Sensor arrangement recommendation method, device and storage medium for building detection

Abstract

The application discloses a sensor arrangement recommendation method, device and storage medium for building detection, and relates to the technical field of data processing. The method comprises the following steps: determining a target sensor matched according to environmental information data and detection requirement data of a building; determining a basic matching rule set corresponding to each target sensor, and adjusting the basic matching rule set according to drawing data of the building to obtain a target rule set of each target sensor; determining a minimum sensor quantity of the building and an initial installation coordinate according to the target rule set to obtain an initial deployment position. The accuracy of sensor deployment can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a sensor arrangement recommendation method for building detection, a device and a storage medium. BACKGROUND

[0002] In building environment monitoring, security early warning, intelligent operation and maintenance and other scenarios, sensor deployment is the core link of building a perception network. By deploying various sensors in key areas of buildings, real-time collection, transmission and analysis of environmental parameters, security status and equipment operation data are realized, providing data support for subsequent fire alarm, environment regulation, equipment maintenance and other decisions.

[0003] In existing sensor deployment schemes, experience selection and general rule deployment modes are usually adopted. In the sensor selection stage, there is a lack of standardized multi-dimensional decision system, and the type of sensor is determined by relying on the past project experience of engineering and technical personnel. In the deployment rule application stage, industry general standards or specifications are directly applied without individual adjustment of deployment parameters in combination with the specific spatial structure of the building. This leads to poor adaptability of deployment rules to building scenarios, and easy formation of monitoring blind areas or waste of resources due to over deployment. SUMMARY

[0004] The main purpose of the present application is to provide a sensor arrangement recommendation method for building detection, a device and a storage medium, aiming to solve the technical problem of how to improve the accuracy of sensor deployment.

[0005] To solve the above problems, the present application provides a sensor arrangement recommendation method for building detection, which comprises:

[0006] determining a matching target sensor according to environmental information data and detection requirement data of the building;

[0007] determining a basic matching rule set corresponding to each target sensor, and adjusting the basic matching rule set according to drawing data of the building to obtain a target rule set of each target sensor;

[0008] determining the minimum number of sensors and the initial installation coordinates of the building according to the target rule set to obtain the initial deployment position.

[0009] In an embodiment, the step of determining a matching sensor type according to environmental information data and detection requirement data of the building comprises:

[0010] determining a matching target sensor according to environmental information data and detection requirement data of the building;

[0011] comparing the environment information data with preset working environment threshold values of the sensors, and determining the sensors whose environment information data is greater than or equal to the working environment threshold values as a second candidate sensor type set;

[0012] determining the target sensors according to an intersection of the first sensor candidate set and the second sensor candidate set.

[0013] In an embodiment, the step of determining the target sensor set according to the intersection of the first sensor candidate set and the second sensor candidate set comprises:

[0014] obtaining sensor precision, sensor cost, sensor energy consumption and effective working threshold interval of each sensor in the intersection;

[0015] comparing an average value corresponding to the effective working threshold interval with a preset safety region interval, and determining an environment fitness score according to a comparison result;

[0016] determining a sensor precision score according to a ratio of the sensor precision to a preset required precision;

[0017] determining a sensor cost score based on a ratio of the sensor cost to a preset minimum cost;

[0018] determining a sensor energy consumption score according to a ratio of the sensor energy consumption to a preset minimum energy consumption;

[0019] determining a total score as a sum of the environment fitness score, the sensor precision score, the sensor cost score and the sensor energy consumption score, and determining the target sensor as a sensor with the highest total score.

[0020] In an embodiment, the step of determining a basic matching rule set corresponding to each target sensor and adjusting the basic matching rule set according to building drawing data to obtain a target rule set of each target sensor comprises:

[0021] determining an associated basic deployment rule from a preset deployment rule library according to a type identifier of the target sensor, the basic deployment rule comprising an upper limit of sensor installation height, a lower limit of sensor installation height, a coverage range calculation model and a preset safety distance;

[0022] establishing a building three-dimensional space model according to the drawing data, obtaining a building height and a beam body lower edge height of a detection area based on the building three-dimensional space model, adjusting the upper limit of sensor installation height according to the building height, and adjusting the lower limit of sensor installation height according to the beam body lower edge height;

[0023] Adjust a preset coverage calculation model based on size data and a type of a blocking structure of a detection area obtained based on the three-dimensional space model of the building.

[0024] Obtain coordinates of an electrical equipment based on the three-dimensional space model of the building, and generate an installation prohibited area based on the coordinates of the electrical equipment and the preset safety distance.

[0025] In an embodiment, the step of adjusting a preset coverage calculation model based on size data and a type of a blocking structure of a detection area obtained based on the three-dimensional space model of the building comprises:

[0026] Determine a corresponding detection range superposition coefficient based on the size data;

[0027] Determine a blocking structure based on a coordinate range of a component in the three-dimensional space model of the building and a preset space collision detection algorithm, and adjust a blocking correction factor in the coverage calculation model based on a type of the blocking structure;

[0028] Determine an effective radius as a product of the blocking correction factor, the detection range superposition coefficient, and a preset reference detection radius, and adjust the coverage calculation model based on the effective radius.

[0029] In an embodiment, the step of determining a minimum number of sensors and an initial installation coordinate of the building according to the target rule set to obtain an initial deployment position comprises:

[0030] Divide a detection area into a three-dimensional grid according to a preset precision, and divide the three-dimensional grid into an effective monitoring grid and an installable grid according to the target rule set;

[0031] Iterate through the installable grid based on a preset greedy algorithm, and determine a number of coverage effective monitoring grids of the installable grid according to a preset installable grid and coverage grid mapping table;

[0032] Determine a target installable grid with the largest number of coverage effective monitoring grids as an installation position, and determine a midpoint coordinate of the target installable grid as the initial installation coordinate;

[0033] Determine a number of sensors with a coverage reaching a preset coverage threshold as the minimum number of sensors.

[0034] In an embodiment, the sensor arrangement recommendation method for building detection further comprises:

[0035] Obtain distances between the sensors and signal frequency coincidence degrees based on the initial deployment position;

[0036] input the distance and the signal frequency coincidence degree into a preset sensor interference model to generate an interference risk matrix;

[0037] adjust the initial deployment position according to the interference risk matrix and a preset conflict position adjustment rule to obtain a target deployment position set.

[0038] In an embodiment, the step of inputting the distance and the signal frequency coincidence degree into a preset sensor interference model to generate an interference risk matrix comprises:

[0039] determining a distance contribution degree according to a ratio of the distance and a sensor signal transmission limit distance, and performing weighted summation on the distance contribution degree and the frequency coincidence degree to obtain a risk score;

[0040] determining a risk level according to the risk score and a preset risk level division threshold;

[0041] generating the interference risk matrix based on the risk level and a sensor identifier.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a sensor arrangement recommendation device for building detection, the device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the sensor arrangement recommendation method for building detection as described above.

[0043] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the sensor arrangement recommendation method for building detection as described above.

[0044] The present application provides a sensor arrangement recommendation method for building detection, which ensures that the target sensor meets functional adaptability, environmental compatibility and spatial adaptability through a two-dimensional screening logic of environmental information data and detection requirement data. Based on building drawing data, the basic matching rule set is dynamically adjusted to convert the general deployment specification into a quantitative constraint that fits the specific building structure. The adjusted target rule set adapts to the building space features, so that the deployment scheme is upgraded from generalization to customization, providing accurate basis for subsequent precise deployment. When the building monitoring requirements are added or the environmental conditions change, the existing screening logic and rule adjustment method can be quickly reused to add or adjust the sensor deployment, without the need to reconstruct the entire deployment scheme, thereby improving the expansion efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the application and, together with the description, function to explain the principles of the application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the field, based on these drawings, other drawings can also be obtained without creative labor.

[0047] Figure 1 The first flowchart provided for the sensor arrangement recommendation method for building detection of the present application;

[0048] Figure 2 The second flowchart provided for the sensor arrangement recommendation method for building detection of the present application;

[0049] Figure 3 The structural diagram of the hardware running environment involved in the sensor arrangement recommendation method for building detection in the embodiments of the present application.

[0050] The purpose implementation, functional features and advantages of the present application will be further explained with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0052] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings and specific embodiments of the specification.

[0053] To achieve the above purpose, the present application provides a sensor arrangement recommendation method for building detection, which comprises: determining the matching target sensor according to the environmental information data and the detection requirement data of the building; determining the basic matching rule set corresponding to each target sensor, and adjusting the basic matching rule set according to the drawing data of the building to obtain the target rule set of each target sensor; determining the minimum sensor quantity of the building and the initial installation coordinate according to the target rule set, and obtaining the initial deployment position.

[0054] In the scenes of building environment monitoring, security early warning, intelligent operation and maintenance, sensor deployment is the core link of building perception network. By deploying various sensors in key areas of the building, real-time collection, transmission and analysis of environmental parameters, safety state and equipment operation data are realized, which provides data support for subsequent fire alarm, environment control, equipment maintenance and other decisions.

[0055] In existing sensor deployment solutions, the mode of experience selection and general rule deployment is usually adopted. In the sensor selection stage, there is a lack of standardized multi-dimensional decision system, and the type of sensor is determined depending on the past project experience of engineering technicians. In the application stage of deployment rules, the industry general standards or specifications are directly applied without individual adjustment of deployment parameters in combination with the specific spatial structure of the building. This leads to poor adaptability of deployment rules to building scenarios, and easy formation of monitoring blind area or waste of over-deployed resources.

[0056] The present application provides a sensor arrangement recommendation method for building detection, which ensures that the target sensor meets the functional adaptability, environmental compatibility and spatial adaptability through the double-dimensional screening logic of environmental information data and detection demand data. The basic matching rule set is dynamically adjusted based on the building drawing data, and the general deployment specification is converted into a quantitative constraint that fits the specific building structure. The adjusted target rule set adapts to the spatial characteristics of the building, so that the deployment scheme is upgraded from generalization to customization, providing accurate basis for subsequent accurate deployment. When the building monitoring demand is added or the environmental conditions change, the existing screening logic and rule adjustment method can be quickly reused to add or adjust the sensor deployment, without the need to reconstruct the entire deployment scheme, thereby improving the expansion efficiency.

[0057] It should be noted that the execution subject of the present embodiment can be a computing service device with network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device or apparatus capable of realizing the above functions. The following takes a sensor arrangement recommendation device for building detection as an example to describe the present embodiment and each of the following embodiments.

[0058] Based on this, the present application provides a sensor arrangement recommendation method for building detection, which is described with reference to Figure 1 , Figure 1 The flowchart of the first embodiment of the sensor arrangement recommendation method for building detection of the present application is shown in FIG. 1.

[0059] In the present embodiment, the sensor arrangement recommendation method for building detection comprises steps S10-S30:

[0060] Step S10, determining the matching target sensor according to the environmental information data and the detection demand data of the building.

[0061] In the present embodiment, the environmental information data includes environmental parameters such as temperature value, humidity value, light intensity, dust concentration, noise level, etc.; the monitoring demand data includes monitoring targets such as fire alarm, temperature and humidity monitoring, security monitoring, etc., and the monitoring accuracy threshold and data transmission delay requirement corresponding to each monitoring target.

[0062] For environmental information data, parameter values in different units are converted to standard units, string type parameter values are converted to floating point numerical values, and timestamps are converted to standard time formats; grouped by region ID-parameter type, time series data sets are generated. Traverse each parameter's time series data, compare the system's preset reasonable range threshold, mark the values outside the range as range outliers, and record the abnormal position. For data that passes the range check, calculate the statistical characteristics of the parameter, mean μ, and standard deviation σ; mark outliers according to the 3σ principle, if the parameter value satisfies |v-μ|>3σ, it is determined as a statistical outlier; merge range outliers and statistical outliers to generate an outlier list; remove outliers from the data set and retain normal data.

[0063] For detection requirement data, natural language processing techniques are used to segment the original requirement text, and monitoring targets (such as fire, temperature and humidity, security, and dust) and monitoring objects (such as buildings, conference rooms, warehouses, and workshops) are extracted according to the segmentation results. Function requirements (such as alarm, monitoring, ensuring no dead angles, and whether exceeding the standard). Based on keyword matching, identify the demand type; according to the monitoring object keyword, map to the specific area in the building three-dimensional model.

[0064] According to the demand type, match the corresponding monitoring parameter type and corresponding threshold from the demand index mapping rule library, such as smoke concentration and temperature for fire monitoring requirements, temperature and relative humidity for temperature and humidity comfort requirements, video coverage rate and human body sensing signal for security monitoring requirements, PM2.5 concentration and total suspended particulate matter concentration for dust over-standard monitoring. If the demand is not clear about the parameter type, automatically supplement according to the industry standard. According to the structure of demand ID-monitoring area-monitoring target-parameter type-quantitative threshold-capture frequency, all transformed quantitative indicators are sorted out.

[0065] In a possible implementation, step S10 can include steps S11-S13:

[0066] Step S11, match the detection requirement data with the preset sensor function rule library to determine the first candidate sensor corresponding to the detection requirement data.

[0067] In this embodiment, the sensor function rule library is generated in advance according to the attribute information of each sensing type, and the sensor function rule library includes data such as sensor type ID, sensor name, matching monitoring target, adaptive parameter, working environment threshold, and the like. For example, the sensor type ID is S001, the sensor name is a photoelectric smoke sensor, the matching monitoring target is fire alarm, the adaptive parameter is smoke density, and the working environment threshold is -10~60℃ / 0~95%RH. The monitoring target and the core parameter in the detection requirement data are taken as the retrieval keywords, and the matching is performed based on the matching algorithm and the sensors in the sensor function rule library. For example, the monitoring target is fire alarm, and the core parameter is smoke density.

[0068] Optionally, the matching algorithm can be exact matching or fuzzy matching. When the monitoring target and the adaptive parameter are clear in the detection requirement data, exact matching is performed, the monitoring target and the adaptive parameter are directly matched with the matching rule library, and the first candidate sensor type set is obtained. When the monitoring target and the adaptive parameter are not clear in the detection requirement data, fuzzy matching is performed. For example, the monitoring target is temperature and humidity comfort, and the parameter is not clear. The rule engine automatically associates the sensor with the adaptive parameter = temperature + humidity. It is checked whether the matched sensor can cover all the core parameters in the requirement. The sensor whose monitoring accuracy does not meet the minimum accuracy requirement of the requirement is removed. The sensor types that pass the verification are integrated to generate the first sensor candidate set, so as to ensure that the function of the sensor matches the requirement.

[0069] In step S12, the environment information data is compared with the preset working environment threshold of the sensor. The sensor whose environment information data is greater than or equal to the working environment threshold is determined as the second candidate sensor.

[0070] In step S13, the target sensor is determined according to the intersection of the first candidate sensor and the second candidate sensor.

[0071] In this embodiment, the working environment threshold of each sensor is extracted from the sensor attribute table. The working environment threshold can be the working temperature range, the working humidity range, the dust resistance level, and the like. The environment statistical characteristics of the region are obtained from the region environment characteristic mapping table according to the monitoring region corresponding to the requirement. If the region environment parameter is greater than or equal to the sensor working threshold, the sensor is directly excluded. The sensors that pass the determination are scored according to the degree of fit between the environment parameter and the working threshold. For example, the working temperature of the sensor is 10~60℃, the region temperature is 28℃, the degree of fit is 100 points, the region temperature is 58℃, and the degree of fit is 80 points. The second sensor candidate set is obtained, and it is ensured that the sensor can stably work in the environment of the target region.

[0072] Optionally, when determining the environment adaptation fitness score, the effective working threshold interval of the sensor parameter, the minimum value Min and the maximum value Max, and the statistical characteristic value of the parameter in the region are obtained. The statistical characteristic value can be the mean value. The sensor working threshold interval is divided into a safe region and a boundary buffer region. The safe region is the core range after removing the buffer segments at both ends, and the sensor works most stably when the parameter falls within this region; the boundary buffer region is the edge part of the threshold interval, and the sensor can still work in this region but the stability decreases. The buffer segment length is set to 10% of the total length of the threshold interval according to the general rule. The score is calculated according to the falling point position of the regional environment parameter. If the statistical characteristic X of the parameter falls within the safe region, the fitness score is full marks; if it falls within the lower limit buffer region, i.e. Min≤X<Min plus the buffer segment length, the score is based on the preset score, for example 60 points, multiplied by the ratio of (X-Min) to the buffer segment length, and the result of the difference between the full score and the preset score is added to the score. The score linearly increases as the V approaches the safe region; if X is in the upper limit buffer region, i.e. Max minus the buffer segment length<X≤Max, the score is based on the preset score, multiplied by the ratio of (Max-X) to the buffer segment length, and the result of the difference between the full score and the preset score is added to the score. The score linearly increases as X approaches the safe region; if X is less than Min or greater than Max, it is directly determined as incompatible, and the sensor is eliminated.

[0073] The sensors that exist in both the first sensor candidate set and the second sensor candidate set are determined as target sensors. Optionally, after determining the intersection of the first sensor candidate set and the second sensor candidate set, the index scores of each sensor can also be determined based on the monitoring accuracy fitness, environmental adaptability, cost and energy consumption. The monitoring accuracy fitness is the matching degree of the sensor accuracy and the required accuracy. If the sensor accuracy≤required accuracy, the preset monitoring accuracy full score is obtained; if the sensor accuracy>required accuracy, the required accuracy / sensor accuracy×monitoring accuracy full score is calculated. The environmental adaptability is a direct mapping of the environmental adaptation score, and the environmental adaptation score×environmental adaptability weight is the environmental adaptability score. The cost is the economy of the sensor unit price, and the lowest cost in the intersection of the first sensor candidate set and the second sensor candidate set is taken as the benchmark, and the score is calculated by (lowest cost / sensor cost)×cost weight. The energy consumption is the economy of the sensor working energy consumption, and the lowest energy consumption in the intersection of the first sensor candidate set and the second sensor candidate set is taken as the benchmark, and the score is calculated by (lowest energy consumption / sensor energy consumption)×energy consumption weight. The sum of the monitoring accuracy fitness score, the environmental adaptability score, the cost score and the energy consumption score is taken as the total score. The target sensor type with the highest score is selected as the target sensor in descending order of the total score; if there is a same score, the monitoring accuracy fitness>environmental adaptability>cost>energy consumption is prioritized for secondary sorting, and the adapted target sensor type list is obtained.

[0074] Step S20, determine the basic matching rule set corresponding to each target sensor, and adjust the basic matching rule set according to the drawing data of the building, to obtain the target rule set of each target sensor.

[0075] In this embodiment, the building drawing data can be in CAD format or BIM model file, containing wall coordinates, beam column coordinates, floor height, room layout, door and window position, ventilation duct direction and other spatial structure data. The building drawing data is parsed into structured data, and key information such as spatial coordinates and structure size is extracted to establish a three-dimensional space model of the building. If the building drawing data is in CAD format, a CAD file parsing engine is called, such as an OpenCASCADE or Teigha-based parsing library, to read the layer information of a.dwg / .dxf file, filter non-structural layers such as annotation layers and auxiliary line layers; extract geometric data according to component type, for walls, extract the start point and end point coordinates (X, Y) of the polyline / straight line, combine with the height information of the section view to generate the three-dimensional coordinate range of the wall, and extract the wall thickness and material information; for beams and columns, extract the boundary coordinates of rectangles / circles to generate the three-dimensional bounding box of the beams and columns, and record the cross-sectional size and quantity; for doors and windows, extract the coordinate range of the door and window contour, mark the door and window type (such as single door, sliding window), opening direction and hole size; extract the ground elevation of each floor through the section view, distribute the plan components according to the floor to form layered structure data. If the building drawing data is a BIM model file, a BIM parsing library such as IfcOpenShell or RevitAPI is called to read the IFC standard data structure of the file, such as IfcWall, IfcBeam, IfcSlab and other component classes; for geometric properties, get the absolute coordinates through the "LocalPlacement" of the component, and get the three-dimensional geometric shape through the "Representation", and convert it to a standardized coordinate range (Xmin, Xmax, Ymin, Ymax, Zmin, Zmax); for non-geometric properties, extract component name, material, thickness, span, installation height and other attributes; extract room (IfcSpace) information, divide the room range through the component boundary, and record the room name, area, ceiling height and ground elevation. Convert all component coordinates to a preset global coordinate system, and map custom component types in CAD / BIM to built-in type library, such as: concrete shear wall to wall, I-shaped steel beam to beam, to ensure uniform component classification;

[0076] The extracted geometric information and attribute information are organized as key-value pair structures and structured according to the hierarchical relationship of buildings, floors, rooms and components. A three-dimensional space skeleton is constructed in units of floors, including the elevation range and room layout boundary of each floor; a visual geometric model is generated for each component, binding its coordinate range and attribute data; a topological association between components is established, such as that the wall W001 is adjacent to the door M001, and the beam B002 is supported on the top of the column C003, and the occlusion relationship is marked; it is checked whether the component coordinates overlap or whether there are floating components, and a verification report is output and minor conflicts are automatically corrected, so as to establish a three-dimensional space model of the building.

[0077] Key deployment-related parameters such as detection range, installation height requirement and signal transmission distance are extracted from sensor technology parameters; according to the sensor type, the corresponding basic deployment rules are called in the deployment rule library, such as that a photoelectric smoke sensor needs to avoid air vents and beam bodies; combined with the three-dimensional space model of the building, the parameters of the basic deployment rules are adjusted, such as adjusting the sensor installation height threshold for high floors and adjusting the detection range overlap coefficient for large-span spaces. The adjusted deployment rules are converted into quantitative constraint conditions to obtain a set of individualized deployment rules for the target sensor. Based on the sensor technology characteristics and the building space structure, the general deployment rules are converted into individualized and calculable constraint conditions, which provide a basis for subsequent deployment position calculation.

[0078] Step S30, determining the minimum number of sensors and the initial installation coordinates of the building according to the target rule set to obtain an initial deployment position set.

[0079] In this embodiment, the physical structure and environmental constraints of the building are taken as input conditions, the sensor type adaptation is completed through a pre-defined rule library, and then the spatial geometric calculation and the interference model between sensors are combined to output a globally optimal sensor deployment scheme, ensuring that the deployment position meets the monitoring coverage, data acquisition accuracy and equipment compatibility requirements.

[0080] In a feasible real-time manner, step S30 includes steps S31-S34:

[0081] Step S31, dividing the detection area into a three-dimensional grid according to a preset accuracy, and dividing the three-dimensional grid into effective monitoring grids and installable grids according to the target rule set.

[0082] Step S32, traversing the installable grid based on a preset greedy algorithm, and determining the number of covered effective monitoring grids of the installable grid according to a preset installable grid and coverage grid mapping table.

[0083] In this embodiment, the monitoring area is divided into a three-dimensional grid according to a preset accuracy, each grid is assigned a unique ID and the center point coordinates are recorded; combined with the installation prohibition conditions and the component blocking data, the effective monitoring grid and the installable grid are marked, the effective monitoring grid is the grid that needs to be covered, the installable grid is the grid that meets the coordinate constraints, distance constraints and has no prohibition, the grids that cannot be monitored or installed such as walls, beams and columns are removed, and the effective monitoring grid set and the installable grid set are generated. According to the target rule set, the effective detection radius R effective corresponding to each installable grid is calculated; the installable grid and the covered grid mapping table is generated in advance, that is, when each installable grid is used as a sensor installation position, the list of effective monitoring grids that can be covered and the coverage effectiveness.

[0084] In an alternative embodiment, in this embodiment, the component blocking data in the building three-dimensional space model is read, and the three-dimensional coordinate range of the unmonitorable components such as walls, beams, columns and pipes is extracted; for each grid, it is judged by a space collision detection algorithm whether it overlaps with the coordinate range of the unmonitorable components: if it overlaps, it is marked as an unmonitorable grid; if it does not overlap and falls completely within the effective space of the monitoring area, it is marked as an effective monitoring grid; the effective monitoring grids are integrated to generate an effective monitoring grid set containing grid ID, center point coordinates, coordinate range, whether coverage is needed and other attributes.

[0085] The quantitative coordinate constraints in the target rule set are extracted, and the center point coordinates of each effective monitoring grid are checked to remove the grids whose coordinates exceed the constraint range; the prohibited installation coordinate set in the target rule set is read, and it is judged by space collision detection whether the grid falls within the prohibited area to remove the prohibited installation grid; the distance constraint in the target rule set is extracted, such as the distance from the air vent ≥1.5m, the center point coordinates of the air vent are obtained from the building three-dimensional space model, the spatial distance between the center point of each candidate grid and the center point of the air vent is calculated, and the grid whose distance is less than 1.5m is removed; the installation size in the sensor technical parameters is read to judge whether the physical space corresponding to the grid can accommodate the sensor, and the grid whose space is insufficient is removed; the installable grid set is generated, containing grid ID, center point coordinates, coordinate range, list of constraint conditions met and other attributes.

[0086] The core parameters of the coverage range calculation model are extracted from the target rule set: the sensor reference detection radius R base , the detection range superposition coefficient K overlay ; the component blocking data around each installable grid is extracted from the building three-dimensional space model to determine the blocking correction factor K occlude . According to the formula R effective =R base ×K overlay ×K occludeCalculate the corresponding effective detection radius for each installable grid. Update the installable grid set with the R effective Bind with the grid ID, update the installable grid set, add the effective detection radius attribute.

[0087] In this embodiment, through the coverage optimization logic of the greedy algorithm, the optimal installation position is selected under the constraint of the target rule set and the goal of maximizing coverage efficiency, ensuring that global effective coverage is achieved with the least number of sensors. Compared with uniform distribution or empirical deployment methods, the number of sensor deployments can be reduced, significantly reducing equipment procurement, installation and post-operation and maintenance costs.

[0088] Step S33, determine the target installable grid with the most number of coverage effective monitoring grids as the installation position, and determine the midpoint coordinates of the target installable grid as the initial installation coordinates.

[0089] Step S34, determine the number of sensors with coverage reaching the preset coverage threshold as the minimum sensor number.

[0090] In this embodiment, the coverage determination rule is defined in advance. For a certain installable grid S, the center point coordinates (Xs, Ys, Zs), and the effective detection radius R effective If the center point coordinates (Xm, Ym, Zm) of the effective monitoring grid M satisfy the spatial distance formula √[(Xm-Xs)²+(Ym-Ys)²+(Zm-Zs)²]≤R effective , then determine M as the coverage grid of S. Traverse each grid S in the installable grid set to obtain its center point coordinates and effective detection radius; traverse each grid M in the effective monitoring grid set to obtain its center point coordinates and calculate the spatial distance between them; if the distance ≤ effective detection radius, then determine M as the coverage grid of S, record the grid ID of M, and mark the coverage effectiveness; for each installable grid S, integrate the IDs and coverage effectiveness of all its coverage grids to form a mapping record. Remove the installable grids without coverage grids in the mapping table, sort them by installable grid ID, and generate a structured installable grid and coverage grid mapping table.

[0091] Determine the minimum number of sensors based on the greedy algorithm, set the covered grid set to empty, the selected installation grid set to empty, the remaining to be covered grid set = the effective monitoring grid set, and the sensor number count N = 0. Traverse all unselected grids in the installable grid set, query the installable grid and covered grid mapping table, calculate the number of remaining to be covered grids that each grid can cover, i.e. the number of grids that can be newly covered at this location, select the installable grid with the most newly covered grids as the optimal installation location, and add it to the selected installation grid set. Move the grids covered by this location from the remaining to be covered grid set to the covered grid set, and update the installable grid and covered grid mapping table. Repeat the above steps until the remaining to be covered grid set is empty or the coverage reaches the preset threshold, at which point the sensor number count N is the determined minimum number of sensors. Extract the center point coordinates of each grid from the selected installation grid set as the initial installation coordinates of the sensors.

[0092] In this embodiment, through the double-dimensional screening logic of environmental information data and detection requirement data, the target sensor is ensured to meet the functional adaptability, environmental compatibility and spatial adaptability. Based on the building drawing data, the basic matching rule set is dynamically adjusted, and the general deployment specification is converted into a quantitative constraint that fits the specific building structure. The adjusted target rule set adapts to the building space features, so that the deployment scheme is upgraded from generalization to customization, providing accurate basis for subsequent accurate deployment. When the building monitoring requirements are added or the environmental conditions change, the existing screening logic and rule adjustment method can be quickly reused to add or adjust the sensor deployment, without the need to reconstruct the entire deployment scheme, improving the expansion efficiency.

[0093] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above-mentioned first embodiment can refer to the above introduction, and the following will not be repeated. On this basis, please refer to Figure 2 , step S20 can include steps S21-S24:

[0094] Step S21, according to the type identifier of the target sensor, determine the associated basic deployment rule from the preset deployment rule library, the basic deployment rule includes the upper limit of sensor installation height, the lower limit of sensor installation height, the coverage range calculation model and the preset safety distance.

[0095] In this embodiment, the preset deployment related parameter field library includes detection range, installation constraints (upper and lower limits of installation height, installation method adaptation type, minimum space size required for installation), signal transmission characteristics (wireless / wired transmission distance, signal penetration capability, anti-interference threshold), physical properties (sensor volume, weight, mounting hole size), etc. The sensor technology parameter set is traversed, and the values or range descriptions corresponding to the above-mentioned preset fields are extracted. For non-standardized expressions, they are parsed into quantitative value constraints; for range type parameters, the middle value is taken or a reference value is determined according to environmental adaptation requirements; for classification attributes, they are converted into keyword labels for subsequent rule matching. Finally, a structured sensor core parameter table is formed, which is associated with the sensor ID, and the value, unit and constraint type of each parameter are clearly defined.

[0096] The type identifier of the sensor is used as a retrieval condition to call the adapted basic deployment rule from the built-in deployment rule library. The built-in deployment rule library of the system is stored in categories according to the sensor type, and each rule contains structured information such as rule ID, applicable sensor type, rule description, constraint type (space constraint, distance constraint, environmental constraint, etc.), default parameter, and associated core parameter field. According to the sensor type, a primary search is performed to filter out the basic rule set commonly used for sensors of this type, such as the rules for photoelectric smoke sensors to avoid air vents, avoid beam body obstruction, and installation height ≤0.5m from the ceiling. Combined with the sensor core parameters, a secondary screening is performed to eliminate rules that are incompatible with the current sensor parameters, such as a sensor detection radius of only 3 meters, eliminating coverage range overlapping rules that require a detection radius ≥5 meters. At the same time, the rule library will mark the priority of each rule, such as safety class rules with higher priority than optimization class rules, to ensure that high-priority rules are matched and called first. Finally, an association table of sensor ID and matched basic rule set is generated, and each rule clearly defines the corresponding constraint type and default parameter, laying the foundation for subsequent personalized adjustment.

[0097] In step S22, a three-dimensional space model of the building is established according to the drawing data, the building height and the beam bottom height of the detection area are obtained based on the three-dimensional space model of the building, the upper limit of the sensor installation height is adjusted according to the building height, and the lower limit of the sensor installation height is adjusted according to the beam bottom height.

[0098] In step S23, the size data and the shielding construction type of the detection area are obtained based on the three-dimensional space model of the building, and the preset coverage range calculation model is adjusted.

[0099] In step S24, the coordinates of the electrical equipment are obtained based on the three-dimensional space model of the building, and the prohibited installation area is generated according to the coordinates of the electrical equipment and the preset safety distance.

[0100] In this embodiment, a three-dimensional space model of the building is established based on the drawing data of the building, and general basic rules are combined with specific features of the three-dimensional space model of the building to realize dynamic adaptation of rule parameters, so that the rules conform to the spatial structure and layout characteristics of a specific area.

[0101] The features of the monitoring area corresponding to the current sensor are extracted from the three-dimensional space model of the building, including the three-dimensional coordinate range of the area, the specific position and size of the ceiling, wall, beam, ventilation opening and pipeline, the distribution of doors and windows, and the available area of the installation surface. For each matched basic rule, the parameters are adjusted according to the constraint type: for the installation height rule, if the detection area is a high floor and the upper limit of the installation height in the sensor core parameters is lower than 80% of the ceiling height, the upper limit of the installation height is reset to 90% of the ceiling height, while ensuring that it does not exceed the allowed range of sensor performance; if there is a protruding beam in the area, the lower limit of the installation height is adjusted to be below the height of the lower edge of the beam to avoid conflict between the installation position and the beam. For the coverage range related rule, if the monitoring area is a large-span space, the detection range overlap coefficient is adjusted, and a supplementary rule is set that the coverage overlap rate of adjacent sensors is greater than or equal to 15% to avoid blind spots in monitoring; if there are many wall partitions in the area and the sensor signal penetration ability is weak, the single sensor coverage range calculation benchmark is reduced and the coverage density related rule is increased. For the distance constraint rule, the distance threshold is adjusted in combination with the actual size of the ventilation opening and the wind speed parameter: if the ventilation opening wind speed is greater than or equal to 2 m / s, the default distance greater than or equal to 1 m is adjusted to a distance greater than or equal to 1.5 m; if there are many ventilation openings and they are densely distributed, the distance threshold is further expanded to 2 m to ensure that the sensor is not affected by the airflow. For the installation prohibition rule, the location of electrical equipment recorded in the three-dimensional space model of the building is combined to supplement the constraint.

[0102] The adjusted personalized rules are converted into calculable quantitative expressions, coordinate constraints, mathematical models and other forms to ensure that the subsequent deployment position calculation can be directly called.

[0103] For spatial coordinate constraints, the three-dimensional coordinate interval is generated in combination with the coordinate range of the monitoring area in the three-dimensional space model of the building and the sensor installation constraint. First, the effective installation space of the monitoring area is determined, and then the coordinate limits corresponding to the installation height and installation method of the sensor are superimposed to finally form the quantitative coordinate constraint, which clearly defines the spatial boundary of the installation position.

[0104] For distance constraints, the rule of avoiding certain types of components is converted into a spatial distance formula. First, the three-dimensional coordinate range of the component is extracted, the geometric center coordinate is calculated, and then the distance formula that the sensor installation coordinate (X, Y, Z) needs to satisfy is generated according to the adjusted distance threshold D: √[(X-X0)²+(Y-Y0)²+(Z-Z0)²]≥D. If there are multiple components of the same type, multiple distance constraint formulas are generated, and the intersection of all constraints is taken as the final distance requirement.

[0105] In an implementable embodiment, step S23 can include: determining a corresponding detection range superposition coefficient based on the size data; determining an occlusion component based on the coordinate range of the component in the three-dimensional space model of the building and a preset spatial collision detection algorithm, and adjusting an occlusion correction factor in the coverage range calculation model based on the construction type of the occlusion component; determining a product of the occlusion correction factor, the detection range superposition coefficient and a preset reference detection radius as an effective radius, and adjusting the coverage range calculation model based on the effective radius.

[0106] In the embodiment, a three-dimensional spherical coverage model is constructed with the sensor installation coordinate as the core, and the core parameter is the effective detection radius; the sensor installation three-dimensional coordinate is set as (Xs, Ys, Zs), and the basic coverage range of the sensor is a spherical space with (Xs, Ys, Zs) as the center and the radius R as the effective detection radius; for any point (X, Y, Z) in the monitoring area, if the point satisfies the formula corresponding to the coverage range calculation model:

[0107] , the point is covered by the sensor.

[0108] Effective detection radius calculation formula R effective =R base *K overlay *K occlude , wherein R effective is the actual effective detection radius of the sensor; R base is the reference detection radius in the technical parameters of the sensor; K overlay is the personalized adjusted detection range superposition coefficient, such as 1.2 for large-span space and 0.8 for wall-intensive area; K occlude is the occlusion correction factor, 1.0 for no occlusion, 0.7 for weak occlusion, and 0.3 for strong occlusion, and the occlusion correction factor is determined based on the occlusion type of the component in the three-dimensional space model of the building.

[0109] Combined with the distribution of components (wall, beam, pipeline, etc.) in the three-dimensional model of the building, a shielding correction factor is introduced to quantify the weakening effect of shielding on the detection range. By traversing all components between the sensor and the monitoring point using the component coordinate range of the three-dimensional model of the building and combining the spatial collision detection algorithm, the shielding type is determined and the corresponding correction factor K occlude is matched; it is judged whether there is a shielding component between the sensor installation position (Xs, Ys, Zs) and the monitoring point (X, Y, Z), and the correction factor value is determined according to the type of the shielding component. In the case of no shielding, the correction factor K occlude = 1.0; when the type is glass or thin wall, it is considered as weak shielding, K occlude = 0.7; concrete wall and thick beam are strong shielding, the correction factor K occlude = 0.3; the effective detection radius after correction: R effective = R × K occlude . In the shielding scenario, R effective is used instead of the basic radius R for coverage determination.

[0110] The length, width, and height dimension data of the monitoring area are extracted from the three-dimensional model of the building. The maximum value of the length and width is located as the spatial span L. A large-span space judgment threshold is preset, such as L ≥ 20m is marked as a large-span space. The default overlap coefficient K_default of the sensor type in the deployment rule library is read; if the area is a large-span space, it is adjusted according to the span classification, when L ∈ [20m, 30m], K overlay = K_default × 1.2; when L ∈ [30m, 40m], K overlay = K_default × 1.3; when L > 40m, K overlay = K_default × 1.4, to ensure that the effective detection range is expanded to cover large spaces. If the area is a regular space (L < 20m), keep K overlay = K_default; if the area is a narrow space (L < 10m), K overlay = K_default × 0.9, to avoid excessive coverage and waste of resources.

[0111] For installation prohibition conditions, the rules that prohibit installation in certain positions are converted into a quantitative prohibited coordinate set. By extracting the three-dimensional coordinate range of the taboo area, the coordinate interval of prohibited installation is generated, and is marked as the prohibited installation coordinate set.

[0112] After the constraint condition quantification is completed, all rules are classified and integrated according to constraint types and rule priorities to form a personalized deployment rule set for the target sensor type. The rule set is organized in a structured format and includes sensor ID, applicable monitoring area ID, quantified constraint condition set (coordinate constraint, distance constraint, coverage constraint, etc., each constraint contains a quantified expression and an applicable range), coverage range calculation model (including reference parameters, correction coefficients, and overlap rate requirements), installation taboo conditions (inhibited installation coordinate set and taboo component type), and the like.

[0113] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as the above-mentioned first embodiment can be referred to the above description, and the subsequent will not be described in detail. On this basis, please refer to Figure 2 , after step S30, steps A10-A30 can also be included:

[0114] Step A10, based on the initial deployment position, the distance between each sensor and the signal frequency coincidence degree are obtained.

[0115] In this embodiment, the global three-dimensional installation coordinates of all sensors are extracted from the initial deployment position set, each sensor is assigned a unique ID, and a sensor ID and installation coordinate correspondence table is established. The working signal frequency range of each sensor is obtained from the sensor technical parameters and supplemented into the correspondence table. All sensors are traversed in a pairwise combination manner, and the straight-line distance D i , S j (i≠j) between the installation positions of each pair of sensors (S i，j ) is calculated through the three-dimensional space distance formula.

[0116] ;

[0117] After the calculation is completed, the distance value is associated with the sensor to generate a temporary data table of the sensor and the distance.

[0118] For each pair of sensors (S i , S j） , the signal frequency coincidence degree is calculated. The intersection interval of the frequency ranges of the two sensors is obtained: the minimum value of the intersection is the larger value of the minimum values of the frequencies of the two sensors, and the maximum value of the intersection is the smaller value of the maximum values of the frequencies of the two sensors; if the minimum value of the intersection is greater than the maximum value, it means that there is no frequency coincidence, and the coincidence degree is 0; if there is an intersection, the length of the intersection interval is divided by the length of the shorter interval in the frequency range of the two sensors to obtain the frequency coincidence degree, which is supplemented into the temporary data of the sensor and the distance.

[0119] Step A20, inputting the distance and the signal frequency coincidence degree into a preset sensor interference model to generate an interference risk matrix.

[0120] In this embodiment, the preset sensor interference model is a distance and frequency coincidence degree weighted evaluation model, the inter-sensor interference risk is positively correlated with the frequency coincidence degree and negatively correlated with the distance, and the risk level is determined by quantitative scoring.

[0121] In a feasible implementation, step A20 includes: determining a distance contribution degree according to a ratio of the distance and a sensor signal transmission limit distance, performing weighted summation on the distance contribution degree and the frequency coincidence degree to obtain a risk score; determining a risk level according to the risk score and a preset risk level division threshold; and generating the interference risk matrix based on the risk level and a sensor identifier.

[0122] In this embodiment, the risk score formula is:

[0123] wherein, W D is a distance weight, W C is a frequency coincidence degree weight, and D max is a sensor signal transmission limit distance. The preset risk level division threshold is exemplarily high risk ≥ 0.7, medium risk 0.3 ≤ < 0.7, and low risk < 0.3.

[0124] The sensor pair, distance, and frequency coincidence degree data table are traversed, the risk score of each pair of sensors is calculated by substituting the data into the risk score formula, if D i，j ≥ D max , then 1-(D i，j / D max ) ≤ 0, the value of this factor is 0, the distance exceeds the transmission limit, and there is no interference; if D i，j <D max , the value of this factor is calculated according to the formula. An n×n-dimensional interference risk matrix is constructed with sensor ID as rows and columns, and the matrix element M[i][j] represents the interference risk level of the sensor pair.

[0125] Step A30, according to the interference risk matrix and a preset conflict position adjustment rule, the initial deployment position is adjusted to obtain a target deployment position set.

[0126] In this embodiment, the preset conflict adjustment rule can be risk level priority and lowest adjustment cost; the high-risk conflict is adjusted, the position translation mode is preferentially adopted, the position replacement is adopted if translation is invalid, and the sensor model replacement is considered if the position replacement is still invalid; the medium-risk conflict preferentially adopts signal parameter adjustment, such as fine-tuning the frequency range, the position translation is performed if the adjustment is invalid; and the low-risk conflict does not need to be adjusted, and the original deployment position is retained.

[0127] ​The position translation rule can be: translating in the direction away from the interference source in the installable grid set, the translation step is the grid accuracy, the interference risk is recalculated after each translation, and the risk level is reduced to medium or low or the maximum translation number is reached. The position replacement rule can be: filtering the grids from the installable grid set that can cover all the effective monitoring grids of the original sensor and have a distance greater than 1.5 times the original distance from the interference source, and preferentially selecting the grid with the highest coverage efficiency as the new installation position.

[0128] For high-risk conflict adjustment, all high-risk sensor pairs in the interference risk matrix are filtered, and sorted in descending order according to the number of conflicts. One of the sensors is selected, and the sensor with fewer covered grids is preferentially selected. The direction vector of the interference source is calculated to determine the translation direction. The distance and frequency coincidence degree with all other sensors are recalculated after each translation grid accuracy, and the interference risk score is updated. If the risk level is reduced to medium or low within the preset number of translations, the new position is retained. If the translation adjustment is invalid, the replacement grid that meets the conditions is filtered from the installable grid set: the covered grid set contains all the effective monitoring grids of the original sensor; the distance from the interference source is greater than 1.5 times the original distance; and all deployment constraint conditions are met. The replacement grid with the highest coverage efficiency is selected, the installation coordinates are updated to the center point coordinates of the grid, and the interference risk is recalculated until the risk meets the standard. If there is no feasible grid for position replacement, an alarm prompt is output, and it is suggested to replace one of the sensor models. After updating the sensor technical parameters, the interference risk is recalculated until the conflict is resolved.

[0129] For medium-risk conflict adjustment, signal parameter adjustment is preferentially attempted. The frequency adjustable range of the sensor is extracted, the frequency range of one of the sensors is fine-tuned, and the frequency coincidence degree is recalculated. If the coincidence degree is reduced to the preset coincidence threshold after frequency adjustment, the risk level is reduced to low, and the adjusted frequency parameter is retained. If the frequency adjustment is invalid, the position of one of the sensors is adjusted according to the position translation rule of the high-risk conflict until the risk level is reduced to low.

[0130] After all conflict adjustments are completed, the distance, frequency coincidence degree and interference risk of all sensor pairs are recalculated, a new interference risk matrix is generated, and it is ensured that there is no high-risk conflict and the proportion of medium-risk conflicts is less than the preset proportion.

[0131] In this embodiment, the interference relationship between sensors is converted into quantifiable and classifiable risk data through distance and frequency coincidence degree calculation and interference risk matrix construction, and the precise positioning of interference conflicts is realized. The hierarchical adjustment rule based on risk level ensures the pertinence and low cost of interference conflict resolution.

[0132] The application provides a sensor arrangement recommendation device for building detection, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the sensor arrangement recommendation method for building detection in the above embodiment one.

[0133] Reference is made below in conjunction with Figure 3 which shows a structural schematic diagram of a sensor arrangement recommendation device for building detection suitable for implementing the embodiments of the application. The sensor arrangement recommendation device for building detection in the embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, personal digital assistants (PDA), tablet computers (PAD), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 3 The sensor arrangement recommendation device for building detection shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0134] As Figure 3As shown, the sensor arrangement recommendation device for building detection can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or loaded from a storage device 1003 into a random access memory (RAM) 1004. In the random access memory 1004, various programs and data required for the operation of the sensor arrangement recommendation device for building detection are also stored. The processing device 1001, the read only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the sensor arrangement recommendation device for building detection to communicate with other devices wirelessly or by wire to exchange data. Although the sensor arrangement recommendation device for building detection with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0135] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the read only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0136] The sensor arrangement recommendation device for building detection provided in the application adopts the sensor arrangement recommendation method for building detection in the above embodiment, and can solve the technical problem of how to improve the accuracy of sensor deployment. Compared with the prior art, the beneficial effects of the sensor arrangement recommendation device for building detection provided in the application are the same as those of the sensor arrangement recommendation method for building detection provided in the above embodiment, and other technical features in the sensor arrangement recommendation device for building detection are the same as those disclosed in the above embodiment method, and thus are not described herein.

[0137] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0138] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0139] The present application provides a computer readable storage medium having computer readable program instructions (i.e. computer programs) stored thereon, the computer readable program instructions being used to execute the sensor arrangement recommendation method for building detection in the above embodiment.

[0140] The computer readable storage medium provided in the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), an optical fiber, a portable compact disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained in the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, a radio frequency (RF), or the like, or any suitable combination of the above.

[0141] The computer readable storage medium described above may be contained in a sensor arrangement recommendation device for building detection, or may exist independently without being assembled into the sensor arrangement recommendation device for building detection. The computer readable storage medium described above carries one or more programs, which, when executed by the sensor arrangement recommendation device for building detection, cause the sensor arrangement recommendation device for building detection to: determine a matched target sensor according to environmental information data and detection requirement data of a building; determine a basic matching rule set corresponding to each target sensor, and adjust the basic matching rule set according to drawing data of the building to obtain a target rule set of each target sensor; determine a minimum sensor quantity and an initial installation coordinate of the building according to the target rule set to obtain an initial deployment position.

[0142] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0143] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0144] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0145] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., a computer program) for executing the above-mentioned sensor arrangement recommendation method for building detection, and can solve the technical problem of how to improve the accuracy of sensor deployment. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the sensor arrangement recommendation method for building detection provided by the above-mentioned embodiments, which will not be described here.

[0146] The above merely describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or direct / indirect application in other related technical fields based on the technical concept of the present application, and the contents of the specification and drawings are included in the patent protection scope of the present application.

Claims

1. A sensor arrangement recommendation method for building detection, characterized by, The sensor arrangement recommendation method for building detection comprises the following steps: According to the environmental information data of the building and the detection requirement data, determine the matching target sensor, comprising: according to the environmental information data of the building and the detection requirement data, determine the first candidate sensor; compare the environmental information data with the preset working environment threshold value of the sensor, and determine the sensor whose environmental information data is greater than or equal to the working environment threshold value as the second candidate sensor; according to the intersection of the first candidate sensor and the second candidate sensor, determine the target sensor, wherein the step of determining the first candidate sensor according to the environmental information data of the building and the detection requirement data comprises: determining the monitoring target and the adaptive parameter according to the detection requirement data, matching the monitoring target and the adaptive parameter with the matching rule library to obtain the first candidate sensor type set; Determine the basic matching rule set corresponding to each target sensor, and adjust the basic matching rule set according to the drawing data of the building to obtain the target rule set of each target sensor; According to the target rule set, determine the minimum number of sensors of the building and the initial installation coordinates to obtain the initial deployment position; Based on the initial deployment position, obtain the distance between each sensor and the signal frequency coincidence degree; Input the distance and the signal frequency coincidence degree into the preset sensor interference model to generate an interference risk matrix, comprising: determining the distance contribution degree according to the ratio of the distance and the sensor signal transmission limit distance, weighting and summing the distance contribution degree and the frequency coincidence degree to obtain the risk score; according to the risk score and the preset risk level division threshold, determine the risk level; based on the risk level and the sensor identifier, generate the interference risk matrix; According to the interference risk matrix and the preset conflict position adjustment rule, adjust the initial deployment position to obtain the target deployment position; The step of determining the target sensor according to the intersection of the first candidate sensor and the second candidate sensor comprises: Obtain the sensor accuracy, sensor cost, sensor energy consumption and effective working threshold interval of each sensor in the intersection; compare the average value corresponding to the effective working threshold interval with the preset safety area interval, and determine the environmental fitness score according to the comparison result; determine the sensor accuracy score according to the ratio of the sensor accuracy and the preset requirement accuracy; determine the sensor cost score based on the ratio of the sensor cost and the preset minimum cost; determine the sensor energy consumption score according to the ratio of the sensor energy consumption and the preset minimum energy consumption; determine the total score by summing the environmental fitness score, the sensor accuracy score, the sensor cost score and the sensor energy consumption score; determine the target sensor with the highest total score.

2. The sensor arrangement recommendation method for building detection according to Claim 1, wherein The step of determining the basic matching rule set corresponding to each target sensor and adjusting the basic matching rule set according to the drawing data of the building to obtain a target rule set of each target sensor comprises: According to the type identifier of the target sensor, determine the associated basic deployment rule from the preset deployment rule library, wherein the basic deployment rule comprises an upper limit of sensor installation height, a lower limit of sensor installation height, a coverage range calculation model and a preset safety distance; According to the drawing data, a three-dimensional space model of the building is established, the building height and the beam body lower edge height of the detection area are obtained based on the three-dimensional space model of the building, the upper limit of the sensor installation height is adjusted according to the building height, and the lower limit of the sensor installation height is adjusted according to the beam body lower edge height; Based on the three-dimensional space model of the building, the size data and the shielding construction type of the detection area are obtained, and the preset coverage range calculation model is adjusted; Based on the three-dimensional space model of the building, the coordinates of the electrical equipment are obtained, and the prohibited installation area is generated according to the coordinates of the electrical equipment and the preset safety distance.

3. The sensor arrangement recommendation method for building detection according to claim 2, wherein, The step of adjusting the preset coverage range calculation model based on the size data and the shielding construction type of the detection area based on the three-dimensional space model of the building comprises: Determine the corresponding detection range superposition coefficient based on the size data; Determine the shielding member based on the coordinate range of the member in the three-dimensional space model of the building and the preset space collision detection algorithm, and adjust the shielding correction factor in the coverage range calculation model based on the construction type of the shielding member; The product of the shielding correction factor, the detection range superposition coefficient and the preset reference detection radius is determined as the effective radius, and the coverage range calculation model is adjusted based on the effective radius.

4. The sensor arrangement recommendation method for building detection according to Claim 1, wherein The step of determining the minimum sensor quantity of the building and the initial installation coordinates according to the target rule set to obtain the initial deployment position comprises: Divide the detection area into a three-dimensional grid according to a preset precision, and divide the three-dimensional grid into an effective monitoring grid and an installable grid according to the target rule set; Based on the preset greedy algorithm, traverse the installable grid, and determine the number of covered effective monitoring grids of the installable grid according to a preset installable grid and coverage grid mapping table; Determine the target installable grid with the largest number of covered effective monitoring grids as the installation position, and determine the midpoint coordinates of the target installable grid as the initial installation coordinates; Determine the sensor quantity with a coverage reaching a preset coverage threshold as the minimum sensor quantity.

5. A sensor arrangement recommendation device for building detection, characterized by, The sensor arrangement recommendation device for building detection comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the sensor arrangement recommendation method for building detection according to any one of claims 1 to 4.

6. A storage medium, characterized by The storage medium is a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the sensor arrangement recommendation method for building detection according to any one of claims 1 to 4.

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