponding first expert node will be used as the second expert node.

[0065] The first inspection personnel are dynamically connected to the second expert node, and the second expert node guides the first inspection personnel to inspect the second field equipment.

[0066] This invention provides a GIS-based intelligent inspection and command system, comprising:

[0067] The acquisition module is used to acquire on-site information of the inspection site where inspection command is required;

[0068] The construction module is used to construct a GIS model corresponding to the inspection site based on the on-site information using GIS technology.

[0069] A module for formulating appropriate inspection command strategies based on the GIS model;

[0070] The command module is used to give corresponding commands to multiple first inspection personnel in the inspection site based on the inspection command strategy.

[0071] Preferably, the designation module performs the following operations:

[0072] Training inspection command strategy formulation model;

[0073] Based on the inspection command strategy formulation model, and according to the GIS model, a suitable inspection command strategy is formulated.

[0074] Preferably, the designation module performs the following operations:

[0075] Acquire multiple first-stage formulation processes for manually developing inspection command strategies;

[0076] The first reliability of the first formulation process is verified, and at the same time, the second reliability of the corresponding process source of the first formulation process is verified.

[0077] When all verifications are successful, the first formulation process will be used as the second formulation process.

[0078] Integrate the various second-stage formulation processes to obtain training samples;

[0079] Based on a preset model training algorithm, the training samples are used to train the model and obtain a patrol command strategy formulation model.

[0080] Other features and advantages of the invention will be set forth in the description which follows, 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, claims, and drawings.

[0081] 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

[0082] 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:

[0083] Figure 1 is a flowchart of a GIS-based intelligent inspection and command method in an embodiment of the present invention;

[0084] Figure 2 is a schematic diagram of a GIS-based intelligent inspection and command system in an embodiment of the present invention. Detailed Implementation

[0085] 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.

[0086] This invention provides a GIS-based intelligent inspection and command method, as shown in Figure 1, including:

[0087] Step 1: Obtain on-site information of the inspection site where inspection command is required;

[0088] Step 2: Based on GIS technology, construct a GIS model corresponding to the inspection site according to the on-site information;

[0089] Step 3: Based on the GIS model, formulate appropriate inspection command strategies;

[0090] Step 4: Based on the inspection command strategy, give corresponding commands to the multiple first inspection personnel in the inspection site.

[0091] The working principle and beneficial effects of the above technical solution are as follows:

[0092] Acquire on-site information of the inspection site requiring inspection command (e.g., building structure information; last inspection time, location, model, and operating data of each piece of equipment; current location of each inspection personnel and their status information, such as whether they are performing an inspection task). Based on GIS technology, construct a GIS model according to the on-site information (e.g., display the location and status information of each piece of equipment on the GIS model). Based on the GIS model, formulate appropriate inspection command strategies (e.g., if the operating data of a piece of equipment is suspected to be abnormal, instruct the nearest inspection personnel to inspect it; or if a piece of equipment has reached its next inspection time, instruct the nearest inspection personnel to inspect it). Based on the inspection command strategy, give corresponding instructions to the first inspection personnel in the inspection site (when giving instructions, the inspection task can be sent to the smart terminal carried by the inspection personnel, which can be a smartphone, tablet, etc.).

[0093] This invention constructs a GIS model based on on-site information from the inspection site, formulates appropriate inspection command strategies based on the GIS model, and provides corresponding commands to inspection personnel. This eliminates the need to set up inspection command personnel on-site, reducing labor costs. In addition, it avoids the problems of incomplete and unreasonable command that may occur when conducting inspections manually.

[0094] This invention provides a GIS-based intelligent inspection and command method. Step 3: Based on the GIS model, formulate a suitable inspection and command strategy, including:

[0095] Training inspection command strategy formulation model;

[0096] Based on the inspection command strategy formulation model, and according to the GIS model, a suitable inspection command strategy is formulated.

[0097] The working principle and beneficial effects of the above technical solution are as follows:

[0098] When formulating inspection and command strategies based on GIS models, machine learning technology can be used to learn how humans formulate inspection and command strategies based on GIS models, i.e., to train the inspection and command strategy formulation model; based on the training, the inspection and command strategy formulation model is trained, and appropriate inspection and command strategies are formulated according to the GIS model.

[0099] This invention provides a GIS-based intelligent inspection command method, which trains an inspection command strategy formulation model, including:

[0100] Acquire multiple first-stage formulation processes for manually developing inspection command strategies;

[0101] The first reliability of the first formulation process is verified, and at the same time, the second reliability of the corresponding process source of the first formulation process is verified.

[0102] When all verifications are successful, the first formulation process will be used as the second formulation process.

[0103] Integrate the various second-stage formulation processes to obtain training samples;

[0104] Based on a preset model training algorithm, the training samples are used to train the model and obtain a patrol command strategy formulation model.

[0105] The working principle and beneficial effects of the above technical solution are as follows:

[0106] When training the patrol command strategy formulation model, the first formulation process of the patrol command strategy is obtained from a manual GIS model (which can be obtained based on big data technology). Machine learning technology is then used to learn from this first formulation process. However, this first formulation process can be generated by local staff or by staff from other patrol sites. Therefore, to ensure the training quality of the patrol command strategy formulation model and the accuracy of subsequent patrol command strategy formulation, the reliability of the first formulation process needs to be verified. This verification can include verifying the reliability of the first formulation process itself and its primary reliability, as well as the reliability and secondary reliability of the process source corresponding to the first formulation process. When all verifications pass, the corresponding secondary formulation process is integrated to obtain training samples. Based on a pre-defined model training algorithm (e.g., a machine learning algorithm), the training samples are used to train the model (machine learning algorithms and model training using machine learning algorithms are existing technologies and will not be elaborated upon), thus obtaining the patrol command strategy formulation model.

[0107] This invention provides a GIS-based intelligent inspection and command method, which verifies the reliability of the first formulation process, including:

[0108] Obtain the person who made the decision-making process corresponding to the first decision-making process;

[0109] Obtain the first experience value corresponding to the person making the decision, and at the same time, obtain the decision weight of the person making the decision in the first decision process;

[0110] Assign the first experience value to the specified weight to obtain the second experience value;

[0111] By summing up each of the second empirical values, the first reliability of the first formulation process is obtained;

[0112] If the first reliability is greater than or equal to the preset first reliability threshold, the first reliability of the first formulation process is verified.

[0113] Otherwise, it failed.

[0114] The working principle and beneficial effects of the above technical solution are as follows:

[0115] When verifying the reliability of the first formulation process itself, one can start by considering the experience level of the person who formulated the first formulation process; obtain the first experience value corresponding to the person who formulated it, the higher the first experience value, the richer the person's command experience, etc.; obtain the formulation weight of the person who formulated it in the first formulation process, the higher the formulation weight, the greater the degree of participation and contribution of the person who formulated it in the first formulation process, etc.; assign the formulation weight corresponding to the first experience value to obtain the second experience value (multiply the two, for example: if the first experience value is 100 and the formulation weight is 0.25, the second experience value is 100*0.25=25); accumulate the second experience values ​​to obtain the first reliability; when the first reliability is greater than or equal to the preset first reliability threshold, the verification passes; otherwise (if the first reliability is less than the first reliability threshold), it fails.

[0116] This invention provides a GIS-based intelligent inspection and command method, which verifies the second reliability of the process source corresponding to the first formulation process, including:

[0117] Based on a preset process source association library, multiple associated process sources are determined that correspond to the process source of the first defined process;

[0118] Obtain the first credit value corresponding to the source of the association process, and at the same time, obtain the association weight corresponding to the association relationship between the source of the association process and the source of the process.

[0119] Assign the first credit value the associated weight to obtain the second credit value;

[0120] The third credit score is obtained by averaging the second credit score.

[0121] Obtain the fourth credit value corresponding to the source of the process;

[0122] The third credit value and the fourth credit value are summed to obtain the second reliability of the process source corresponding to the first formulation process;

[0123] If the second reliability is greater than or equal to the preset second reliability threshold, the second reliability of the corresponding process source of the first formulation process is verified.

[0124] Otherwise, it failed.

[0125] The working principle and beneficial effects of the above technical solution are as follows:

[0126] When verifying the reliability of the process source for the first formulation process (e.g., other inspection sites and big data service providers), a fourth credit value can be obtained by considering the historical credit history of the process source (the authenticity of the formulation processes provided in the past). Alternatively, a process source association database can be established. Based on this database, associated process sources can be identified (e.g., inspection sites from the same head office). The first credit value can then be obtained based on the historical credit history of these associated process sources. By comprehensively verifying the reliability of the process source for the first formulation process, a negative credit record from a process source will affect its associated process sources. Conversely, increasing the cost of poor credit in the process source production line indirectly improves the quality of the process source's provision of the specified process. After obtaining the first credit value, the association weights corresponding to the association relationships between related process sources (e.g., the inspection site comes from the same head office) are obtained. The closer the association relationship, the greater the association weight. The first credit value is assigned a corresponding association weight (the two are multiplied) to obtain the second credit value. The second credit value is averaged to obtain the third credit value. The third and fourth credit values ​​are summed to obtain the second reliability. If the second reliability is greater than or equal to the preset second reliability threshold, the verification is passed; otherwise, it is not passed.

[0127] This invention provides a GIS-based intelligent inspection and command method, which also includes:

[0128] After giving instructions to the first inspection personnel, obtain the first field equipment that the first inspection personnel will inspect at the inspection site.

[0129] Obtain the inspection experience value of the first inspection personnel corresponding to the first field equipment;

[0130] Obtain the inspection experience value threshold and the necessary values ​​for inspection specification verification corresponding to the first field equipment;

[0131] If the inspection experience value is less than or equal to the inspection experience value threshold and / or the inspection specification verification necessity value is greater than or equal to the preset inspection specification verification necessity value threshold, obtain the preset dynamic distribution map of the guidance trolley, and determine the guidance trolley closest to the first field equipment from the dynamic distribution map of the guidance trolley;

[0132] Obtain the docking node corresponding to the guide vehicle, and dock with the guide vehicle through the docking node;

[0133] After docking is completed, the guide trolley is controlled to move to the first field equipment. At the same time, the first inspection personnel are informed to wait for the guide trolley to arrive at the first field equipment before starting the inspection.

[0134] When the guide trolley arrives at the first field device, the guide trolley is controlled to dynamically collect three-dimensional information within a preset range around it;

[0135] Based on the aforementioned three-dimensional information, a site three-dimensional model corresponding to the aforementioned range is constructed;

[0136] Based on the preset behavior acquisition dynamic control model, the guide vehicle is dynamically controlled to acquire behavior according to the on-site three-dimensional model;

[0137] The inspection behavior of the first inspection personnel collected by the guidance vehicle is obtained when the guidance vehicle is dynamically controlled to collect behavior data.

[0138] Obtain the inspection non-standard behavior database corresponding to the first field equipment, and match the inspection behavior with the first inspection non-standard behavior in the inspection non-standard behavior database;

[0139] If a match is found, the first non-standard inspection behavior that matches is taken as the second non-standard inspection behavior, and the first inspection personnel who generated the matched inspection behavior is taken as the second inspection personnel.

[0140] Obtain the severity value corresponding to the second non-standard inspection behavior;

[0141] If the severity value is less than or equal to a preset severity value threshold, obtain the reminder method corresponding to the second non-standard inspection behavior, and based on the reminder method, control the guidance trolley to give the second inspection personnel a corresponding reminder;

[0142] Otherwise, obtain the response and command strategy corresponding to the second non-standard inspection behavior, and based on the response and command strategy, control the guidance trolley to give corresponding response and command to the first inspection personnel;

[0143] At the same time, we will obtain the dynamic information extraction strategy and risk assessment model corresponding to the second non-standard inspection behavior;

[0144] Based on the aforementioned information dynamic extraction strategy, target information is dynamically extracted from the on-site information;

[0145] Based on the risk assessment model, and according to the target information, the risk assessment is performed on the inspection sub-site corresponding to the first field equipment in the inspection site to obtain the risk value;

[0146] If the risk value is greater than or equal to the preset risk threshold, the guide trolley will be immediately controlled to lead the first inspection personnel away from the inspection site.

[0147] The working principle and beneficial effects of the above technical solution are as follows:

[0148] When inspecting special equipment (such as special gas equipment), it is crucial to strictly adhere to standardized inspection procedures. Improper procedures can lead to safety accidents (e.g., inspectors overconfidently performing maintenance without protective masks, resulting in the inhalation of the gas), impacting inspectors and potentially causing irreversible damage, as well as affecting company production. Furthermore, inspectors vary in their experience with different equipment. When inspecting unfamiliar equipment, they typically consult the corresponding inspection manual. However, manuals do not cover all inspection items. If inspectors attempt to repair problems not documented in the manual (e.g., oil seal leaks), improper procedures may damage the equipment. Consulting experienced inspectors may not yield timely answers (e.g., experienced inspectors are currently performing inspections; or some specialized issues may only be known to a small number of inspectors), reducing inspection efficiency. Therefore, solutions are urgently needed.

[0149] When the first inspection personnel inspect the first field equipment, the inspection experience value of the first inspection personnel for the first field equipment is obtained (this can be determined based on the inspection duration and number of problems encountered by the first inspection personnel in the past inspections of the first field equipment; the higher the inspection experience value, the more experienced the first inspection personnel are in inspecting the first field equipment); the inspection experience value threshold (representing the required level of experience of the inspection personnel inspecting the first field equipment) and the inspection specification verification necessity value (representing the degree of necessity for the inspection personnel to verify the inspection operation specifications when the first field equipment is inspected, for example, for special gas equipment, the inspection specification verification necessity value is 95); if the inspection experience value is less than or equal to the inspection experience value threshold and / or the inspection specification verification necessity value is greater than or equal to the inspection specification verification necessity value threshold, it indicates that the inspection operation specifications of the first inspection personnel need to be monitored.

[0150] When monitoring the inspection operation procedures of the first inspection personnel, the task can be completed by a guidance trolley, further reducing labor costs. The nearest guidance trolley is determined from a preset dynamic distribution map (virtual map, marking the dynamic positions of each guidance trolley), and docking is established with it through its corresponding docking node. The controller then travels to the first field equipment and simultaneously informs the first inspection personnel to wait. When the guidance trolley arrives at the field, it needs to collect the operational behavior generated by the first inspection personnel during their inspection of the first field equipment (this can be done by installing a camera on the guidance trolley).

[0151] However, the on-site conditions of the first-line equipment are generally complex (e.g., other inspectors are inspecting other equipment). It is necessary to ensure that the guidance trolley can continuously collect operational behavior data. Therefore, the guidance trolley is controlled to dynamically (timedly) collect three-dimensional information within a preset range (e.g., 3 meters) (millimeter-wave radar sensors can be installed on the trolley for data collection). Based on the three-dimensional information, a three-dimensional model of the site is constructed. Based on a preset dynamic control model for behavior data collection (a pre-trained model used to control the guidance trolley to continuously collect operational behaviors generated by inspectors according to the site conditions; for example, when inspectors operate with their hands but are facing away from the guidance trolley, the guidance trolley is controlled to move in front of the inspectors to collect hand gestures), the inspection behavior of the first inspector on the guidance trolley is dynamically controlled according to the three-dimensional model of the site.

[0152] Next, it is necessary to confirm whether the inspection behavior is standardized. If it is not standardized, appropriate measures should be taken. The inspection behavior is matched with the first non-standard inspection behavior in the non-standard inspection behavior database corresponding to the first field equipment (e.g., not wearing a protective mask). If the match is found, it means that the inspection behavior is not standardized. The severity value corresponding to the second non-standard inspection behavior that matches is obtained. The higher the severity value, the more serious the consequences. When the consequences are not serious, the corresponding reminder method is obtained (e.g., voice broadcast "Please wear a protective mask immediately"). Based on this reminder method, the second inspection personnel are reminded. When the consequences are serious, the corresponding response command strategy for the second non-standard inspection behavior is obtained (e.g., when repairing a valve, if the valve is not closed tightly before opening the main valve of the special gas, resulting in a special gas leak, the inspection personnel are instructed by voice to tighten the valve and at the same time, the special gas chamber is evacuated and vented). The first inspection personnel are instructed to take timely and effective action on site.

[0153] However, if the consequences are severe, they may affect the inspection personnel. If the impact is significant, the inspection personnel need to be reminded to evacuate. A dynamic information extraction strategy (e.g., extracting the concentration of special gas in the special gas chamber) and a risk assessment model (a pre-trained model based on on-site information for risk assessment, e.g., a high concentration of special gas indicates a high risk) are needed to acquire information corresponding to the second inspection non-standard behavior. Based on the dynamic information extraction strategy, target information (e.g., the concentration of special gas in the special gas chamber) is dynamically extracted from the on-site information. Based on the risk assessment model, the risk situation is determined according to the target information, and a risk value is obtained. Once the risk value is greater than or equal to the preset risk threshold, it indicates a high risk. The control and guidance trolley then leads the first inspection personnel away to ensure their safety.

[0154] This invention provides a GIS-based intelligent inspection and command method, which also includes:

[0155] When the first inspection personnel inputs a guidance request while inspecting the second field equipment, the expert node set corresponding to the second field equipment is obtained, and the expert node set includes: multiple first expert nodes;

[0156] Obtain the level of expertise of the first expert node corresponding to the second field device;

[0157] The first expert node is traversed in descending order of its level of expertise.

[0158] Obtain the node status corresponding to the first expert node encountered in each iteration, where the node status includes: idle and busy;

[0159] When the node corresponding to the first expert node is idle each time it is traversed, the corresponding first expert node will be used as the second expert node.

[0160] When the node status corresponding to the first expert node is busy each time it is traversed, obtain the execution progress of the task being executed by the corresponding first expert node;

[0161] Retrieve the importance and execution progress of the task type - accessible value library;

[0162] Based on the execution progress-interventionable value library, determine the interventionable value corresponding to the execution progress;

[0163] If the importance is less than or equal to a preset importance threshold and the intervention value is less than or equal to a preset intervention value threshold, the corresponding first expert node will be used as the second expert node.

[0164] The first inspection personnel are dynamically connected to the second expert node, and the second expert node guides the first inspection personnel to inspect the second field equipment.

[0165] The working principle and beneficial effects of the above technical solution are as follows:

[0166] When the first inspector inspects the second-site equipment, they may encounter problems beyond their capabilities. In this case, multiple first expert nodes are set up, each corresponding to an inspector with extensive experience inspecting the second-site equipment. These first expert nodes can be from the current site or from other inspection sites. The most suitable first expert node is selected to guide the first inspector. During selection, the level of expertise of the first expert node must be ensured. Therefore, the level of expertise of the first expert node corresponding to the second-site equipment is determined. The higher the level of expertise, the more experienced the inspector is in inspecting the second-site equipment. The first expert nodes are traversed sequentially according to their level of expertise, from highest to lowest. The node status of each traversed first expert node is obtained, with the node status categorized as idle (corresponding to the inspector's hands). The node status is either "idle" or "busy" (corresponding to the inspector currently performing an inspection task); when the node status is idle, it directly acts as the second expert node for connection; when the node status is busy, it obtains the task progress of the task being executed by the first expert node; it obtains the task type and execution progress - intervention value library (which stores intervention values ​​corresponding to different execution progresses; the intervention value represents the degree to which the inspector can answer questions when the task has reached the corresponding execution progress, for example: the task is about to end and some finishing work is being done, so questions can be answered); it obtains the importance corresponding to the task type; the higher the importance, the more important the task; based on the execution progress - intervention value library, it determines the intervention value corresponding to the execution progress; when the importance is low and the intervention value is high, it indicates that intervention is possible, and the node acts as the second expert node for dynamic connection with the first inspector;

[0167] When the second expert node connects with the first inspector, the second expert node can answer the questions raised by the first inspector, and can also broadcast the first inspector's first-person inspection perspective to the second expert node based on VR technology.

[0168] This invention provides a GIS-based intelligent inspection and command method, which also includes:

[0169] The GIS model is adaptively modified at preset time intervals;

[0170] The steps for determining the time interval are as follows:

[0171] Obtain the operational data of the GIS model within the most recent preset time period;

[0172] Feature extraction is performed on the operational data to obtain multiple first features;

[0173] Obtain a preset operation trigger feature library, match the first feature with the second feature in the operation trigger feature library, and if the match is found, obtain the trigger value corresponding to the matching second feature;

[0174] Based on the trigger value, the time interval is calculated using the following formula:

[0175] in, The time interval is γ, where γ is a preset initial value for the time interval, and J t Let t be the t-th trigger value, and Q be the total number of trigger values.

[0176] The working principle and beneficial effects of the above technical solution are as follows:

[0177] When a GIS model is in operation, it may experience operational anomalies, such as incomplete information mapping, lag, and delays. Therefore, it is necessary to periodically perform adaptive corrections on the GIS model (e.g., correcting incomplete information mapping). An operational trigger feature library is set up, which stores features that suggest potential operational anomalies in the GIS model. The first feature of the data from the GIS model's operation within the most recent preset time period (e.g., 50 seconds) is matched with the second feature in this library. If a match is found, the corresponding trigger value is obtained. The larger the trigger value, the greater the likelihood of an anomaly, and the time interval needs to be adjusted downwards. This improves the timeliness and efficiency of corrections.

[0178] This invention provides a GIS-based intelligent inspection and command system, as shown in Figure 2, comprising:

[0179] Module 1 is used to acquire on-site information of the inspection site where inspection command is required;

[0180] Module 2 is used to construct a GIS model corresponding to the inspection site based on the on-site information using GIS technology.

[0181] Module 3 is used to formulate appropriate inspection command strategies based on the GIS model.

[0182] Command module 4 is used to give corresponding commands to multiple first inspection personnel in the inspection site based on the inspection command strategy.

[0183] The working principle and beneficial effects of the above technical solution have been explained in the methodology summary and will not be repeated here.

[0184] 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 GIS-based intelligent inspection and command method, characterized in that, include: Step 1: Obtain on-site information of the inspection site where inspection command is required; Step 2: Based on GIS technology and on-site information, construct a GIS model corresponding to the inspection site; Step 3: Based on the GIS model, formulate appropriate inspection command strategies; Step 4: Based on the inspection command strategy, give corresponding instructions to the multiple first inspection personnel in the inspection site.

2. The GIS-based intelligent inspection and command method as described in claim 1, characterized in that, Step 3: Based on the GIS model, formulate appropriate inspection command strategies, including: Training inspection command strategy formulation model; Based on the patrol and inspection command strategy formulation model, appropriate patrol and inspection command strategies are formulated according to the GIS model.

3. The GIS-based intelligent inspection and command method as described in claim 2, characterized in that, Training inspection command strategy formulation model, including: Acquire multiple first-stage formulation processes for manually developing inspection command strategies; The first reliability of the first formulation process is verified, and at the same time, the second reliability of the corresponding process source of the first formulation process is verified. When all verifications are successful, the corresponding first formulation process will be used as the second formulation process. Integrate the various second-stage formulation processes to obtain training samples; Based on a pre-set model training algorithm, the model is trained on the training samples to obtain a patrol command strategy formulation model.

4. The GIS-based intelligent inspection and command method as described in claim 3, characterized in that, Verification of the first reliability of the first formulation process includes: Obtain the person who formulated the first formulation process; Obtain the first experience value corresponding to the person who made the decision, and at the same time, obtain the decision weight of the person who made the decision in the first decision process; Assign a specific weight to the first experience value to obtain the second experience value; By summing up the second empirical values, the first reliability of the first formulation process is obtained; If the first reliability is greater than or equal to the preset first reliability threshold, the first reliability of the first formulation process is verified. Otherwise, it failed.

5. The GIS-based intelligent inspection and command method as described in claim 3, characterized in that, Verification of the second reliability of the process source corresponding to the first formulation process includes: Based on a preset process source association library, multiple associated process sources are identified that correspond to the process source of the first defined process; Obtain the first credit value corresponding to the source of the associated process, and at the same time, obtain the association weight corresponding to the association relationship between the source of the associated process and the process source; Assign a corresponding association weight to the first credit value to obtain the second credit value; The third credit score is obtained by averaging the second credit score. The fourth credit score corresponding to the source of the acquisition process; The third and fourth credit values ​​are summed to obtain the second reliability of the process source corresponding to the first formulation process; If the second reliability is greater than or equal to the preset second reliability threshold, the second reliability of the corresponding process source of the first formulation process is verified. Otherwise, it failed.

6. The GIS-based intelligent inspection and command method as described in claim 1, characterized in that, Also includes: When the first inspection personnel inputs a guidance request while inspecting the second field equipment, the expert node set corresponding to the second field equipment is obtained. The expert node set includes: multiple first expert nodes. Obtain the level of expertise of the first expert node corresponding to the second field equipment; Iterate through the first expert node in descending order of professional level; Get the node status corresponding to the first expert node encountered in each traversal. The node status includes: idle and busy. When the node corresponding to the first expert node encountered in each iteration is in an idle state, the corresponding first expert node will be used as the second expert node. When the node status corresponding to the first expert node encountered in each iteration is busy, obtain the execution progress of the task being executed by the corresponding first expert node; Retrieve the importance and execution progress of a task based on its task type - accessible value library; Based on the execution progress-interventionable value library, determine the interventionable values ​​corresponding to the execution progress; If the importance is less than or equal to the preset importance threshold and the intervention value is less than or equal to the preset intervention value threshold, the corresponding first expert node will be used as the second expert node. The first inspection personnel are dynamically connected with the second expert node, and the second expert node guides the first inspection personnel to inspect the second field equipment.

7. A GIS-based intelligent inspection and command system, characterized in that, include: The acquisition module is used to acquire on-site information of the inspection site where inspection command is required; The module is used to build a GIS model corresponding to the inspection site based on GIS technology and on-site information; A module is designed to develop appropriate inspection and command strategies based on GIS models. The command module is used to give corresponding commands to multiple first inspection personnel at the inspection site based on the inspection command strategy.

8. The GIS-based intelligent inspection and command system as described in claim 7, characterized in that, The module is configured to perform the following operations: Training inspection command strategy formulation model; Based on the patrol and inspection command strategy formulation model, appropriate patrol and inspection command strategies are formulated according to the GIS model.

9. The GIS-based intelligent inspection and command system as described in claim 8, characterized in that, The module is configured to perform the following operations: Acquire multiple first-stage formulation processes for manually developing inspection command strategies; The first reliability of the first formulation process is verified, and at the same time, the second reliability of the corresponding process source of the first formulation process is verified. When all verifications are successful, the corresponding first formulation process will be used as the second formulation process. Integrate the various second-stage formulation processes to obtain training samples; Based on a pre-set model training algorithm, the model is trained on the training samples to obtain a patrol command strategy formulation model.

10. The GIS-based intelligent inspection and command system as described in claim 9, characterized in that, Verification of the first reliability of the first formulation process includes: Obtain the person who formulated the first formulation process; Obtain the first experience value corresponding to the person who made the decision, and at the same time, obtain the decision weight of the person who made the decision in the first decision process; Assign a specific weight to the first experience value to obtain the second experience value; By summing up the second empirical values, the first reliability of the first formulation process is obtained; If the first reliability is greater than or equal to the preset first reliability threshold, the first reliability of the first formulation process is verified. Otherwise, it failed.

Images