GIS-based intelligent inspection command method and system

Abstract

The invention provides a GIS-based intelligent inspection commanding method and system, and the method comprises the steps: 1, dynamically obtaining the field information of an inspection field which needs inspection commanding; 2, based on the GIS technology, according to the field information, constructing an inspection GIS model corresponding to the inspection field; 3, determining a suitable inspection command strategy based on the inspection GIS model; and step 4, correspondingly commanding at least one first inspector in the inspection site based on the inspection command strategy. According to the intelligent inspection commanding method and system based on the GIS, the GIS model is constructed based on the field information of the inspection field, the appropriate inspection commanding strategy is formulated based on the GIS model, corresponding commanding is carried out on the inspection personnel, the inspection commanding personnel do not need to be arranged in the inspection field, the labor cost is reduced, and in addition, the inspection commanding efficiency is improved. And the problems of incomplete and unreasonable commanding and the like possibly caused by manual inspection commanding are avoided.

Description

Technical Field

[0001] The present invention relates to the field of GIS technology, and in particular to an intelligent inspection command method and system based on GIS. Background Art

[0002] At present, when inspectors conduct inspections at the inspection site, it is necessary to set up inspection commanders to command the inspectors accordingly according to the equipment conditions at the inspection site. However, the inspection site is generally large, and multiple inspection commanders need to be set up, which has a high manpower cost. In addition, manual inspection command may result in incomplete and unreasonable command.

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

[0004] The present 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, an appropriate inspection command strategy is formulated, and corresponding commands are given to inspection personnel. There is no need to set up inspection command personnel at the inspection site, which reduces labor costs. In addition, it avoids the occurrence of problems such as incomplete and unreasonable command in manual inspection command.

[0005] The present invention provides a GIS-based intelligent inspection 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 site information;

[0008] Step 3: Formulate appropriate inspection command strategies based on the GIS model;

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

[0010] Preferably, step 3: formulating an appropriate inspection command strategy based on the GIS model, including:

[0011] Training inspection command strategy formulation model;

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

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

[0014] Obtain multiple first formulation processes for manual inspection command strategy formulation;

[0015] Verifying a first reliability of the first formulation process and, at the same time, verifying a second reliability of a process source corresponding to the first formulation process;

[0016] When both pass the verification, the first formulation process is used as the second formulation process;

[0017] Integrate each of the second formulation processes to obtain training samples;

[0018] Based on a preset model training algorithm, model training is performed on the training samples to obtain an inspection command strategy formulation model.

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

[0020] Obtaining a formulater corresponding to the first formulation process;

[0021] Acquire a first experience value corresponding to the formulater, and at the same time, acquire a formulation weight of the formulater corresponding to the first formulation process;

[0022] Assigning the first experience value to the set weight corresponding to the first experience value to obtain a second experience value;

[0023] Accumulating and calculating the second experience values ​​to obtain a first reliability of the first formulation process;

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

[0025] Otherwise, failed.

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

[0027] Based on a preset process source association library, determining a plurality of associated process sources associated with a process source corresponding to the first formulated process;

[0028] Acquire a first credit value corresponding to the associated process source, and at the same time, acquire an associated weight corresponding to an associated relationship between the associated process source and the process source;

[0029] Assigning the first credit value to the corresponding association weight to obtain a second credit value;

[0030] averaging the second credit values ​​to obtain a third credit value;

[0031] Obtaining a fourth credit value corresponding to the process source;

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

[0033] If the second reliability is greater than or equal to a preset second reliability threshold, the second reliability of the process source corresponding to the first formulation process passes verification;

[0034] Otherwise, failed.

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

[0036] After directing the first inspection personnel, obtaining the first on-site equipment that the first inspection personnel is about to inspect in the inspection site;

[0037] Acquire the inspection experience value of the first inspection personnel corresponding to the first on-site equipment;

[0038] Obtaining an inspection experience value threshold and an inspection specification verification necessary value corresponding to the first field device;

[0039] If the inspection experience value is less than or equal to the inspection experience value threshold and / or the inspection specification verification necessary value is greater than or equal to the preset inspection specification verification necessary value threshold, obtaining a preset guidance vehicle dynamic distribution map, and determining the guidance vehicle closest to the first field device from the guidance vehicle dynamic distribution map;

[0040] Obtaining a docking node corresponding to the guiding vehicle, and docking with the guiding vehicle through the docking node;

[0041] After the docking is completed, the guidance vehicle is controlled to go to the first on-site device, and at the same time, the first inspection personnel are informed to wait for the guidance vehicle to arrive at the first on-site device before starting the inspection;

[0042] When the guidance vehicle arrives at the first on-site device, the guidance vehicle is controlled to dynamically collect three-dimensional information within a preset range of the surrounding area;

[0043] Based on the three-dimensional information, construct a three-dimensional model of the site corresponding to the range;

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

[0045] Acquiring the inspection behavior of the first inspection personnel collected by the guidance vehicle when dynamically controlling the behavior collection of the guidance vehicle;

[0046] Acquire a patrol irregular behavior library corresponding to the first field device, and match the patrol behavior with a first patrol irregular behavior in the patrol irregular behavior library;

[0047] If the match is met, the first patrol irregular behavior that matches the match is used as the second patrol irregular behavior, and at the same time, the first patrol inspector who produces the patrol behavior that matches the match is used as the second patrol inspector;

[0048] Obtaining a severity value corresponding to the second inspection irregular behavior;

[0049] If the severity value is less than or equal to a preset severity value threshold, obtaining a reminder method corresponding to the second inspection irregular behavior, and based on the reminder method, controlling the guidance vehicle to give a corresponding reminder to the second inspection personnel;

[0050] Otherwise, obtaining a response command strategy corresponding to the second inspection irregular behavior, and based on the response command strategy, controlling the guidance vehicle to give corresponding response command to the first inspection personnel;

[0051] At the same time, obtaining a dynamic information extraction strategy and a risk determination model corresponding to the second inspection irregular behavior;

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

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

[0054] If the risk value is greater than or equal to a preset risk threshold, the guidance vehicle is immediately controlled to lead the first inspection personnel to leave the inspection sub-site.

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

[0056] When the first inspection personnel inputs a guidance request when inspecting the second field device, an expert node set corresponding to the second field device is obtained, the expert node set including: a plurality of first expert nodes;

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

[0058] Traversing the first expert nodes in order from the highest to the lowest expertise;

[0059] Obtaining the node status corresponding to the first expert node traversed each time, the node status including: idle and busy;

[0060] When the node state corresponding to the first expert node traversed each time is idle, the corresponding first expert node is used as the second expert node;

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

[0062] Obtain the importance and execution progress corresponding to the task type of the task - the value library can be intervened;

[0063] Determining the intervenable value corresponding to the execution progress based on the execution progress-intervenable value library;

[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 is used as a second expert node;

[0065] The first inspection personnel is dynamically connected to the second expert node, and the second expert node guides the first inspection personnel to inspect the second on-site equipment.

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

[0067] An acquisition module is used to acquire on-site information of the inspection site that needs to be inspected and directed;

[0068] A construction module, used to construct a GIS model corresponding to the inspection site based on the site information based on GIS technology;

[0069] A formulation module, used to formulate appropriate inspection command strategies based on the GIS model;

[0070] A command module is used to give corresponding commands to a plurality of first inspection personnel in the inspection site based on the inspection command strategy.

[0071] Preferably, the formulation 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, an appropriate inspection command strategy is formulated.

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

[0075] Obtain multiple first formulation processes for manual inspection command strategy formulation;

[0076] Verifying a first reliability of the first formulation process and, at the same time, verifying a second reliability of a process source corresponding to the first formulation process;

[0077] When both pass the verification, the first formulation process is used as the second formulation process;

[0078] Integrate each of the second formulation processes to obtain training samples;

[0079] Based on a preset model training algorithm, model training is performed on the training samples to obtain an inspection command strategy formulation model.

[0080] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0081] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

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

[0084] Figure 2 The figure is a schematic diagram of a GIS-based intelligent inspection command system in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0086] The present invention provides a GIS-based intelligent inspection command method, such as Figure 1 As shown, 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 site information;

[0089] Step 3: Formulate appropriate inspection command strategies based on the GIS model;

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

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

[0092] Obtain on-site information of the inspection site that needs to be inspected and directed (for example: architectural information of the inspection site; the last inspection time, equipment location, model and operating data of each device at the inspection site; the current location of each inspector, whether the status information of the inspection task is being performed, etc.); Based on GIS technology, build a GIS model according to on-site information (for example: the location and status information of each device is displayed on the GIS model, etc.); Based on the GIS model, formulate appropriate inspection and command strategies (for example: if the operating data of a certain device is suspected to be abnormal, command the nearest inspector to inspect it; for example: if the time for another inspection of a certain device has been reached, command the nearest inspector to inspect it); Based on the inspection and command strategy, give corresponding commands to the first inspector at the inspection site (when giving commands, the inspection task can be sent to the smart terminal carried by the inspector, which can be a smart phone, tablet, etc.);

[0093] The embodiment of the present invention constructs a GIS model based on the on-site information of the inspection site, formulates an appropriate inspection command strategy based on the GIS model, and gives corresponding commands to the inspection personnel. There is no need to set up inspection command personnel at the inspection site, which reduces labor costs. In addition, it avoids the occurrence of problems such as incomplete and unreasonable command in manual inspection command.

[0094] The present invention provides a GIS-based intelligent inspection command method, step 3: based on the GIS model, formulate an appropriate inspection 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, an appropriate inspection command strategy is formulated.

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

[0098] When formulating inspection and command strategies based on the GIS model, we can use machine learning technology to learn how to manually formulate inspection and command strategies based on the GIS model, that is, to train the inspection and command strategy formulation model; based on the training inspection and command strategy formulation model, we can formulate appropriate inspection and command strategies according to the GIS model.

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

[0100] Obtain multiple first formulation processes for manual inspection command strategy formulation;

[0101] Verifying a first reliability of the first formulation process and, at the same time, verifying a second reliability of a process source corresponding to the first formulation process;

[0102] When both pass the verification, the first formulation process is used as the second formulation process;

[0103] Integrate each of the second formulation processes to obtain training samples;

[0104] Based on a preset model training algorithm, model training is performed on the training samples to obtain an inspection command strategy formulation model.

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

[0106] When training the inspection command strategy formulation model, the first formulation process of the inspection command strategy formulation using the artificial GIS model is obtained (which can be obtained based on big data technology), and the first formulation process is prepared to be learned using machine learning technology. However, the first formulation process can be directed by local staff or by staff at other inspection sites. Therefore, in order to ensure the training quality of the inspection command strategy formulation model and to ensure the accuracy of the subsequent formulation of the inspection command strategy, it is necessary to verify the reliability of the first formulation process. When verifying the reliability of the first formulation process, the reliability and first reliability of the first formulation process itself can be verified, and the reliability and second reliability of the process source corresponding to the first formulation process can also be verified. When all of them pass the verification, the corresponding second formulation process is integrated to obtain training samples. Based on a preset model training algorithm (for example, a machine learning algorithm), the training samples are trained (machine learning algorithms and model training using machine learning algorithms belong to the scope of existing technologies and will not be described in detail) to obtain the inspection command strategy formulation model.

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

[0108] Obtaining a formulater corresponding to the first formulation process;

[0109] Acquire a first experience value corresponding to the formulater, and at the same time, acquire a formulation weight of the formulater corresponding to the first formulation process;

[0110] Assigning the first experience value to the set weight corresponding to the first experience value to obtain a second experience value;

[0111] Accumulating and calculating the second experience values ​​to obtain a first reliability of the first formulation process;

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

[0113] Otherwise, failed.

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

[0115] When verifying the reliability of the first formulation process itself, we can start from the experience level of the formulater corresponding to the first formulation process; obtain the first experience value corresponding to the formulater, the larger the first experience value, the richer the command experience of the formulater, etc.; obtain the formulation weight of the formulater corresponding to the first formulation process, the larger the formulation weight, the greater the degree of participation and contribution of the formulater in the formulation of 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: the first experience value is 100, the formulation weight is 0.25, and the second experience value is 100*0.25=25); accumulate and calculate the second experience value to obtain the first reliability; when the first reliability is greater than or equal to the preset first reliability threshold, the verification passes, otherwise (the first reliability is less than the first reliability threshold), it fails.

[0116] The present invention provides a GIS-based intelligent inspection 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, determining a plurality of associated process sources associated with a process source corresponding to the first formulated process;

[0118] Acquire a first credit value corresponding to the associated process source, and at the same time, acquire an associated weight corresponding to an associated relationship between the associated process source and the process source;

[0119] Assigning the first credit value to the corresponding association weight to obtain a second credit value;

[0120] averaging the second credit values ​​to obtain a third credit value;

[0121] Obtaining a fourth credit value corresponding to the process source;

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

[0123] If the second reliability is greater than or equal to a preset second reliability threshold, the second reliability of the process source corresponding to the first formulation process passes verification;

[0124] Otherwise, failed.

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

[0126] When performing reliability verification on the process source of the first formulation process (for example, other inspection sites and big data service agencies, etc.), the fourth credit value can be obtained based on the historical credit situation of the process source (the authenticity of the formulation process provided historically, etc.), and a process source association library can be set up. Based on the process source association library, the associated process source associated with the process source is determined (for example, the inspection site comes from the same head office, etc.), and the first credit value is obtained based on the historical credit situation of the associated process source. The reliability of the process source of the first formulation process is comprehensively verified. In this way, when the process source has a bad credit, it will affect the associated process source associated with it. On the contrary, it increases the cost of bad credit of the process source production line, and indirectly improves the quality of the process provided by the process source. After obtaining the first credit value, obtain the associated weight corresponding to the association relationship between the associated process source and the process source (for example, the inspection site comes from the same head office, etc.). The closer the association relationship, the greater the association weight. Assign the first credit value corresponding to the association weight (multiply the two) to obtain the second credit value. Calculate the average value of the second credit value to obtain the third credit value. Calculate the sum of the third credit value and the fourth credit value to obtain the second reliability. If the second reliability is greater than or equal to the preset second reliability threshold, the verification passes, otherwise, it fails.

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

[0128] After directing the first inspection personnel, obtaining the first on-site equipment that the first inspection personnel is about to inspect in the inspection site;

[0129] Acquire the inspection experience value of the first inspection personnel corresponding to the first on-site equipment;

[0130] Obtaining an inspection experience value threshold and an inspection specification verification necessary value corresponding to the first field device;

[0131] If the inspection experience value is less than or equal to the inspection experience value threshold and / or the inspection specification verification necessary value is greater than or equal to the preset inspection specification verification necessary value threshold, obtaining a preset guidance vehicle dynamic distribution map, and determining the guidance vehicle closest to the first field device from the guidance vehicle dynamic distribution map;

[0132] Obtaining a docking node corresponding to the guiding vehicle, and docking with the guiding vehicle through the docking node;

[0133] After the docking is completed, the guidance vehicle is controlled to go to the first on-site device, and at the same time, the first inspection personnel are informed to wait for the guidance vehicle to arrive at the first on-site device before starting the inspection;

[0134] When the guidance vehicle arrives at the first on-site device, the guidance vehicle is controlled to dynamically collect three-dimensional information within a preset range of the surrounding area;

[0135] Based on the three-dimensional information, construct a three-dimensional model of the site corresponding to the range;

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

[0137] Acquiring the inspection behavior of the first inspection personnel collected by the guidance vehicle when dynamically controlling the behavior collection of the guidance vehicle;

[0138] Acquire a patrol irregular behavior library corresponding to the first field device, and match the patrol behavior with a first patrol irregular behavior in the patrol irregular behavior library;

[0139] If the match is met, the first patrol irregular behavior that matches the match is used as the second patrol irregular behavior, and at the same time, the first patrol inspector who produces the patrol behavior that matches the match is used as the second patrol inspector;

[0140] Obtaining a severity value corresponding to the second inspection irregular behavior;

[0141] If the severity value is less than or equal to a preset severity value threshold, obtaining a reminder method corresponding to the second inspection irregular behavior, and based on the reminder method, controlling the guidance vehicle to give a corresponding reminder to the second inspection personnel;

[0142] Otherwise, obtaining a response command strategy corresponding to the second inspection irregular behavior, and based on the response command strategy, controlling the guidance vehicle to give corresponding response command to the first inspection personnel;

[0143] At the same time, obtaining a dynamic information extraction strategy and a risk determination model corresponding to the second inspection irregular behavior;

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

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

[0146] If the risk value is greater than or equal to a preset risk threshold, the guidance vehicle is immediately controlled to lead the first inspection personnel to leave the inspection sub-site.

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

[0148] When inspecting some special equipment (for example, special gas equipment), it is necessary to strictly pay attention to the standardization of the inspection operation. If the inspection operation is not standardized, it may cause safety accidents (for example, the inspectors are "blindly confident", do not wear protective masks, perform special gas equipment maintenance operations, and inhale special gases), which will affect the inspectors. Some of the impacts are even irreversible, which will also affect the production of the enterprise. In addition, the inspectors have different levels of experience in inspecting different equipment. When inspectors inspect equipment they are not familiar with, they generally consult the corresponding inspection manual. However, the inspection manual does not record all inspection items. When encountering some problems that are not in the inspection manual (for example, oil seal leakage), if the inspectors perform maintenance on their own, it may affect the equipment. If you ask experienced inspectors, you may not be able to get answers in time (for example, experienced inspectors are performing inspection tasks; for example, only a small number of inspectors know about some special problems), which will reduce the efficiency of the inspection. Therefore, it is urgent to solve it.

[0149] When the first inspector inspects the first field equipment, the inspection experience value of the first inspector corresponding to the first field equipment is obtained (it can be determined based on the inspection time of the first inspector for the first field equipment in history, the number of problems encountered, etc.; the larger the inspection experience value, the richer the inspection experience of the first inspector for the first field equipment); the inspection experience value threshold corresponding to the first field equipment (indicating the experience level requirement of the inspector who inspects the first field equipment) and the inspection specification verification necessary value (indicating the necessary degree to which the inspection operation specification of the inspector needs to be verified when the first field equipment is inspected, for example: for special gas equipment, the inspection specification verification necessary 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 necessary value is greater than or equal to the inspection specification verification necessary value threshold, it means that the inspection operation specification monitoring of the first inspector is required;

[0150] When monitoring the inspection operation specifications of the first inspection personnel, it can be completed by the guidance vehicle, further reducing the labor cost; determine the nearest guidance vehicle from the preset guidance vehicle dynamic distribution map (virtual map, marked with the dynamic position of each guidance vehicle), dock with it through its corresponding docking node, and the controller goes to the first on-site equipment, and at the same time, informs the first inspection personnel to wait; when the guidance vehicle arrives at the scene, the guidance vehicle needs to collect the operation behavior generated by the first inspection personnel during the inspection of the first on-site equipment (a camera can be set on the guidance vehicle for collection);

[0151] However, in general, the first on-site equipment is more complicated (for example, other inspectors are inspecting other equipment, etc.), and it is necessary to ensure that the guidance car can continuously collect operational behaviors. Therefore, the guidance car is controlled to dynamically (timed) collect three-dimensional information within a preset range (for example, 3 meters) around it (a millimeter-wave radar sensor can be set on the car for collection), and a three-dimensional model of the scene is constructed based on the three-dimensional information. Based on the preset behavior collection dynamic control model (a pre-trained model used to control the guidance car to continuously collect operational behaviors generated by the inspection personnel according to the on-site situation, when controlling, for example, the inspection personnel operate with their hands, but turn their backs to the guidance car, and the guidance car is controlled to go to the front of the inspection personnel to collect hand behaviors), according to the on-site three-dimensional model, the inspection behavior of the first inspection personnel of the guidance car is dynamically controlled;

[0152] Next, it is necessary to confirm whether the inspection behavior is standardized. If it is not standardized, corresponding processing is carried out; the inspection behavior is matched with the first irregular inspection behavior in the irregular inspection behavior library corresponding to the first on-site equipment (for example, not wearing a protective mask). If the match is consistent, it means that the inspection behavior is not standardized, and the severity value corresponding to the second irregular inspection behavior that matches is obtained. The larger the severity value, the more verified the consequence. When the consequence is not serious, the corresponding reminder method is obtained (for example, voice broadcast "Please wear a protective mask immediately"), and based on the reminder method, the second inspection personnel are reminded. When the consequence is serious, the corresponding response command strategy for the second irregular inspection behavior is obtained (for example, when inspecting the valve, the special gas main valve is opened without closing the valve, resulting in special gas leakage, and the voice command inspection personnel to tighten the valve, and at the same time, the special gas chamber is evacuated), and the first inspection personnel are commanded to respond to the scene in a timely and effective manner;

[0153] However, when the consequences are serious, it may have an impact on the inspection personnel. When the impact is large, the inspection personnel need to be reminded to evacuate; obtain the dynamic information extraction strategy corresponding to the second inspection's irregular behavior (for example: extracting the special gas concentration in the special gas chamber) and the risk judgment model (a pre-trained model for risk judgment based on on-site information, for example: the higher the special gas concentration, the greater the risk); based on the dynamic information extraction strategy, dynamically extract target information from the on-site information (for example: the special gas concentration in the special gas chamber), and based on the risk judgment model, judge the risk situation according to the target information and obtain the risk value; once the risk value is greater than or equal to the preset risk threshold, it means that the risk is large, and the control guidance vehicle is controlled to lead the first inspection personnel to leave to ensure the safety of the first inspection personnel.

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

[0155] When the first inspection personnel inputs a guidance request when inspecting the second field device, an expert node set corresponding to the second field device is obtained, the expert node set including: a plurality of first expert nodes;

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

[0157] Traversing the first expert nodes in order from the highest to the lowest expertise;

[0158] Obtaining the node status corresponding to the first expert node traversed each time, the node status including: idle and busy;

[0159] When the node state corresponding to the first expert node traversed each time is idle, the corresponding first expert node is used as the second expert node;

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

[0161] Obtain the importance and execution progress corresponding to the task type of the task - the value library can be intervened;

[0162] Determining the intervenable value corresponding to the execution progress based on the execution progress-intervenable value library;

[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 is used as a second expert node;

[0164] The first inspection personnel is dynamically connected to the second expert node, and the second expert node guides the first inspection personnel to inspect the second on-site equipment.

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

[0166] When the first inspector inspects the second on-site equipment, he may encounter problems beyond his capabilities. In this case, multiple first expert nodes corresponding to the second on-site equipment are set. The first expert node corresponds to an inspector who has rich inspection experience on the second on-site equipment. The inspector can come from the current site or from other inspection sites. The most suitable first expert node is prepared to be selected from the first expert nodes to provide docking guidance to the first inspector. When selecting, the professionalism of the first expert node must be guaranteed first. Therefore, the professionalism of the first expert node corresponding to the second on-site equipment is obtained. The greater the professionalism, the richer the inspection experience for inspecting the second on-site equipment. The first expert nodes are traversed in order from large to small according to the professionalism, and the node status corresponding to the first expert node traversed each time is obtained. The node status is divided into idle (corresponding to the inspector's manual When the node status is idle, it is directly connected as the second expert node; 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 corresponding to the task-intervention value library (which stores the intervention value library corresponding to different execution progresses. The intervention value represents the degree to which the inspector can answer questions when the task is executed to the corresponding execution progress. For example, the task is about to end, and some finishing work is being performed, so questions can be answered); it obtains the importance corresponding to the task type. The greater 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 small and the intervention value is large, it means that it can intervene, and as the second expert node, it dynamically connects with the first inspector;

[0167] When the second expert node connects with the first inspector, the second expert node can answer 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] The present invention provides a GIS-based intelligent inspection command method, which also includes:

[0169] At preset time intervals, adaptively modifying the GIS model;

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

[0171] Obtaining operation data of the GIS model within a recent preset time period;

[0172] Performing feature extraction on the operation data to obtain a plurality of first features;

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

[0174] Based on the trigger value, the time interval is calculated, and the calculation formula is as follows:

[0175]

[0176] in, is the time interval, γ is the preset initial value of the time interval, J t is the tth trigger value, and Q is the total number of the trigger values.

[0177] The working principle and beneficial effects of the above technical solution are:

[0178] When the GIS model is in operation, operation anomalies may occur, such as incomplete information mapping, freezes and delays, etc. Therefore, it is necessary to regularly perform adaptive corrections to the GIS model (for example, correct incomplete information mapping, etc.); set up an operation trigger feature library, which stores some suspected operation anomalies of the GIS model during operation, and match the first feature of the GIS model operation data in the most recent preset time period (for example, 50 seconds) with the second feature therein. If the match is satisfactory, obtain the corresponding trigger value. The larger the trigger value, the greater the possibility of an abnormality, and the time interval needs to be lowered; the timeliness and efficiency of correction are improved.

[0179] The present invention provides a GIS-based intelligent inspection command system, such as Figure 2 As shown, including:

[0180] Acquisition module 1, used to acquire on-site information of the inspection site where inspection command is required;

[0181] Construction module 2, for constructing a GIS model corresponding to the inspection site according to the site information based on GIS technology;

[0182] A formulation module 3 is used to formulate an appropriate inspection command strategy based on the GIS model;

[0183] The command module 4 is used to give corresponding commands to a plurality of first inspection personnel in the inspection site based on the inspection command strategy.

[0184] The working principle and beneficial effects of the above technical solution have been explained in the method claims and will not be repeated here.

[0185] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A GIS-based intelligent inspection 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, a GIS model corresponding to the inspection site is constructed; Step 3: Develop appropriate inspection and command strategies based on the GIS model; Step 4: Based on the inspection command strategy, corresponding commands are given to multiple first inspection personnel in the inspection site.

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

3. The GIS-based intelligent inspection command method according to claim 2, characterized in that: Training inspection command strategy formulation model, including: Obtain multiple first formulation processes for manual inspection command strategy formulation; Verifying a first reliability of a first formulation process and, at the same time, verifying a second reliability of a process source corresponding to the first formulation process; When all of them pass the verification, the corresponding first formulation process will be used as the second formulation process; Integrate each second formulation process to obtain training samples; Based on the preset model training algorithm, the model is trained on the training samples to obtain the inspection command strategy formulation model.

4. The GIS-based intelligent inspection command method according to claim 3, characterized in that: Verify the first reliability of the first formulation process, including: Get the formulater corresponding to the first formulation process; Obtaining a first experience value corresponding to a formulater, and at the same time, obtaining a formulation weight of the formulater corresponding to the first formulation process; Assign the first experience value a corresponding weight, and obtain the second experience value; Accumulating and calculating the second experience values ​​to obtain a first reliability of the first formulation process; If the first reliability is greater than or equal to a preset first reliability threshold, the first reliability of the first formulation process is verified; Otherwise, failed.

5. The GIS-based intelligent inspection command method according to claim 3, characterized in that: Verifying a second reliability of a process source corresponding to the first formulation process includes: Based on a preset process source association library, determining a plurality of associated process sources associated with a corresponding process source of the first formulated process; Obtaining a first credit value corresponding to the associated process source, and at the same time, obtaining an associated weight corresponding to an associated relationship between the associated process source and the process source; Assigning a corresponding association weight to the first credit value to obtain a second credit value; averaging the second credit values ​​to obtain a third credit value; Obtain a fourth credit value corresponding to the process source; The third credit value and the fourth credit value are summed to obtain a second reliability of a process source corresponding to the first formulation process; If the second reliability is greater than or equal to a preset second reliability threshold, the second reliability of the process source corresponding to the first formulation process is verified; Otherwise, failed.

6. The GIS-based intelligent inspection command method according to claim 1, characterized in that: Also includes: When the first inspection personnel inputs a guidance request when inspecting the second field device, an expert node set corresponding to the second field device is obtained, the expert node set including: a plurality of first expert nodes; Acquire the expertise of the first expert node corresponding to the second field device; Traverse the first expert node in order from highest to lowest expertise; Obtain the node status corresponding to the first expert node traversed each time, the node status includes: idle and busy; When the node state corresponding to the first expert node traversed each time is idle, the corresponding first expert node is used as the second expert node; When the node status corresponding to the first expert node traversed each time is busy, the execution progress of the task being executed by the corresponding first expert node is obtained; Get the importance and execution progress of the task type corresponding to the task - the value library can be intervened; Based on the execution progress-intervention value library, determine the intervention value 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 is used as the second expert node; The first inspection personnel is dynamically connected to the second expert node, and the second expert node guides the first inspection personnel to inspect the second on-site equipment.

7. An intelligent inspection command system based on GIS, characterized in that: include: An acquisition module is used to acquire on-site information of the inspection site that needs to be inspected and directed; A construction module is used to construct a GIS model corresponding to the inspection site based on GIS technology and on-site information; A development module is used 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 in the inspection site based on the inspection command strategy.

8. The GIS-based intelligent inspection command system according to claim 7, characterized in that: The development module performs the following operations: Training inspection command strategy formulation model; Based on the inspection command strategy formulation model, formulate appropriate inspection command strategies according to the GIS model.

9. The GIS-based intelligent inspection command system according to claim 8, characterized in that: The development module performs the following operations: Obtain multiple first formulation processes for manual inspection command strategy formulation; Verifying a first reliability of a first formulation process and, at the same time, verifying a second reliability of a process source corresponding to the first formulation process; When all of them pass the verification, the corresponding first formulation process will be used as the second formulation process; Integrate each second formulation process to obtain training samples; Based on the preset model training algorithm, the model is trained on the training samples to obtain the inspection command strategy formulation model.

10. The GIS-based intelligent inspection command system according to claim 9, characterized in that: Verify the first reliability of the first formulation process, including: Get the formulater corresponding to the first formulation process; Obtaining a first experience value corresponding to a formulater, and at the same time, obtaining a formulation weight of the formulater corresponding to the first formulation process; Assign the first experience value a corresponding weight, and obtain the second experience value; Accumulating and calculating the second experience values ​​to obtain a first reliability of the first formulation process; If the first reliability is greater than or equal to a preset first reliability threshold, the first reliability of the first formulation process is verified; Otherwise, failed.

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