Engineering monitoring data processing method and device, electronic equipment and medium

Abstract

The invention provides an engineering monitoring data processing method and device, electronic equipment and a medium, and the method comprises the steps: obtaining original monitoring data from a measurement point according to a preset time interval or a condition triggered by a specific event; the original monitoring data are calculated through a preset calculation formula, intermediate parameters are generated, and the intermediate parameters are used for representing specific physical quantities or states; calling one or more related intermediate parameters according to specific requirements of a target test item; and performing mathematical operation and logic processing on the called intermediate parameter to generate a target monitoring value representing the current state or the change trend of the target monitoring item. Through the mode, the efficiency and the accuracy of data processing are improved, the real-time response capability and the user interaction experience of the monitoring system are enhanced, and powerful data support is provided for engineering safety.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data acquisition and fusion, and in particular to a method, device, electronic equipment and medium for processing engineering monitoring data. Background Art

[0002] Engineering monitoring is a key link in ensuring the safety of engineering construction and operation. It covers the entire engineering life cycle from design, construction to maintenance. Monitoring data not only provides a scientific basis for engineering decision-making, but also tracks the progress and quality of the project in real time, and warns of potential structural defects, playing a vital role in the long-term maintenance and management of the project. Despite the continuous advancement of monitoring technology, existing data processing methods still have some limitations.

[0003] In many monitoring systems, intermediate data, that is, raw data that has been preliminarily processed but has not yet formed a final result, is often not properly stored. This practice ignores the potential value of intermediate data in data analysis and decision support, and also leads to a waste of data resources. When multiple projects need to use the same intermediate data, due to the lack of a sharing mechanism, each project must independently collect and process the data, which not only increases the workload but also reduces monitoring efficiency.

[0004] In addition, in traditional monitoring processes, the two types of data, manual and automated measurements, are usually processed separately, lacking a unified framework to integrate and manage these data. This practice not only limits the sharing and reuse of data, but can also lead to inconsistency and inefficiency in data processing. For example, when a project needs to combine manual and automated monitoring data, repeated calculations and analysis may be required due to the lack of an effective data integration mechanism, increasing resource consumption and monitoring costs. Summary of the invention

[0005] The present invention provides a method, device, electronic device and medium for processing engineering monitoring data, aiming to improve the processing efficiency of monitoring data, reduce resource waste, and ultimately improve the overall performance of engineering monitoring.

[0006] The first aspect of the present disclosure provides a method for processing engineering monitoring data, comprising:

[0007] Obtain raw monitoring data from measurement points at predetermined time intervals or under conditions triggered by specific events;

[0008] The raw monitoring data is calculated by a preset calculation formula to generate an intermediate parameter, wherein the intermediate parameter is used to represent a specific physical quantity or state;

[0009] According to the specific requirements of the target measurement item, call one or more related intermediate parameters;

[0010] Mathematical operations and logical processing are performed on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

[0011] In an embodiment of the first aspect, when the original monitoring data is manually input by a user, the acquisition process includes:

[0012] In response to a user's input operation, selecting a collection record template matching the target measurement item from a plurality of predefined collection record templates;

[0013] Receive the original monitoring data input by the user on the selected acquisition record template.

[0014] In an embodiment of the first aspect, when the original monitoring data is automatically uploaded by a collector, the acquisition process includes:

[0015] Read the identification information provided by the collector, the identification information including the manufacturer, model and code number;

[0016] Compare the read identification information with the predefined device registration information to determine the device type;

[0017] According to the identified device type, a data parsing function code matching the type of the acquisition instrument is selected from a predefined data parsing function library;

[0018] Based on the selected data parsing function code, the data packet uploaded by the collector is parsed to obtain the original monitoring data.

[0019] In an embodiment of the first aspect, the data packet uploaded by the collector is a collection value of a sensor, and the process of matching the sensor collection value with the measurement value type in the target measurement item includes:

[0020] Extracting sensor identification information from the data packet, the sensor identification information including the sensor code, name, model and manufacturer;

[0021] According to the extracted sensor identification information, searching for configuration information matching the sensor in a predefined sensor configuration information library; wherein the sensor configuration information library includes the type of sensor, the measurement type and their corresponding relationship;

[0022] Based on the found sensor configuration information, determine the measurement type corresponding to the sensor collection value;

[0023] Based on the determined measurement value type, the collected value of the sensor is matched with the corresponding measurement value type in the target measurement item to ensure the correct attribution of the collected value.

[0024] In an embodiment of the first aspect, after the original monitoring data is obtained, the method further includes preprocessing the original monitoring data, including:

[0025] Performing unit conversion on the raw monitoring data of different sources or types to achieve consistency of data units;

[0026] The number of decimal places of the original monitoring data of different sources or types is adjusted to achieve a unified data numerical format.

[0027] In an embodiment of the first aspect, performing mathematical operations and logical processing on the intermediate parameters of the call includes:

[0028] According to the predefined intermediate parameter tree structure, the intermediate parameters with the largest depth are identified and calculated preferentially;

[0029] Each intermediate parameter is calculated in sequence from bottom to top until the target monitoring value is obtained.

[0030] In an embodiment of the first aspect, after generating the target monitoring value, the method further includes:

[0031] Setting one or more alarm thresholds for the target measurement item to evaluate whether the target monitoring value exceeds a normal range;

[0032] When the target monitoring value exceeds the set alarm threshold, an alarm is triggered;

[0033] The alarm content is displayed on the manual interaction interface so that monitoring personnel can understand abnormal situations in a timely manner and take corresponding measures.

[0034] A second aspect of the present disclosure discloses a device for processing engineering monitoring data, comprising:

[0035] An acquisition module is used to acquire raw monitoring data from a measuring point at a predetermined time interval or under conditions triggered by a specific event;

[0036] An intermediate parameter generation module, used to calculate the original monitoring data through a preset calculation formula to generate intermediate parameters, wherein the intermediate parameters are used to represent a specific physical quantity or state;

[0037] A calling module, used to call one or more related intermediate parameters according to the specific requirements of the target measurement item;

[0038] The target monitoring value generation module is used to perform mathematical operations and logical processing on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

[0039] The third aspect of the present disclosure discloses an electronic device, comprising: a processor and a memory; wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for processing engineering monitoring data as described in any one of the first aspects.

[0040] A fourth aspect of the present disclosure discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the method for processing engineering monitoring data described in any one of the first aspects.

[0041] As described above, the present disclosure provides a method, device, electronic device and medium for processing engineering monitoring data, which at least include the following technical effects:

[0042] (1) By converting the original monitoring data into intermediate parameters and then generating the target monitoring values ​​through mathematical operations and logical processing, the automation and accuracy of data processing are improved. The use of intermediate parameters enhances the consistency and traceability of data, simplifies the data processing process, and improves monitoring efficiency.

[0043] (2) For the automatically uploaded raw monitoring data, by reading the identification information of the collector and matching the predefined device registration information, the automatic identification and parsing of data uploaded by different collectors is realized, which improves the compatibility and flexibility of data collection and ensures that data from different devices can be accurately parsed and processed.

[0044] (3) By preprocessing the raw monitoring data, including unit conversion and decimal place adjustment, the data format is standardized, ensuring that data from different sources or types have a consistent format before entering the subsequent processing flow, thereby improving the efficiency and accuracy of data processing.

[0045] (4) By identifying the intermediate parameter tree structure and giving priority to calculating the intermediate parameters with the greatest depth, the order and efficiency of parameter calculation are optimized, the logic and accuracy of data processing are ensured, and the accuracy and reliability of target monitoring value calculation are improved.

[0046] (5) By setting alarm thresholds and triggering alarms, real-time monitoring and early warning of abnormal situations are achieved, which improves the response speed and safety of the monitoring system and ensures that monitoring personnel can understand and deal with potential engineering problems in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart showing a method for processing engineering monitoring data in an embodiment of the present disclosure is shown.

[0048] Figure 2A schematic diagram showing a process of manually inputting raw monitoring data by a user in an embodiment of the present disclosure.

[0049] Figure 3 A measurement value entry interface in an example of the present disclosure is shown.

[0050] Figure 4 A schematic diagram showing a process of automatically uploading raw monitoring data through a collector in one embodiment of the present disclosure.

[0051] Figure 5 The device type of the collector in an example of the present disclosure and the corresponding parsing code are shown.

[0052] Figure 6 A schematic diagram showing a flow chart of matching sensors with measurement types in one embodiment of the present disclosure.

[0053] Figure 7 An interface for binding collected values ​​and measured values ​​of a sensor in an example of the present disclosure is shown.

[0054] Figure 8 A calculation formula in an example of the present disclosure is shown.

[0055] Fig. 9 A schematic diagram showing a flow chart of performing mathematical operations and logical processing on intermediate parameters of a call in one embodiment of the present disclosure.

[0056] Fig.10 A schematic diagram of a module of a processing device for engineering monitoring data in an embodiment of the present disclosure is shown.

[0057] Fig.11 A schematic diagram of the circuit structure of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0058] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0059] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0060] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0061] like Figure 1 As shown, a flow chart of a method for processing engineering monitoring data in an embodiment of the first aspect of the present disclosure is shown, including steps S10-S50, wherein:

[0062] Step S10: acquiring raw monitoring data from the measuring points at predetermined time intervals or according to conditions triggered by specific events.

[0063] Specifically, in the field of engineering monitoring and data collection, a "measurement point" refers to a specific location or point used to monitor and measure specific physical quantities or environmental parameters. The system automatically collects data from each measurement point according to a pre-set time interval. This time interval may be fixed, such as once an hour, day, or week, or it may be based on specific conditions, such as seasonal changes, weather conditions, or project progress. In addition to timed collection, the system can also respond to specific events to trigger data collection. These events may include, but are not limited to, structural vibrations, sudden temperature changes, pressure changes, etc., which are usually closely related to engineering safety indicators.

[0064] In some embodiments, there are two ways to obtain the original monitoring data, one is manual input, and the other is automatic uploading by a data collector.

[0065] When the original monitoring data is manually input by the user, such as Figure 2 As shown, the acquisition process includes steps S111 to S112, wherein:

[0066] Step S111: In response to a user input operation, a collection record template matching the target measurement item is selected from a plurality of predefined collection record templates.

[0067] Specifically, in order to standardize and simplify the data entry process, the system usually predefines multiple collection record templates. Each template is designed for a specific type of monitoring project or measurement item and contains a series of input fields that correspond to the type of data to be collected.

[0068] The user operates through the user interface (UI) to indicate the need to enter monitoring data for a specific target measurement item. This operation can be triggered by clicking a button, selecting a menu item, entering a command, or any other interactive method.

[0069] The system displays a list of predefined multiple acquisition record templates, each with its own specific identification or description so that the user can identify its applicability. The user selects a best-matching acquisition record template from the list based on the characteristics of the target measurement item. For example, if the target measurement item is "dam displacement monitoring", the user will select a template that contains displacement-related fields. The user's selection is recorded and confirmed by the system, and the selected template is loaded into the data entry interface, ready to receive data.

[0070] For example, Figure 3 As shown, the input interface of elevation measurement is displayed.

[0071] Step S112: receiving the original monitoring data input by the user on the selected acquisition record template.

[0072] Specifically, the selected acquisition record template contains multiple input fields, each field corresponding to a specific parameter of the raw monitoring data. For example, the fields may include date, time, sensor reading, environmental conditions, operator notes, etc. The user enters the raw monitoring data in the corresponding fields of the template according to the actual monitoring situation.

[0073] When all necessary data has been entered, the user can instruct the system to save the data. The system will store the data in a database for subsequent processing and analysis.

[0074] When the original monitoring data is automatically uploaded by the collector, Figure 4 As shown, the acquisition process includes steps S121 to S124, wherein:

[0075] Step S121: Read the identification information provided by the acquisition instrument, the identification information including the manufacturer, model and code number.

[0076] Specifically, the system starts the communication process with the data collector and prepares to receive data. The system reads identification information from the data collector through the communication interface. This information usually includes: Manufacturer: indicates the manufacturer of the data collector; Model: indicates the specific model of the data collector; Code: a unique serial number or identification code used to distinguish different data collectors.

[0077] Step S122: Compare the read identification information with the predefined device registration information to determine the device type.

[0078] Specifically, the device registration information database is accessed, which contains the manufacturer, model, code number and corresponding device type of all known collectors. The identification information read in step S121 is compared with the registration information in the database to find a matching record. If a matching record is found, the system will determine the device type of the collector.

[0079] Step S123: According to the identified device type, a data analysis function code matching the type of the acquisition instrument is selected from a predefined data analysis function library.

[0080] Specifically, a predefined data analysis function library is accessed, which contains analysis function codes for different data acquisition instrument models. According to the device type determined in step S122, a matching data analysis function code is searched in the function library. For example, Figure 5 As shown, the device type of a collector and the corresponding parsing code are displayed.

[0081] Step S124: Based on the selected data parsing function code, the data packet uploaded by the data collector is parsed to obtain original monitoring data.

[0082] Specifically, data packets are received from the data collector, which usually contain raw monitoring data. The data packets are parsed using the selected data parsing function code. The parsing function extracts the raw monitoring data from the data packet and converts it into a format that can be further processed and stored by the system.

[0083] In some implementations, the system also verifies the parsed data to ensure the integrity and accuracy of the data.

[0084] Furthermore, the data packet uploaded by the data collector is the collected value of the sensor, such as Figure 6 As shown, the process of matching the sensor acquisition value with the measurement value type in the target measurement item includes steps S131 to S134, wherein:

[0085] Step S131: extracting sensor identification information from the data packet, wherein the sensor identification information includes the sensor code, name, model and manufacturer.

[0086] Specifically, the header or metadata portion of the data packet is first parsed, which usually contains the sensor identification information. The system extracts the following sensor identification information from the data packet:

[0087] Code: A unique serial number or identification code used to identify a specific sensor; Name: The common name or identifier of the sensor; Model: The model of the sensor, indicating its specifications and functions; Manufacturer: The name of the manufacturer that produced the sensor.

[0088] Step S132: searching for configuration information matching the sensor in a predefined sensor configuration information library according to the extracted sensor identification information; wherein the sensor configuration information library includes sensor types, measurement types and their corresponding relationships.

[0089] Specifically, a predefined sensor configuration information library is accessed, which contains configuration information of all known sensors. The sensor identification information extracted in step S131 is used as a search keyword to search for a matching configuration record in the configuration information library. Once a matching record is found, the system will obtain the following configuration information:

[0090] Sensor type: the type of sensor, such as temperature sensor, pressure sensor, etc.

[0091] Measurement type: The type of data collected by the sensor, such as temperature, humidity, pressure, etc.

[0092] Correspondence: The mapping relationship between sensor type and measurement value type.

[0093] Step S133: Based on the found sensor configuration information, determine the measurement type corresponding to the sensor collection value.

[0094] Step S134: Based on the determined measurement value type, the collected value of the sensor is matched with the corresponding measurement value type in the target measurement item to ensure the correct attribution of the collected value.

[0095] like Figure 7 As shown, the interface showing the binding of the sensor's collected value and measured value is displayed, where the sensor measured value in the figure is the liquid level and the intermediate parameter is the water level.

[0096] In order to better illustrate the above implementation method, the following will take the groundwater level monitoring project outside the foundation pit as an example to describe how to obtain the original monitoring data through sensors and collectors.

[0097] Assume that in a foundation pit project, multiple piezometer sensors are installed to monitor the changes in groundwater levels outside the foundation pit. These sensors are arranged at different depths to monitor the changes in groundwater levels in real time. The piezometer sensors are installed at predetermined locations outside the foundation pit, and each sensor is connected to a collector. Each sensor is calibrated before installation to ensure its measurement precision and accuracy. The sensor's identification information (code, name, model, manufacturer) is entered into the system's sensor configuration information library.

[0098] The sensor begins to monitor the change of groundwater level. The sensor converts the water level change into electrical signals, which may be analog signals or digital signals. The sensor may perform preliminary processing on the signal, such as amplification, filtering, or analog-to-digital conversion (if the sensor output is an analog signal). The collector collects the sensor signal wirelessly or wired. The collector packages the data according to the preset data reporting protocol (such as the MQTT Internet of Things protocol) and prepares it for upload.

[0099] The collector automatically uploads the packaged data to the central monitoring system or cloud platform. On the central monitoring system or cloud platform, the system selects the data parsing function code that matches the collector type from the predefined data parsing function library based on the identification information in the data packet uploaded by the collector. The system uses the selected data parsing function code to parse the data packet and extract the original monitoring data.

[0100] The system then searches for configuration information that matches the sensor in the predefined sensor configuration information library based on the sensor identification information in the data packet. Based on the found sensor configuration information, the system determines the measurement type corresponding to the sensor acquisition value, such as the groundwater level value. The parsed raw monitoring data is bound to the groundwater level value to ensure the accuracy and traceability of the data. The bound data is stored in the database and associated with the corresponding sensors and measurement points for subsequent analysis and report generation.

[0101] In some embodiments, after obtaining the original monitoring data, the process further includes preprocessing the original monitoring data, including:

[0102] Performing unit conversion on the raw monitoring data of different sources or types to achieve consistency of data units;

[0103] The number of decimal places of the original monitoring data of different sources or types is adjusted to achieve a unified data numerical format.

[0105] Specifically, since raw monitoring data from different sources or types may use different measurement units (such as meters, centimeters, millimeters), the system needs to perform unit conversion operations to unify all data into a standard unit system. This ensures compatibility and comparability between data, facilitating subsequent analysis and processing. For example, if some data is in feet and other data is in meters, the system converts all of these data into the International System of Units (SI) standard, such as meters.

[0106] In addition, the number of decimal places in the original monitoring data may vary depending on the accuracy of the sensor or the source of the data. In order to achieve a unified data value format, the system will adjust the number of decimal places for these data. This step helps ensure data consistency and reduce calculation errors or misunderstandings caused by differences in decimal places. For example, the system may adjust the number of decimal places for all data to two or three to meet specific engineering requirements or standards.

[0107] Step S20: Calculate the original monitoring data using a preset calculation formula to generate intermediate parameters, where the intermediate parameters are used to represent specific physical quantities or states.

[0108] Specifically, the system will calculate the pre-processed data according to preset calculation formulas. These formulas may be based on physical laws, engineering experience and historical data, and are used to convert raw data into intermediate parameters.

[0109] These intermediate parameters are usually associated with specific monitoring objectives or safety standards. This association can be one-to-one or many-to-one, depending on the needs and complexity of the monitoring project.

[0110] For example, assuming that in a dam monitoring project, the original monitoring data include the displacement, stress and crack width of the dam, these data are converted into intermediate parameters, namely "displacement parameters", "stress parameters" and "crack width parameters", etc., to facilitate subsequent analysis of the stability and safety of the dam.

[0111] This embodiment converts the raw monitoring data collected from the sensor or data acquisition system into meaningful intermediate parameters, which can represent specific physical quantities or states and provide a basis for further data analysis and decision-making.

[0112] It should be noted that in order to improve the scalability of the system, the online configuration function of the calculation formula is completed by using the dynamic code of the page in this embodiment. This function completely solves the hard-coded writing mode of the traditional monitoring platform and performs independent configuration for different parameters. Among them, the dynamic code is based on the JAVA language, and dynamically compiles according to the function code edited on the page, generates bytecode, and uses the JVM virtual machine for encoding in real time, fully integrating the framework code, realizing online configuration and online modification, and the system can complete the configuration change of the calculation logic without upgrading.

[0113] For example, Figure 8 As shown in the figure, this section represents the calculation formula of vertical displacement (i.e. ground subsidence), and the operation process is:

[0114] (1) Determine whether it is the first measurement:

[0115] If it is the first measurement, the vertical displacement is calculated using the "Current Elevation" and "Initial Elevation". If it is not the first measurement, the vertical displacement is calculated using the "Current Elevation" and "Last Elevation".

[0116] (2) Calculate vertical displacement:

[0117] Vertical displacement = (current elevation - comparison elevation) * 1000, where the multiplication by 1000 is to convert the unit from meters to millimeters, which makes it easier to observe subtle changes.

[0118] (3) Update information:

[0119] Record the calculated vertical displacement value and use the "current elevation" as the "last elevation" for the next calculation.

[0120] (4) Ensure that other data is also calculated:

[0121] Call other methods to ensure that other ground height related data is also calculated and processed.

[0122] Step S30: according to the specific requirements of the target test item, call one or more related intermediate parameters.

[0123] Specifically, analyze the needs of specific monitoring objectives and determine which intermediate parameters are needed to evaluate these objectives. Select intermediate parameters related to the monitoring objectives from the database. For example, if the goal is to monitor the stability of a dam, you may need to call "displacement parameters" and "stress parameters". Retrieve the current values ​​or historical data of these intermediate parameters from the database. Perform further processing on the retrieved data, such as trend analysis, statistical analysis, or model prediction.

[0124] For example, in the above-mentioned dam monitoring project, according to the monitoring requirements of dam stability, the system may call "displacement parameters" and "stress parameters".

[0125] Step S40: performing mathematical operations and logical processing on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

[0126] Specifically, the retrieved data is further processed, such as trend analysis, statistical analysis, or model prediction. Based on the processed data, target monitoring values ​​are generated. These values ​​can be real-time or predicted values ​​based on historical data.

[0127] Exemplarily, mathematical operations and logical processing are performed on the "displacement parameters" and "stress parameters" of the dam to generate a target monitoring value for the dam's stability, a "dam safety index".

[0128] In some embodiments, the mathematical operations and logical processing are performed on the intermediate parameters of the call, such as Fig. 9 As shown, it includes steps S41 to S42, wherein:

[0129] Step S41: According to the predefined intermediate parameter tree structure, identify and preferentially calculate the intermediate parameter with the greatest depth.

[0130] Specifically, the system identifies all intermediate parameters and their interdependencies based on a predefined intermediate parameter tree structure. This usually involves building a parameter dependency graph, where each node represents an intermediate parameter and the edges represent the dependencies between parameters.

[0131] In the parameter tree structure, the parameters with the greatest depth (i.e. the parameters at the bottom) will be calculated first. The reason for this is that the calculation results of these parameters are often dependent on other parameters, so their values ​​must be determined first.

[0132] The system will perform predetermined mathematical operations and logical processing on these intermediate parameters with the greatest depth, such as addition, subtraction, multiplication, division, exponentiation, logarithm, etc. These operations are based on preset calculation formulas and may involve linear or nonlinear transformations to ensure that the data can accurately reflect the actual status of the monitored object.

[0133] Step S42: Calculate each intermediate parameter in sequence from bottom to top until the target monitoring value is obtained.

[0134] Specifically, the system will calculate each intermediate parameter from the bottom to the top according to the dependency relationship of the parameters in the parameter tree structure. This order ensures that before calculating any parameter, the values ​​of all the lower-level parameters on which it depends have been calculated and determined.

[0135] During the calculation process, the system may perform more complex data processing, such as data interpolation, error analysis, etc. These processing steps help improve the accuracy and reliability of the data.

[0136] Finally, the system will generate target monitoring values ​​based on the calculation results of all intermediate parameters. These values ​​directly reflect the current status or change trend of the target measurement items of the monitoring project, providing a scientific basis for engineering decision-making.

[0137] In some implementations, after generating the target monitoring value, the method further includes:

[0138] One or more alarm thresholds are set for the target measurement item to evaluate whether the target monitoring value exceeds the normal range; when the target monitoring value exceeds the set alarm threshold, an alarm is triggered; the alarm content is displayed on the manual interaction interface so that the monitoring personnel can understand the abnormal situation in time and take corresponding measures.

[0139] Specifically, the system sets one or more alarm thresholds for each target measurement item. These thresholds are determined based on engineering safety standards, historical data, and expert experience, and are used to assess whether the target monitoring value is within the normal range. For example, in a bridge health monitoring system, the alarm thresholds for displacement, stress, and other parameters may be set based on the design parameters and usage conditions of the bridge.

[0140] Once the target monitoring value is generated, the system will immediately compare it with the set alarm threshold. This evaluation process is automated, ensuring real-time response to monitoring data. If the target monitoring value exceeds the set alarm threshold, the system will trigger an alarm. This may mean that the engineering structure is experiencing changes beyond expectations and immediate action is required.

[0141] The triggered alarm will be displayed on the manual interaction interface, including the alarm type, severity, occurrence time and possible impact. This design enables monitoring personnel to quickly understand the abnormal situation and take appropriate measures. The display of alarm content is intended to help monitoring personnel understand the abnormal state of the engineering structure in a timely manner and take appropriate emergency measures according to the severity of the alarm, such as increasing the monitoring frequency, on-site inspection or taking reinforcement measures.

[0142] It should be particularly noted that the process or method represented by the flowchart of the above embodiment of the present disclosure can be understood as representing a module, fragment or part of a code including one or more sets of executable instructions configured to implement the steps of a specific logical function or process. And the scope of the preferred embodiment of the present disclosure includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0143] like Fig.10 As shown, the processing device 100 of the engineering monitoring data disclosed in the present invention is shown. It should be noted that the principle and technical implementation of the processing device of engineering monitoring data can refer to the processing method of engineering monitoring data in the previous embodiment (for example Figure 1 ), so it will not be repeated in this embodiment.

[0144] Specifically, the engineering monitoring data processing device 100 includes: an acquisition module 101, an intermediate parameter generation module 102, a calling module 103, and a target monitoring value generation module 104, wherein:

[0145] The acquisition module 101 is used to acquire raw monitoring data from the measuring points according to a predetermined time interval or a condition triggered by a specific event;

[0146] The intermediate parameter generating module 102 is used to calculate the original monitoring data by a preset calculation formula to generate an intermediate parameter, wherein the intermediate parameter is used to represent a specific physical quantity or state;

[0147] The calling module 103 is used to call one or more related intermediate parameters according to the specific requirements of the target measurement item;

[0148] The target monitoring value generating module 104 is used to perform mathematical operations and logical processing on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

[0149] It should be noted that Fig.10 Each functional module in the embodiment can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a program instruction product. The program instruction product includes one or a group of program instructions. When the program instruction is loaded and executed on a computer, the process or function according to the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The program instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0150] and, Fig.10 The device disclosed in the embodiment can be implemented by other module division methods. The device embodiments shown above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as a group of modules or modules can be combined or dynamically added to another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0151] in addition, Fig.10 Each functional module and submodule in the embodiment can be dynamically in a processing component, or each module can exist physically separately, or two or more modules can be dynamically in one component. The above-mentioned dynamic components can be implemented in the form of hardware or in the form of software functional modules. If the above-mentioned dynamic components are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0152] like Fig.11 As shown, a schematic diagram of the structure of an electronic device in an embodiment of the present disclosure is shown.

[0153] The electronic device can execute the following steps by running the computer program instructions: Figure 1 The method in any one of the items. Exemplarily, the electronic device may be a server group / server, a desktop computer, a laptop computer, etc., or a server / server group in the cloud that communicates remotely with a local terminal, a distributed computing node system, etc.

[0154] The electronic device 110 includes a bus 111, a processor 112, and a memory 113. The processor 112 and the memory 113 can communicate with each other through the bus 111. The memory 113 can store program instructions. The processor 112 implements the method steps in the previous embodiment by running the program instructions in the memory 113, such as Figure 1 Any method.

[0155] The bus 111 may be a peripheral component interconnect standard bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, although only one thick line is used in the figure, it does not mean that there is only one bus or one type of bus.

[0156] In some embodiments, the processor 112 may be implemented as a central processing unit (CPU), a microprocessing unit (MCU), a system on chip (System On Chip), or a field programmable logic array (FPGA). The memory 113 may include a volatile memory (Volatile Memory) for temporary storage of data when running a program, such as a random access memory (Random Access Memory, RAM).

[0157] The memory 113 may also include a non-volatile memory for data storage, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state disk (SSD).

[0158] In some embodiments, the electronic device 110 may further include a communicator 114. The communicator 114 is used to communicate with the outside. In a specific example, the communicator 114 may include one or a group of wired and / or wireless communication circuit modules. For example, the communicator 114 may include one or more of a wired network card, a USB module, a serial interface module, etc. The wireless communication protocols followed by the wireless communication module include: for example, near field wireless communication (NFC) technology, infrared (IR) technology, global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division code division multiple access (TD-SCDMA), long term evolution (LTE), Bluetooth (BT), global navigation satellite system (GNSS), etc. One or more.

[0159] In an embodiment of the present disclosure, a computer-readable storage medium may be provided, wherein program instructions are stored, and the program instructions are executed by running, for example Figure 1 The method for processing engineering monitoring data in the embodiment.

[0160] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or are implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded via a network and to be stored in a local recording medium, so that the method represented herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA).

[0161] In summary, the processing method, device, electronic device and medium of engineering monitoring data disclosed in the present invention first obtain data from the measuring point regularly, or trigger data collection when a specific event occurs. The data may be manually input or automatically uploaded by the collector. For the automatically uploaded data, the data packet is parsed by reading the collector identification information and matching the predefined parsing function library. The collected raw data is then preprocessed, including unit conversion and decimal adjustment, to achieve standardization. Subsequently, the data is converted into intermediate parameters through preset formulas, which represent specific physical quantities or states. The system calls the relevant parameters according to the needs, performs calculations to generate target monitoring values, and reflects the engineering status or trend. This processing method not only improves the efficiency and accuracy of data processing, but also enhances the real-time response capability and user interaction experience of the monitoring system, providing strong data support for engineering safety.

[0162] The above embodiments are merely illustrative of the principles and effects of the present disclosure, and are not intended to limit the present disclosure. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present disclosure. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present disclosure shall still be covered by the claims of the present disclosure.

Claims

1. A method for processing engineering monitoring data, characterized in that: include: Obtain raw monitoring data from measurement points at predetermined time intervals or under conditions triggered by specific events; The raw monitoring data is calculated by a preset calculation formula to generate an intermediate parameter, wherein the intermediate parameter is used to represent a specific physical quantity or state; According to the specific requirements of the target measurement item, call one or more related intermediate parameters; Mathematical operations and logical processing are performed on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

2. The method for processing engineering monitoring data according to claim 1, characterized in that: When the original monitoring data is manually input by the user, the acquisition process includes: In response to a user's input operation, selecting a collection record template matching the target measurement item from a plurality of predefined collection record templates; Receive the original monitoring data input by the user on the selected acquisition record template.

3. The method for processing engineering monitoring data according to claim 1, characterized in that: When the original monitoring data is automatically uploaded by the data collector, the acquisition process includes: Read the identification information provided by the collector, the identification information including the manufacturer, model and code number; Compare the read identification information with the predefined device registration information to determine the device type; According to the identified device type, a data parsing function code matching the type of the acquisition instrument is selected from a predefined data parsing function library; Based on the selected data parsing function code, the data packet uploaded by the collector is parsed to obtain the original monitoring data.

4. The method for processing engineering monitoring data according to claim 3, characterized in that: The data packet uploaded by the collector is the collected value of the sensor. The process of matching the collected value of the sensor with the measurement value type in the target measurement item includes: Extracting sensor identification information from the data packet, the sensor identification information including the sensor code, name, model and manufacturer; According to the extracted sensor identification information, searching for configuration information matching the sensor in a predefined sensor configuration information library; wherein the sensor configuration information library includes the type of sensor, the measurement type and their corresponding relationship; Based on the found sensor configuration information, determine the measurement type corresponding to the sensor collection value; Based on the determined measurement value type, the collected value of the sensor is matched with the corresponding measurement value type in the target measurement item to ensure the correct attribution of the collected value.

5. The method for processing engineering monitoring data according to claim 1, characterized in that: After the original monitoring data is obtained, the original monitoring data is preprocessed, including: Performing unit conversion on the raw monitoring data of different sources or types to achieve consistency of data units; The number of decimal places of the original monitoring data of different sources or types is adjusted to achieve a unified data numerical format.

6. The method for processing engineering monitoring data according to claim 1, characterized in that: The performing of mathematical operations and logical processing on the intermediate parameters of the call includes: According to the predefined intermediate parameter tree structure, the intermediate parameters with the largest depth are identified and calculated preferentially; Each intermediate parameter is calculated in sequence from bottom to top until the target monitoring value is obtained.

7. The method for processing engineering monitoring data according to claim 1, characterized in that: After generating the target monitoring value, it also includes: Setting one or more alarm thresholds for the target measurement item to evaluate whether the target monitoring value exceeds a normal range; When the target monitoring value exceeds the set alarm threshold, an alarm is triggered; The alarm content is displayed on the manual interaction interface so that monitoring personnel can understand abnormal situations in a timely manner and take corresponding measures.

8. A device for processing engineering monitoring data, characterized in that: include: An acquisition module is used to acquire raw monitoring data from a measuring point at a predetermined time interval or under conditions triggered by a specific event; An intermediate parameter generation module, used to calculate the original monitoring data through a preset calculation formula to generate intermediate parameters, wherein the intermediate parameters are used to represent a specific physical quantity or state; A calling module, used to call one or more related intermediate parameters according to the specific requirements of the target measurement item; The target monitoring value generation module is used to perform mathematical operations and logical processing on the called intermediate parameters to generate a target monitoring value representing the current state or change trend of the target measurement item.

9. An electronic device, characterized in that: The electronic device comprises: Processor and memory; Wherein, the memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the method for processing engineering monitoring data according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for processing engineering monitoring data described in any one of claims 1 to 7 is implemented.

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