Workshop building body maintenance method and device based on industrial internet of things, terminal and medium

Abstract

The application discloses a workshop building body maintenance method and device based on an industrial Internet of Things, a terminal and a medium. The method comprises the following steps: acquiring sensor data corresponding to key positions of a workshop building body; performing state evaluation on the workshop building body based on the sensor data, to obtain a current building health index corresponding to the workshop building body; predicting the remaining service life of the workshop building body based on the sensor data and the current building health index, to obtain a predicted building health index corresponding to the workshop building body and a predicted remaining service life value corresponding to the workshop building body; and screening a target maintenance scheme from a preset maintenance scheme based on a preset threshold, the current building health index, the predicted building health index and the predicted remaining service life value, so as to maintain the workshop building body by using the target maintenance scheme. The application aims to improve the service life of the workshop building body and reduce the safety risk of the workshop building body.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, device, terminal and medium for workshop building maintenance based on the Industrial Internet of Things. Background Technology

[0002] Workshop buildings are the core physical carriers of industrial production, and their structural stability directly affects production safety, equipment operating efficiency, and enterprise capacity. Industrial workshop buildings (especially those in heavy manufacturing, chemical, and warehousing industries) are subjected to multiple loads over long periods, including dynamic loads (such as vibrations of large equipment and overhead crane operation), static loads (such as the loading of equipment and raw materials), environmental corrosion (such as humidity and chemical gas erosion), and geological settlement. This makes them prone to structural damage problems such as steel structure stress fatigue, concrete crack propagation, uneven foundation settlement, and wall cracking. If maintenance is not timely, it may lead to building collapse, production interruption, or even safety accidents.

[0003] However, existing workshop building maintenance technologies have the following shortcomings: Existing technologies often use fixed weights to fuse sensor data, failing to consider the decreased reliability caused by abnormal fluctuations in sensor data and the urgency of risks corresponding to data mutations, resulting in significant deviations between the condition assessment results and the actual structural health status; existing technologies typically directly perform simple weighted summation of sensor data to obtain a health index, but the quantitative accuracy of the health index is insufficient and cannot accurately reflect the true condition of the building; existing technologies often use a single model for life prediction, leading to poor temporal correlation of the prediction results, insufficient capture of key features, and low prediction accuracy, thus easily resulting in over-maintenance or delayed maintenance. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, terminal and medium for the maintenance of workshop buildings based on the Industrial Internet of Things, which aims to improve the service life of workshop buildings and reduce the safety risks of workshop buildings.

[0005] To achieve the above objectives, this application provides a workshop building maintenance method based on the Industrial Internet of Things, the method comprising: Acquire sensor data corresponding to key parts of the workshop building; Based on the sensor data, the condition of the workshop building is assessed to obtain the current building health index corresponding to the workshop building; Based on the sensor data and the current building health index, the remaining service life of the workshop building is predicted, and the predicted building health index and the predicted remaining service life value of the workshop building are obtained. Based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life, a target maintenance plan is selected from preset maintenance plans to maintain the workshop building through the target maintenance plan.

[0006] Specifically, the sensor data includes monitoring data from multiple sensors; The process of assessing the condition of the workshop building based on the sensor data to obtain the current building health index includes: The real-time confidence value corresponding to the sensor monitoring data is calculated; Based on the changing trends corresponding to the sensor monitoring data, determine the urgency calculation factor corresponding to the sensor monitoring data. Based on the real-time confidence value, the urgency calculation factor, and the preset basic weights corresponding to the sensor monitoring data, the comprehensive weights corresponding to the sensor monitoring data are obtained. The current building health index is obtained based on the sensor monitoring data and the comprehensive weight.

[0007] Specifically, the sensor monitoring data includes the monitoring time and the sensor monitoring value corresponding to the monitoring time; The calculation of the real-time confidence value corresponding to the sensor monitoring data includes: Based on the monitoring time and the sensor monitoring values, a monitoring data sequence is constructed; The standard deviation of the monitoring data sequence was calculated. The absolute value of the linear correlation coefficient between the monitoring data sequence and the preset time index is calculated; By using a predefined function, the standard deviation and the absolute value are mapped to the real-time confidence value.

[0008] Specifically, the step of obtaining the comprehensive weight corresponding to the sensor monitoring data based on the real-time confidence value, the urgency calculation factor, and the preset basic weight corresponding to the sensor monitoring data includes: The comprehensive weight is obtained by multiplying the real-time confidence value, the urgency calculation factor, and the preset basic weight.

[0009] Specifically, obtaining the current building health index based on the sensor monitoring data and the comprehensive weight includes: The sensor monitoring data is weighted and summed using the comprehensive weights to obtain a first calculation result; Calculate the sum of all the combined weights to obtain the second calculation result; The quotient of the first calculation result and the second calculation result is calculated to obtain the current building health index.

[0010] Specifically, the step of predicting the remaining service life of the workshop building based on the sensor data and the current building health index, to obtain the predicted building health index and the predicted remaining service life value of the workshop building, includes: Based on the sensor data and the current building health index, an initial three-dimensional tensor is constructed. By using a pre-set bidirectional long short-term memory network, temporal features are extracted from the initial three-dimensional vector to obtain a hidden state sequence; By using a preset attention mechanism, a weighted context vector is obtained based on the hidden state sequence, wherein the weighted context vector is used to characterize the attention weight corresponding to the hidden state at each time step in the hidden state sequence; By using a pre-set multilayer perceptron, the weighted context vector is decoded to obtain the predicted building health index and the predicted remaining useful life value.

[0011] Specifically, the preset maintenance scheme includes a first preset maintenance scheme, a second preset maintenance scheme, and a third preset maintenance scheme; The preset threshold includes a preset emergency threshold and a preset warning threshold, wherein the preset emergency threshold is greater than the preset warning threshold; The step of selecting a target maintenance plan from preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life includes: The absolute value of the difference between the current building health index and the predicted building health index is calculated. The maintenance trigger evaluation value corresponding to the workshop building is obtained by multiplying the absolute value of the difference with the predicted remaining service life value. By comparing the maintenance trigger evaluation value, the preset emergency threshold, and the preset early warning threshold, if the maintenance trigger evaluation value is greater than or equal to the preset emergency threshold, then the first preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is greater than or equal to the preset early warning threshold and the maintenance trigger evaluation value is less than the preset emergency threshold, then the second preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is less than the preset early warning threshold, then the third preset maintenance plan is determined as the target maintenance plan.

[0012] To achieve the above objectives, this application also provides a workshop building maintenance device based on the Industrial Internet of Things, the device comprising: The first unit is used to acquire sensor data corresponding to key parts of the workshop building; The second unit is used to assess the condition of the workshop building based on the sensor data and obtain the current building health index corresponding to the workshop building. The third unit is used to predict the remaining service life of the workshop building based on the sensor data and the current building health index, and to obtain the predicted building health index and the predicted remaining service life value of the workshop building. The fourth unit is used to select a target maintenance plan from the preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value, so as to maintain the workshop building through the target maintenance plan.

[0013] To achieve the above objectives, this application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any of the methods provided in this application.

[0014] To achieve the above objectives, this application also provides a medium storing a plurality of instructions adapted for loading by a processor to execute the steps in any of the methods provided in this application.

[0015] This application provides a method, device, terminal, and medium for maintaining workshop buildings based on the Industrial Internet of Things (IIoT). The method first acquires sensor data corresponding to key components of the workshop building; based on the sensor data, it assesses the condition of the workshop building to obtain a current building health index; based on the sensor data and the current building health index, it predicts the remaining service life of the workshop building to obtain a predicted building health index and a predicted remaining service life value; based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value, it selects a target maintenance scheme from preset maintenance schemes to maintain the workshop building, thereby improving its service life and reducing its safety risks. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the method provided in the embodiments of this application; Figure 2 A flowchart illustrating the prediction of the remaining service life of a workshop building, provided as an embodiment of this application; Figure 3 This is a schematic diagram of the device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the terminal structure provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Existing workshop building maintenance technologies have the following shortcomings: Firstly, existing technologies often use fixed weights to fuse sensor data, failing to consider the decreased reliability caused by abnormal fluctuations in sensor data and the urgency of risks corresponding to data mutations. This results in significant discrepancies between the condition assessment results and the actual structural health status. Secondly, existing technologies typically obtain a health index by simply weighting and summing sensor data, but the quantitative accuracy of the health index is insufficient and cannot accurately reflect the true condition of the building. Thirdly, existing technologies often use a single model for lifespan prediction, leading to poor temporal correlation of the prediction results, insufficient capture of key features, and low prediction accuracy. This can easily result in over-maintenance or delayed maintenance.

[0019] Therefore, this application provides a workshop building maintenance method, device, terminal, and medium based on the Industrial Internet of Things to solve practical technical problems.

[0020] In some embodiments, with the development of sensor technology and the Industrial Internet of Things (IIoT), the industry is gradually introducing a real-time sensor monitoring mode. By deploying sensors for stress, strain, settlement, cracks, etc., in key parts of the building, the automated collection and transmission of data is realized, providing data support for maintenance decisions.

[0021] In some embodiments, the device may be integrated into an electronic device, such as a terminal or server.

[0022] In some embodiments, the server may also be implemented as a terminal.

[0023] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0024] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0025] The following sections provide detailed descriptions of each example. It should be noted that the sequence numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0026] This application provides a workshop building maintenance method based on the Industrial Internet of Things, which can improve the efficiency and accuracy of factory wastewater discharge.

[0027] In some embodiments, a steel structure production workshop of a heavy machinery manufacturing enterprise (with a building area of ​​10,000㎡, equipped with a 50-ton overhead crane, and subjected to dynamic loads for a long time) requires maintenance of five key parts of the workshop building: No. 1 steel column (stress, settlement), No. 2 steel beam (strain, vibration), foundation platform (settlement), and wall area A (cracks).

[0028] In some embodiments, the pre-deployed sensors and parameters are as follows: Sensor types and numbers: Stress sensor S1 (steel column), Strain sensor S2 (steel beam), Settlement sensor S3 (foundation), Settlement sensor S4 (steel column foundation), Crack width sensor S5 (wall). Data collection frequency: once every 10 minutes, with a collection period of 72 hours; Preset base weights: S1=0.3, S2=0.25, S3=0.15, S4=0.1, S5=0.2; Preset thresholds: Emergency threshold = 80, Warning threshold = 40; Preset maintenance plans: First preset maintenance plan (emergency shutdown maintenance, reinforcement within 24 hours), second preset maintenance plan (planned maintenance, inspection within 7 days), third preset maintenance plan (routine inspection, once a month).

[0029] like Figure 1 The specific process of the method can be as follows: S110. Obtain sensor data corresponding to key parts of the workshop building.

[0030] In some embodiments, monitoring data from five sensors are collected through an industrial IoT gateway. Each sensor data includes the monitoring time and the corresponding sensor monitoring value, as shown in Table 1. The units are: S1 is MPa, S2 is με, S3 / S4 is mm, and S5 is mm.

[0031] Table 1. Example of Sensor Data

[0032] S120. Based on the sensor data, the status of the workshop building is assessed to obtain the current building health index corresponding to the workshop building.

[0033] In some embodiments, the sensor data includes monitoring data from multiple sensors.

[0034] Specifically, the step of assessing the condition of the workshop building based on the sensor data to obtain the current building health index includes the steps A1 to A4 shown below: A1. Calculate the real-time confidence value corresponding to the sensor monitoring data; In some embodiments, the sensor monitoring data includes the monitoring time and the sensor monitoring value corresponding to the monitoring time.

[0035] Specifically, the calculation of the real-time confidence value corresponding to the sensor monitoring data includes the steps A11 to A14 shown below: A11. Based on the monitoring time and the sensor monitoring values, construct a monitoring data sequence.

[0036] In some embodiments, taking the S1 sensor as an example, a monitoring data sequence is constructed based on the monitoring time and the sensor monitoring value: X1=[120,122,...,150] (a total of 432 data points), and the time index sequence is T=[1,2,...,432].

[0037] A12. Calculate the standard deviation corresponding to the monitoring data sequence.

[0038] In some embodiments, the standard deviation of X1 is calculated: σ1 = 12.5; A13. Calculate the absolute value of the linear correlation coefficient between the monitoring data sequence and the preset time index.

[0039] In some embodiments, the absolute value of the linear correlation coefficient between X1 and T is calculated: |r1|=0.85, indicating that the stress shows a significant upward trend over time; A14. The standard deviation and the absolute value are mapped to the real-time confidence value through a preset defined function.

[0040] In some embodiments, the real-time confidence value is obtained by mapping through a preset defined function (such as the normalization function f(x,y)=1-(σ / 100)×(1-|r|)): f1=1-(12.5 / 100)×(1-0.85)=0.98125.

[0041] Similarly, the real-time confidence values ​​of S2-S5 can be obtained as follows: f2=0.978, f3=0.992, f4=0.985, f5=0.965.

[0042] A2. Based on the changing trends of the sensor monitoring data, determine the urgency calculation factor corresponding to the sensor monitoring data.

[0043] In some embodiments, the urgency is determined based on the changing trends of sensor data: S1 stress data increases by 25% in 72 hours (urgency k1=0.9), S2 strain increases by 37.5% (k2=0.95), S3 settlement increases by 19% (k3=0.7), S4 settlement increases by 27.8% (k4=0.8), and S5 crack width increases by 150% (k5=1.0).

[0044] A3. Based on the real-time confidence value, the urgency calculation factor, and the preset basic weights corresponding to the sensor monitoring data, the comprehensive weights corresponding to the sensor monitoring data are obtained.

[0045] In some embodiments, obtaining the comprehensive weight corresponding to the sensor monitoring data based on the real-time confidence value, the urgency calculation factor, and the preset basic weight corresponding to the sensor monitoring data includes the following specific implementation process: The comprehensive weight is obtained by multiplying the real-time confidence value, the urgency calculation factor, and the preset basic weight.

[0046] In some embodiments, the comprehensive weight is calculated as: real-time confidence value × urgency calculation factor × preset base weight. S1: 0.98125×0.9×0.3=0.265; S2: 0.978×0.95×0.25=0.230; S3: 0.992×0.7×0.15=0.104; S4: 0.985 × 0.8 × 0.1 = 0.079; S5: 0.965 × 1.0 × 0.2 = 0.193.

[0047] A4. Based on the sensor monitoring data and the comprehensive weight, the current building health index is obtained.

[0048] In some embodiments, the sensor monitoring data can be averaged over 72 hours: S1 average = 135 MPa, S2 average = 95 με, S3 average = 2.3 mm, S4 average = 2.0 mm, and S5 average = 0.35 mm.

[0049] In some embodiments, obtaining the current building health index based on the sensor monitoring data and the comprehensive weight includes the steps A41 to A43 shown below: A41. Using the comprehensive weights, the sensor monitoring data is weighted and summed to obtain the first calculation result.

[0050] In some embodiments, weighted summation yields the first calculation result: 135×0.265+95×0.230+2.3×0.104+2.0×0.079+0.35×0.193=35.775+21.85+0.2392+0.158+0.06755=58.08975; A42. Calculate the sum of all the comprehensive weights to obtain the second calculation result.

[0051] In some embodiments, the comprehensive weights are calculated to obtain a second calculation result: 0.265+0.230+0.104+0.079+0.193=0.871.

[0052] A43. Calculate the quotient of the first calculation result and the second calculation result to obtain the current building health index.

[0053] In some embodiments, the calculator obtains the current building health index: 58.08975 / 0.871≈66.7.

[0054] S130. Based on the sensor data and the current building health index, predict the remaining service life of the workshop building to obtain the predicted building health index and the predicted remaining service life value of the workshop building.

[0055] In some embodiments, the remaining service life of the workshop building is predicted based on the sensor data and the current building health index, resulting in a predicted building health index and a predicted remaining service life value for the workshop building. Figure 2 This includes the steps from B1 to B4 as shown below: B1. Based on the sensor data and the current building health index, construct an initial three-dimensional tensor.

[0056] In some embodiments, the sensor data is constructed into an initial three-dimensional tensor with the shape [432,5,2] by “time step (432) × sensor dimension (5) × feature dimension (2, monitoring value + timestamp)”.

[0057] B2. By using a pre-set bidirectional long short-term memory network, temporal features are extracted from the initial three-dimensional vector to obtain a hidden state sequence.

[0058] In some embodiments, the preset bidirectional long short-term memory network can be a preset Bi-LSTM. The initial three-dimensional tensor is processed to output a sequence of hidden states (with a shape of [432,128]).

[0059] B3. By using a preset attention mechanism, a weighted context vector is obtained based on the hidden state sequence, wherein the weighted context vector is used to characterize the attention weight corresponding to the hidden state at each time step in the hidden state sequence.

[0060] In some embodiments, the attention weight of the hidden state at each time step is calculated by additive attention through a preset attention mechanism (e.g., the weight of time step T432 is 0.92, which is a key time step), and the weighted context vector is obtained by weighting (shape

[128] ).

[0061] B4. By using a preset multilayer perceptron, the weighted context vector is decoded to obtain the predicted building health index and the predicted remaining service life value.

[0062] In some embodiments, the preset multilayer perceptron can be a preset MLP, which inputs a weighted context vector into the preset MLP and outputs a predicted building health index (55.2) and a predicted remaining useful life value (12 months).

[0063] S140. Based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value, a target maintenance plan is selected from the preset maintenance plans to maintain the workshop building through the target maintenance plan.

[0064] In some embodiments, the preset maintenance scheme includes a first preset maintenance scheme, a second preset maintenance scheme, and a third preset maintenance scheme; the preset threshold includes a preset emergency threshold and a preset early warning threshold, wherein the preset emergency threshold is greater than the preset early warning threshold.

[0065] Specifically, the step of selecting a target maintenance plan from preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value includes the following steps C1 to C3: C1. Calculate the absolute value of the difference between the current building health index and the predicted building health index.

[0066] In some embodiments, the absolute value of the difference between the current and predicted health index is calculated: |66.7-55.2|=11.5.

[0067] C2. Calculate the product of the absolute value of the difference and the predicted remaining service life value to obtain the maintenance trigger evaluation value corresponding to the workshop building.

[0068] In some embodiments, the maintenance trigger evaluation value is calculated as: 11.5 × 12 = 138.

[0069] C3. Compare the maintenance trigger evaluation value, the preset emergency threshold, and the preset early warning threshold. If the maintenance trigger evaluation value is greater than or equal to the preset emergency threshold, then the first preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is greater than or equal to the preset early warning threshold and the maintenance trigger evaluation value is less than the preset emergency threshold, then the second preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is less than the preset early warning threshold, then the third preset maintenance plan is determined as the target maintenance plan.

[0070] In some embodiments, since the maintenance trigger evaluation value is 138, which is greater than the preset emergency threshold (80), the first preset maintenance plan (emergency shutdown maintenance) is determined as the target maintenance plan, and the enterprise needs to arrange a professional team to carry out emergency reinforcement of the workshop building within 24 hours.

[0071] In summary, this application provides a workshop building maintenance method based on the Industrial Internet of Things, which can realize the intelligent management of the entire process of workshop building maintenance, from sensor data acquisition, condition assessment, life prediction to maintenance scheme selection. Compared with the existing technology, the assessment and prediction accuracy is improved by about 30%, and the pertinence and timeliness of the maintenance scheme are significantly enhanced.

[0072] To better implement the above methods, this application also provides a workshop building maintenance device based on the Industrial Internet of Things (IIoT). This device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet, smart Bluetooth device, laptop, or personal computer; the server can be a single server or a server cluster consisting of multiple servers.

[0073] For example, in this embodiment, the method of this application embodiment will be described in detail by taking the specific integration of a workshop building maintenance device based on the Industrial Internet of Things into the terminal as an example.

[0074] For example, such as Figure 3 As shown, the workshop building maintenance device 300 based on the Industrial Internet of Things may include a first unit 301, a second unit 302, a third unit 303, and a fourth unit 304. The device includes: The first unit is used to acquire sensor data corresponding to key parts of the workshop building; The second unit is used to assess the condition of the workshop building based on the sensor data and obtain the current building health index corresponding to the workshop building. The third unit is used to predict the remaining service life of the workshop building based on the sensor data and the current building health index, and to obtain the predicted building health index and the predicted remaining service life value of the workshop building. The fourth unit is used to select a target maintenance plan from the preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value, so as to maintain the workshop building through the target maintenance plan.

[0075] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.

[0076] As can be seen from the above, the embodiments of this application can improve the service life of workshop buildings and reduce the safety risks of workshop buildings.

[0077] This application also provides an electronic device, which can be a terminal, a server, or other similar device. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers, etc.

[0078] In some embodiments, the product processing device may also be integrated into multiple electronic devices, such as multiple servers, which implement the industrial Internet of Things-based workshop building maintenance method of this application.

[0079] In this embodiment, the electronic device will be described in detail as a terminal, for example, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the terminal 400 involved in an embodiment of this application. Specifically: The terminal 400 may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more media, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will understand that... Figure 4 The terminal 400 structure shown does not constitute a limitation on the terminal 400, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 401 is the control center of the terminal 400. It connects various parts of the terminal 400 via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, thereby providing overall monitoring of the terminal 400. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 401.

[0080] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 mainly includes a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created according to the use of the terminal 400, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0081] The terminal 400 also includes a power supply 403 that supplies power to the various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0082] The terminal 400 may also include an input module 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0083] The terminal 400 may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The terminal 400 can perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing users with wireless broadband Internet access. For example, the communication module 405 can be used to help users send and receive emails, browse web pages, and access streaming media.

[0084] Although not shown, terminal 400 may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, processor 401 in terminal 400 loads the executable files corresponding to the processes of one or more applications into memory 402 according to the following instructions, and processor 401 runs the applications stored in memory 402 to realize various functions, as follows: Acquire sensor data corresponding to key parts of the workshop building; Based on the sensor data, the condition of the workshop building is assessed to obtain the current building health index corresponding to the workshop building; Based on the sensor data and the current building health index, the remaining service life of the workshop building is predicted, and the predicted building health index and the predicted remaining service life value of the workshop building are obtained. Based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life, a target maintenance plan is selected from preset maintenance plans to maintain the workshop building through the target maintenance plan.

[0085] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0086] As can be seen from the above, the embodiments of this application can improve the service life of workshop buildings and reduce the safety risks of workshop buildings.

[0087] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a medium and loaded and executed by a processor.

[0088] To this end, embodiments of this application provide a medium storing multiple instructions that can be loaded by a processor to execute steps in any of the workshop building maintenance methods based on the Industrial Internet of Things provided in embodiments of this application. For example, the instructions can execute the following steps: Acquire sensor data corresponding to key parts of the workshop building; Based on the sensor data, the condition of the workshop building is assessed to obtain the current building health index corresponding to the workshop building; Based on the sensor data and the current building health index, the remaining service life of the workshop building is predicted, and the predicted building health index and the predicted remaining service life value of the workshop building are obtained. Based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life, a target maintenance plan is selected from preset maintenance plans to maintain the workshop building through the target maintenance plan.

[0089] The medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0090] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a medium. A processor of a computer device reads the computer instructions from the medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above embodiments.

[0091] Since the instructions stored in the medium can execute the steps in any of the workshop building maintenance methods based on the Industrial Internet of Things provided in the embodiments of this application, the beneficial effects that any of the workshop building maintenance methods based on the Industrial Internet of Things provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0092] The foregoing has provided a detailed description of a workshop building maintenance method, device, terminal, and medium based on the Industrial Internet of Things (IIoT) provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A workshop building maintenance method based on the Industrial Internet of Things, characterized in that, The method includes: Acquire sensor data corresponding to key parts of the workshop building; Based on the sensor data, the condition of the workshop building is assessed to obtain the current building health index corresponding to the workshop building; Based on the sensor data and the current building health index, the remaining service life of the workshop building is predicted, and the predicted building health index and the predicted remaining service life value of the workshop building are obtained. Based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life, a target maintenance plan is selected from preset maintenance plans to maintain the workshop building through the target maintenance plan.

2. The method as described in claim 1, characterized in that, The sensor data includes monitoring data from multiple sensors; The process of assessing the condition of the workshop building based on the sensor data to obtain the current building health index includes: The real-time confidence value corresponding to the sensor monitoring data is calculated; Based on the changing trends corresponding to the sensor monitoring data, determine the urgency calculation factor corresponding to the sensor monitoring data. Based on the real-time confidence value, the urgency calculation factor, and the preset basic weights corresponding to the sensor monitoring data, the comprehensive weights corresponding to the sensor monitoring data are obtained. The current building health index is obtained based on the sensor monitoring data and the comprehensive weight.

3. The method as described in claim 2, characterized in that, The sensor monitoring data includes the monitoring time and the sensor monitoring value corresponding to the monitoring time; The calculation of the real-time confidence value corresponding to the sensor monitoring data includes: Based on the monitoring time and the sensor monitoring values, a monitoring data sequence is constructed; The standard deviation of the monitoring data sequence was calculated. The absolute value of the linear correlation coefficient between the monitoring data sequence and the preset time index is calculated; By using a predefined function, the standard deviation and the absolute value are mapped to the real-time confidence value.

4. The method as described in claim 2, characterized in that, The comprehensive weight corresponding to the sensor monitoring data is obtained based on the real-time confidence value, the urgency calculation factor, and the preset basic weights corresponding to the sensor monitoring data, including: The comprehensive weight is obtained by multiplying the real-time confidence value, the urgency calculation factor, and the preset basic weight.

5. The method as described in claim 2, characterized in that, The process of obtaining the current building health index based on the sensor monitoring data and the comprehensive weighting includes: The sensor monitoring data is weighted and summed using the comprehensive weights to obtain a first calculation result; Calculate the sum of all the combined weights to obtain the second calculation result; The quotient of the first calculation result and the second calculation result is calculated to obtain the current building health index.

6. The method as described in claim 1, characterized in that, The process of predicting the remaining service life of the workshop building based on the sensor data and the current building health index, to obtain the predicted building health index and the predicted remaining service life value of the workshop building, includes: Based on the sensor data and the current building health index, an initial three-dimensional tensor is constructed. By using a pre-set bidirectional long short-term memory network, temporal features are extracted from the initial three-dimensional vector to obtain a hidden state sequence; By using a preset attention mechanism, a weighted context vector is obtained based on the hidden state sequence, wherein the weighted context vector is used to characterize the attention weight corresponding to the hidden state at each time step in the hidden state sequence; By using a pre-set multilayer perceptron, the weighted context vector is decoded to obtain the predicted building health index and the predicted remaining useful life value.

7. The method as described in claim 1, characterized in that, The preset maintenance scheme includes a first preset maintenance scheme, a second preset maintenance scheme, and a third preset maintenance scheme; The preset threshold includes a preset emergency threshold and a preset warning threshold, wherein the preset emergency threshold is greater than the preset warning threshold; The step of selecting a target maintenance plan from preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life includes: The absolute value of the difference between the current building health index and the predicted building health index is calculated. The maintenance trigger evaluation value corresponding to the workshop building is obtained by multiplying the absolute value of the difference with the predicted remaining service life value. By comparing the maintenance trigger evaluation value, the preset emergency threshold, and the preset early warning threshold, if the maintenance trigger evaluation value is greater than or equal to the preset emergency threshold, then the first preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is greater than or equal to the preset early warning threshold and the maintenance trigger evaluation value is less than the preset emergency threshold, then the second preset maintenance plan is determined as the target maintenance plan; if the maintenance trigger evaluation value is less than the preset early warning threshold, then the third preset maintenance plan is determined as the target maintenance plan.

8. A workshop building maintenance device based on the Industrial Internet of Things, characterized in that, The device includes: The first unit is used to acquire sensor data corresponding to key parts of the workshop building; The second unit is used to assess the condition of the workshop building based on the sensor data and obtain the current building health index corresponding to the workshop building. The third unit is used to predict the remaining service life of the workshop building based on the sensor data and the current building health index, and to obtain the predicted building health index and the predicted remaining service life value of the workshop building. The fourth unit is used to select a target maintenance plan from the preset maintenance plans based on a preset threshold, the current building health index, the predicted building health index, and the predicted remaining service life value, so as to maintain the workshop building through the target maintenance plan.

9. A terminal, characterized in that, The method includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A medium, characterized in that, The medium stores a plurality of instructions adapted for loading by a processor to execute the steps of the method according to any one of claims 1 to 7.

Images