Green building intelligent inspection method

Abstract

The invention discloses an intelligent inspection method for a green building, and the method comprises the following steps: obtaining the latitude and longitude information of an inspection point and the number of the inspection point, and inputting the latitude and longitude information of the inspection point and the number of the inspection point into a label; acquiring positioning source data of the position of the inspection maneuvering equipment; performing data fusion on the positioning source data and the label data, splicing the fused positioning data and the label data to generate combined positioning data, and encrypting the combined positioning data to generate inspection positioning data; and obtaining routing inspection result positioning information, comparing the routing inspection result positioning information with the routing inspection positioning data, and finally obtaining a positioning correction result. Compared with the prior art, the method has the advantages that the accuracy and stability of data are improved, the inspection efficiency is improved by the comprehensive label technology, the workload of personnel is reduced, and the method can adapt to different scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of green buildings, and in particular to a green building intelligent inspection method. Background Art

[0002] At present, intelligent inspection tasks in the construction field are usually completed by security personnel, relying more on a single positioning source (such as GPS, dot locator, etc.). These positioning sources are susceptible to various interferences or human damage, resulting in inaccurate positioning information, affecting the reliability of daily intelligent inspection tasks. Some technical solutions use cellular network positioning and Bluetooth beacon positioning, but they also have the problem of easy interference. Therefore, a new intelligent inspection method needs to be developed. Summary of the invention

[0003] Purpose of the invention: To solve the above problems, the present invention provides a green building intelligent inspection method to improve the accuracy and stability of data, improve the efficiency of inspection by integrating labeling technology, reduce the workload of personnel, and can adapt to different scenarios.

[0004] Technical solution: To achieve the above purpose, the green building intelligent inspection method of the present invention comprises the following steps:

[0005] S1. Obtain the latitude and longitude information and inspection point number of the inspection point, and enter the latitude and longitude information and inspection point number of the inspection point into the label;

[0006] S2. Obtaining the positioning source data of the location of the patrol mobile equipment;

[0007] S3. Fuse the positioning source data with the label data, then concatenate the fused positioning data with the label data to generate combined positioning data, and finally encrypt the combined positioning data to generate inspection positioning data;

[0008] S4. Obtain the inspection result positioning information, and compare the inspection result positioning information with the inspection positioning data to obtain the positioning correction result.

[0009] Furthermore, the green building intelligent inspection system used includes inspection mobile equipment and tags; the inspection mobile equipment includes path planning equipment and path walking equipment.

[0010] Furthermore, the tag is an NFC tag or an RFID chip.

[0011] Furthermore, the path planning device includes a heat-insulating material, which is concave in design, with a notch arranged on the front.

[0012] Furthermore, the path walking device includes a walking component.

[0013] Furthermore, the positioning source data described in S2 includes GPS positioning data, WIFI positioning data, cellular network positioning data, and Bluetooth positioning data.

[0014] Furthermore, the data fusion described in S3 is achieved by setting weights of the positioning source data, the sum of the weights being 1, and then using a weighted average algorithm to fuse the data.

[0015] Furthermore, the weighted average algorithm is expressed as:

[0016] lat fused =W GPS lat GPS +W WIFI lat WIFI +W CELL lat CELL +W BLE lat BLE +W NFC lat NFC

[0017] lon fused =W GPS ·lon GPS +W WIFI ·lon WIFI +W CELL ·lon CELL +W BLE ·lon BLE +W NFC ·lon NFC

[0018] lat fused Indicates the latitude of the fused positioning data, lon fused represents the longitude of the fused positioning data, W GPS Indicates the weight of GPS positioning longitude and latitude data, lat GPS Indicates the latitude of GPS positioning longitude and latitude data, lon GPS Indicates the longitude of GPS positioning longitude and latitude data, W WIFI Indicates the weight of WIFI positioning data, lat WIFI Indicates the latitude of WIFI positioning data, lon WIFI Indicates the longitude of WIFI positioning data, W CELL Represents the weight of cellular network positioning data, lat CELL Indicates the latitude of cellular network positioning data, lon CELL Indicates the longitude of the cellular network positioning data, W BLE Indicates the weight of Bluetooth positioning data, lat BLE Indicates the latitude of Bluetooth positioning data, lon BLEIndicates the longitude of Bluetooth positioning data, W NFC The weight of the data representing the label, lat NFC Indicates the latitude of the label data, lon NFC The longitude of the data representing the label.

[0019] Furthermore, the data concatenation method described in S3 is expressed as:

[0020] L final =(lat fused ,lon fused ,lat NFC ,lon NFC , id)

[0021] L final Represents the combined positioning data, and id represents the unique number of the tag.

[0022] Furthermore, the encryption method described in S3 is expressed as:

[0023] L encrypted =SHA-(L final )

[0024] L encrypted This is the final inspection and positioning data.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following significant effects: the inspection route is generated by the thermal generation path planning device, which reduces the workload of personnel and avoids the situation of inadequate inspection; multiple positioning source data are used and integrated to improve the accuracy of the positioning data, and at the same time, the weights of the positioning source data are set to adapt to different scenarios; the stability of the data is guaranteed by the label, the noise interference is reduced, and the location of the inspection equipment is quickly verified by reading the label, which simplifies the verification process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the structure of the inspection system described in the present invention. DETAILED DESCRIPTION

[0027] The present invention discloses a green building intelligent inspection method. Figure 1 As shown, the green building intelligent inspection method provided by the present invention is further described in detail below: The green building intelligent inspection method comprises the following steps:

[0028] Step 1: Obtain the latitude and longitude information of the inspection point and the number of the intelligent inspection point, and enter the latitude and longitude information of the inspection point and the inspection point number into the label. Before the inspection task begins, the user sets a label for each inspection point in advance, obtains the location of each inspection point and sets a unique number for the inspection point, where the location of the inspection point includes the latitude and longitude information of the inspection point; then, the user transmits the pre-set unique number and the obtained latitude and longitude information of the inspection point to the system, where the system includes a background server and a front-end inspection mobile equipment; the user can transmit the unique number of the inspection point and the latitude and longitude information of the inspection point to the background server for storage in advance, and the background server enters the stored unique number of the inspection point and the latitude and longitude information of the inspection point into each inspection point label through existing data transmission or data communication methods, where the two parts of data entered into the label It is a unique number and longitude and latitude information. The unique number refers to the unique identifier of each inspection point, which is mainly used to distinguish different intelligent inspection points. The longitude and latitude information refers to the fixed position of the tag, which is the fixed position of the inspection point. The data structure of the tag can be customized by the user, for example: the tag ID is set to tag_id, the longitude is set to latitude, and the latitude is set to longitude. It can also be set to other entry formats. In addition, the inspection point refers to a fixed device, so the label position is set to a fixed position. The inspection device needs to read and write the device at the fixed position. The inspection point label number is fixed, and the inspection point number and the longitude and latitude information of the inspection point in the background and the label are also fixed.

[0029] Step 2: Obtain the positioning source data of the location of the patrol mobile equipment. During the inspection task, the patrol personnel use the designated patrol mobile equipment to transmit the inspection results to the background server. The background server obtains the results of all patrol tasks to facilitate unified management of all patrol tasks. In order to effectively correct the positioning data and avoid the problem of inaccurate positioning of a single positioning source as much as possible, comprehensive positioning is performed by collecting data from multiple positioning sources to improve the accuracy of the current location of the current patrol mobile equipment to ensure the accuracy of the current location of the patrol mobile equipment. The patrol mobile equipment includes terminal devices such as mobile phones and PDAs. These terminal devices are equipped with positioning modules. The multiple positioning source data of the location of the patrol mobile equipment can be directly obtained through the positioning module of the patrol mobile equipment. The patrol mobile equipment transmits the obtained multiple positioning source data to the back-end server. Among them, the multiple positioning source data obtained by the patrol mobile equipment are real data. It is generally difficult for users to correct the positioning information in the multiple positioning source data. The multiple positioning source data include one or more of GPS positioning data, WIFI positioning data, cellular network positioning data, and Bluetooth positioning data. The specific selection can be based on the actual environment of the patrol mobile equipment.

[0030] Among them, the multi-source data acquisition module obtains GPS positioning latitude and longitude data through the GPS module in the patrol mobile equipment and uploads it to the back-end server. The GPS positioning data provides high-precision positioning information in the open space. The multi-source data acquisition module calculates the WIFI positioning data of the location of the patrol mobile equipment according to the signal strength of the access point of the nearby WIFI that the patrol mobile equipment connects to. The patrol mobile equipment scans the WIFI hotspots around the equipment and accesses the WIFI hotspots. The location of the WIFI hotspot is obtained through existing technology. According to the known location of the WIFI hotspot and the signal strength of the patrol mobile equipment accessing the WIFI hotspot, the patrol mobile equipment WIFI positioning data is calculated through existing technology. The WIFI positioning data has a good positioning effect in indoor environments, especially in areas with dense WIFI hotspots. The multi-source data acquisition module calculates the cellular network positioning data of the location of the patrol mobile equipment according to the signal strength of the base station near the patrol mobile equipment. The base station includes a cellular network tower. The location of the base station is a known location. According to the signal strength of the base station near the patrol mobile equipment and the known base station location, the cellular network positioning data of the patrol mobile equipment is calculated through the existing calculation method. The base station has a wide coverage range, and the cellular network positioning data can be used both indoors and outdoors, especially in areas with weak GPS signals. The inspection mobile equipment receives the Bluetooth beacon signal near the inspection mobile equipment, and calculates the Bluetooth positioning data of the location of the inspection mobile equipment according to the strength of the Bluetooth beacon signal. The inspection mobile equipment can scan the signals emitted by the surrounding Bluetooth beacons, and the location of the Bluetooth beacon signal is obtained through existing technologies. The Bluetooth positioning data is calculated using existing technologies based on the strength of the Bluetooth beacon signal received by the device and the specific location of the Bluetooth beacon signal. The Bluetooth positioning data provides high accuracy in small-range indoor positioning and is suitable for precise positioning of specific locations.

[0031] Step 3: Fuse and encrypt the positioning source data and label data to generate inspection positioning data. First, set the weight of each of the multiple positioning source data in advance, and the sum of all weights is 1. Then use the weighted average method to fuse the data of multiple positioning sources, and encrypt the fused data. The purpose is to improve the accuracy of positioning, enhance data correctness, and improve the overall reliability of the system. Through multi-source data fusion, more reliable and secure positioning services are provided in different application scenarios to ensure the authenticity and integrity of positioning data.

[0032] Among them, the user sets the weights of different positioning source data in advance according to the different environments where the inspection points are located. For example, the GPS weight is set to 0.4, the WIFI weight is set to 0.25, the cellular network weight is set to 0.2, the Bluetooth beacon weight is set to 0.15, and the NFC weight is set to 0.1. The above weight values ​​are only for example. The specific weight values ​​are set according to the actual situation. If there is a situation where a certain positioning source data is difficult to obtain, the weight of the difficult-to-obtain positioning source can be directly set to 0 to ensure that the sum of the set weight values ​​is 1. The method of fusing multiple positioning source data and tag data using the weighted average algorithm is expressed as:

[0033] lat fused =W GPS lat GPS +W WIFI lat WIFI +W CELL lat CELL +W BLE lat BLE +W NFC lat NFC

[0034] lon fused =W GPS ·lon GPS +W WIFI ·lon WIFI +W CELL ·lon CELL +W BLE ·lon BLE +W NFC ·lon NFC

[0035] lat fused Indicates the latitude of the fused positioning data, lon fused represents the longitude of the fused positioning data, W GPS Indicates the weight of GPS positioning longitude and latitude data, lat GPS Indicates the latitude of GPS positioning longitude and latitude data, lon GPS Indicates the longitude of GPS positioning longitude and latitude data, W WIFI Indicates the weight of WIFI positioning data, lat WIFI Indicates the latitude of WIFI positioning data, lon WIFI Indicates the longitude of WIFI positioning data, W CELL Represents the weight of cellular network positioning data, lat CELL Indicates the latitude of cellular network positioning data, lon CELL Indicates the longitude of the cellular network positioning data, W BLEIndicates the weight of Bluetooth positioning data, lat BLE Indicates the latitude of Bluetooth positioning data, lon BLE Indicates the longitude of Bluetooth positioning data, W NFC The weight of the data representing the label, lat NFC Indicates the latitude of the label data, lon NFC The longitude of the data representing the label.

[0036] Then, the fused positioning data and label data are spliced ​​to generate combined positioning data. The data splicing method is expressed as:

[0037] L final =(lat fused ,lon fused ,lat NFC ,lon NFC , id)

[0038] L final Represents the combined positioning data, and id represents the unique number of the tag.

[0039] Finally, the combined positioning data is encrypted using an encryption algorithm to generate inspection positioning data. In order to ensure the uncorrectability of the data, the final combined positioning data is encrypted. The encryption method uses the existing encryption algorithm to directly encrypt. The encryption method is expressed as:

[0040] L encrypted =SHA-(L final )

[0041] L encrypted This is the final inspection positioning data. The inspection positioning data is a variety of data obtained by the inspection mobile equipment.

[0042] The data after fusion and encryption of positioning source data is the accurate positioning data of patrol mobile equipment.

[0043] Step 4: Obtain the inspection result positioning information, and compare the inspection result positioning information with the inspection positioning data to obtain the positioning correction result. The inspection result positioning information is the inspection result positioning information that the inspection personnel or other relevant personnel sign through the specified software and transmit to the background server. The background server compares the obtained inspection result positioning information with the accurate inspection positioning data collected by the inspection mobile equipment to obtain the final positioning correction result, thereby correcting the positioning data. The server verifies the authenticity of the information according to the pre-set signature method, and whether the designated equipment is used to perform the inspection task, so as to achieve the purpose of positioning information correction. If other equipment or web pages are used to transmit the inspection results to the server, because the other equipment has not set the signature method, the background server inspection error occurs, and the system can remind the user that data correction may occur and verification is required. In addition, in order to avoid modifying the uploaded positioning data directly through the server, so that the false position of the inspection mobile equipment is consistent with the on-site position of the inspection equipment to achieve positioning correction, the inspection result positioning data obtained by the background server is compared with the accurate positioning data stored in the background server in advance to prevent correction, data change and other behaviors. Through multiple positioning source data and background verification, multiple verifications are performed to achieve the purpose of positioning information correction, improve the security of positioning data, and avoid positioning information being corrected or modified during the execution of inspection tasks as much as possible.

[0044] The green building intelligent inspection system adopted by the inspection method includes inspection mobile equipment, tags 6, storage, and processors. Among them, the inspection mobile equipment includes path planning equipment 1 and path walking equipment 2. The path planning equipment is of thermal generation type, including walking parts 3, thermal imaging recognition camera 4, and host. The path planning equipment sets the inspection node according to the system. The path walking equipment is of recognizable thermal generation type, including insulation material, heating wire 5, and circuit for driving tags. The path walking equipment performs walking inspection according to the thermal path of the ground; the thermal imaging recognition camera transmits the inspection picture to the control center, and the video information automatically identifies abnormal information after AI processing and sends warning information; the insulation material is concave in design, and the notch is set on the front to facilitate the heat dissipation to the front, and at the same time facilitate the thermal imaging camera to capture the path. The tag is fixed at the inspection point, and the tag is an NFC tag or RFID chip for writing and reading information. The memory stores a computer program of the positioning correction method that can be loaded and executed by the processor. The memory is a storage medium hard disk (HDD) or solid-state drive (SSD), etc. The processor is a central processing unit (CPU) or an embedded neural network processor (NPU) or other deep learning coprocessors, discrete gate or transistor logic devices, discrete hardware components and other chips. The green building intelligent inspection system includes a label data entry module, a multi-source data acquisition module, a positioning data generation module, and a positioning correction judgment module. The label data entry module is used to obtain the latitude and longitude information and the inspection point number of the inspection point, and enter the latitude and longitude information and the inspection point number into the label; the multi-source data acquisition module is used to obtain various positioning source data of the location of the inspection mobile equipment; the positioning data generation module is used to fuse and encrypt the various positioning source data with the label data according to the preset weights to generate inspection positioning data; the positioning correction judgment module is used to obtain the inspection result positioning information, compare the inspection result positioning information with the intelligent inspection positioning data, and obtain the positioning correction result.

Claims

1. A green building intelligent inspection method, characterized in that: The following steps are involved: S1. Obtain the latitude and longitude information and inspection point number of the inspection point, and enter the latitude and longitude information and inspection point number of the inspection point into the label; S2. Obtain the positioning source data of the location of the inspection mobile equipment; S3. Fuse the positioning source data with the label data, then concatenate the fused positioning data with the label data to generate combined positioning data, and finally encrypt the combined positioning data to generate inspection positioning data; S4. Obtain the inspection result positioning information, and compare the inspection result positioning information with the inspection positioning data to obtain the positioning correction result.

2. The green building intelligent inspection method according to claim 1, characterized in that: The green building intelligent inspection system used comprises inspection mobile equipment and a tag (6); the inspection mobile equipment comprises a path planning device (1) and a path walking device (2).

3. The green building intelligent inspection method according to claim 2, characterized in that: The tag (6) is an NFC tag or an RFID chip.

4. The green building intelligent inspection system according to claim 2, characterized in that: The path planning device (1) comprises a heat-insulating material, which is of concave design, with a notch arranged on the front.

5. The green building intelligent inspection system according to claim 2, characterized in that: The path walking device (2) comprises a walking component (3).

6. The green building intelligent inspection method according to claim 1, characterized in that: The positioning source data described in S2 includes GPS positioning data, WIFI positioning data, cellular network positioning data, and Bluetooth positioning data.

7. The green building intelligent inspection method according to claim 1, characterized in that: The data fusion described in S3 is achieved by setting the weights of the positioning source data, the sum of the weights being 1, and then using a weighted average algorithm to fuse the data.

8. The green building intelligent inspection method according to claim 7, characterized in that: The weighted average algorithm is expressed as: years fused =In GPS ·years GPS +W WIFI ·years WIFI +W CELL ·years CELL +W BLE ·years BLE +W NFC ·years NFC lon fused =W GPS lon GPS +W WIFI lon WIFI +W CELL lon CELL +W BLE lon BLE +W NFC lon NFC lat fused Indicates the latitude of the fused positioning data, lon fused represents the longitude of the fused positioning data, W GPS Indicates the weight of GPS positioning longitude and latitude data, lat GPS Indicates the latitude of GPS positioning longitude and latitude data, lon GPS Indicates the longitude of GPS positioning latitude and longitude data, W WIFI Indicates the weight of WIFI positioning data, lat WIFI Indicates the latitude of WIFI positioning data, lon WIFI Indicates the longitude of WIFI positioning data, W CELL Represents the weight of cellular network positioning data, lat CELL Indicates the latitude of cellular network positioning data, lon CELL Indicates the longitude of the cellular network positioning data, W BLE Indicates the weight of Bluetooth positioning data, lat BLE Indicates the latitude of Bluetooth positioning data, lon BLE Indicates the longitude of Bluetooth positioning data, W NFC The weight of the data representing the label, lat NFC Indicates the latitude of the label data, lon NFC The longitude of the data representing the label.

9. The green building intelligent inspection method according to claim 1, characterized in that: The data splicing method described in S3 is expressed as: L final =(years fused ,lon fused ,years NFC ,lon NFC ,id) L final Represents the combined positioning data, and id represents the unique number of the tag.

10. The green building intelligent inspection method according to claim 5, characterized in that: The encryption method described in S3 is expressed as: L encrypted =SHA-(L final ) L encrypted This is the final inspection and positioning data.

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