A method, apparatus, device, and medium for data mapping of measurement and control equipment.

Abstract

This invention provides a data mapping method, apparatus, device, and medium for measurement and control equipment, relating to the field of data processing technology. The method includes: acquiring data collected from at least one measurement and control device; wherein the collected data includes data on at least one physical quantity; performing semantic analysis on the collected data to obtain key fields and feature information; obtaining verification rules corresponding to the collected data from a preset standard database based on the key fields; performing consistency verification on the feature information according to the verification rules to obtain a verification result; and, if the verification result indicates a failure, inputting the key fields and feature information into a data mapping model to obtain mapped data output from the collected data. The technical solution of this invention can unify data with different formats and naming rules, facilitating real-time monitoring of measurement and control equipment and improving monitoring efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data mapping method, apparatus, device, and medium for measurement and control equipment. Background Technology

[0002] Industrial measurement and control technology, as a core component of modern industrial production systems, is widely used in key fields such as intelligent manufacturing, energy and power, and transportation. Measurement and control equipment collects various physical quantities such as temperature, pressure, flow rate, and vibration in real time through sensor networks, forming massive time-series data streams, providing data support for production process optimization and equipment condition monitoring.

[0003] Currently, devices from various manufacturers in the market often employ proprietary communication protocols (such as PROFIBUS, Modbus, CANopen, etc.) and data encoding formats. This results in acquired voltage values ​​being represented in different units such as "V", "Volt", or "0.1mV". The same temperature signal may be stored as a floating-point number, integer, or binary code in different devices. This heterogeneity in data representation necessitates complex format conversions and semantic mappings for cross-platform data integration. When building centralized monitoring systems or implementing advanced applications such as predictive maintenance, engineers must write specific data parsing code for each device type, significantly increasing system complexity and maintenance costs. Especially when implementing AI-based intelligent operation and maintenance applications, algorithm models need to process data from different sources with varying representations. This inconsistency directly impacts the quality of feature engineering and the stability of model training. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a data mapping method, device, equipment and medium for measurement and control equipment, which verifies and maps the acquisition equipment of the measurement and control equipment through a preset standard database, unifies the data format and naming rules used for the same physical quantity, facilitates real-time monitoring of the measurement and control equipment and improves monitoring efficiency.

[0005] In a first aspect, embodiments of the present invention provide a data mapping method for measurement and control equipment, comprising:

[0006] Acquire data from at least one measurement and control device; wherein the acquired data includes data of at least one physical quantity;

[0007] Semantic analysis is performed on the collected data to obtain key fields and feature information of the collected data;

[0008] Based on the key fields, obtain the verification rules corresponding to the collected data from a preset standard database;

[0009] The feature information is subjected to consistency verification according to the verification rules to obtain the verification result;

[0010] If the verification result is a verification failure, the key fields and feature information are input into the data mapping model to obtain the mapping data output by mapping the collected data; wherein, the data format and naming rules used for data of the same physical quantity in the mapping data are the same.

[0011] In a preferred embodiment of the present invention, when the verification result is a verification failure, the key fields and feature information are input into the data mapping model to obtain mapped data that maps the collected data, including:

[0012] If the verification result is a verification failure, the corresponding mapping record of the collected data is queried in the historical record database based on the key fields and feature information to obtain the query result;

[0013] If the query result is successful, the collected data is mapped according to the mapping record to obtain the mapping result;

[0014] If the query result is a query failure, the key fields and feature information are input into the data mapping model to map the collected data and obtain the mapped data.

[0015] In a preferred embodiment of the present invention, the data mapping model pre-stores data information description text corresponding to the measurement and control equipment; the step of inputting the key fields and feature information into the data mapping model to map the collected data and obtain mapped data includes:

[0016] The key fields and feature information are input into the data mapping model to obtain the semantic rule base corresponding to the collected data; wherein, the data mapping model can perform text recognition on the data information description text corresponding to the measurement and control equipment to establish the semantic rule base corresponding to the collected data;

[0017] Based on the semantic rule base, the key fields and feature information are mapped to obtain the mapped fields and mapping information, respectively.

[0018] The mapping fields and mapping information are used as mapping data.

[0019] In a preferred embodiment of the present invention, the above-mentioned method of obtaining the verification rules corresponding to the collected data from a preset standard database based on the key fields includes:

[0020] Based on the key field, query the attribute index corresponding to the key field in the standard database;

[0021] Based on the attribute index, the verification rules corresponding to the collected data are obtained from the standard database.

[0022] In a preferred embodiment of the present invention, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output by mapping the collected data in the case of verification failure, the method further includes:

[0023] Based on the key fields, obtain the logical verification rules corresponding to the collected data from a preset standard database;

[0024] The mapping data is logically verified according to the logical verification rules to obtain the logical verification result.

[0025] If the logical verification result fails, the mapping data is marked and an alarm message is generated.

[0026] In a preferred embodiment of the present invention, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output by mapping the collected data in the case of verification failure, the method further includes:

[0027] Based on the key fields, obtain the range verification rules corresponding to the collected data from a preset standard database;

[0028] The range verification is performed on the mapping data according to the range verification rules to obtain the range verification result;

[0029] If the range verification result fails, the mapping data is marked and an alarm message is generated.

[0030] In a preferred embodiment of the present invention, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output by mapping the collected data in the case of verification failure, the method further includes:

[0031] Based on the key fields, obtain the data type verification rules corresponding to the collected data from a preset standard database;

[0032] The data type of the mapped data is validated according to the data type validation rules to obtain the data type validation result.

[0033] If the data type verification result fails, the mapped data is marked and an alarm message is generated.

[0034] Secondly, embodiments of the present invention also provide a data mapping device for measurement and control equipment, comprising:

[0035] A data acquisition module is used to acquire data collected by at least one measurement and control device; wherein the acquired data includes data of at least one physical quantity;

[0036] The information extraction module is used to perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data;

[0037] The rule acquisition module is used to acquire the verification rules corresponding to the collected data from a preset standard database based on the key fields.

[0038] The verification module is used to perform consistency verification on the feature information according to the verification rules and obtain the verification result;

[0039] The mapping module is used to input the key fields and feature information into the data mapping model when the verification result is a verification failure, so as to obtain the mapping data that maps the collected data; wherein the data format and naming rules used for the same physical quantity in the mapping data are the same.

[0040] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the measurement and control device data mapping method of the first aspect described above.

[0041] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the measurement and control equipment data mapping method described in the first aspect.

[0042] The embodiments of the present invention bring the following beneficial effects:

[0043] This invention provides a data mapping method for measurement and control equipment. It involves collecting and validating the data of at least one physical quantity according to rules. If the validation fails, the collected data is mapped to obtain mapped data with a unified data format and naming rules. This method enables the description of the same physical quantity using a unified data format and naming rules, achieving standardized data processing, facilitating monitoring of the measurement and control equipment by relevant personnel, and improving monitoring efficiency.

[0044] Other features and advantages of the invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a data mapping method for measurement and control equipment provided in an embodiment of the present invention;

[0048] Figure 2 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention;

[0049] Figure 3 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention;

[0050] Figure 4 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the structure of a data mapping device for measurement and control equipment provided in an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In industrial production and various complex systems, measurement and control equipment plays a crucial role in real-time monitoring and control of various physical quantities. However, significant differences exist in the data representation of measurement and control equipment from different types and manufacturers, posing a major challenge to the unified management and analysis of data.

[0055] Based on this, embodiments of the present invention provide a data mapping method, apparatus, device, and medium for measurement and control equipment. The data mapping method for measurement and control equipment provided in these embodiments is applicable to a test equipment monitoring platform. The test equipment monitoring platform is used to monitor multiple different types of measurement and control equipment, specifically monitoring the data collected by each equipment, so that staff can promptly identify anomalies or potential problems with the test equipment. In these embodiments, the measurement and control equipment can originate from different manufacturers, and different equipment can be used to collect data on different physical quantities, or they can collect data on the same physical quantity. For example, in a power system, test equipment can be voltage measuring devices produced by different manufacturers. Some may output voltage values ​​directly as numerical values, while others may append unit identifiers to the numerical values. Regarding naming, for phase A voltage, some devices may label it as "Ua," while others may label it as "VA_A," etc. This inconsistency makes it difficult to directly integrate and compare data, requiring significant manual intervention for data mapping and conversion, which is inefficient and prone to errors. When the test equipment monitoring platform receives such data, it can automatically map it using the data mapping method provided in these embodiments, achieving uniformity in data format and naming rules. By using a pre-set standard database to verify and map the acquisition devices of the measurement and control equipment, the data format and naming rules used for the same physical quantity are unified, which facilitates real-time monitoring of the measurement and control equipment and improves monitoring efficiency.

[0056] To facilitate understanding of this embodiment, a data mapping method for measurement and control equipment disclosed in this embodiment of the invention will first be described in detail.

[0057] Example 1

[0058] This invention provides a data mapping method for measurement and control equipment. Figure 1 This is a flowchart illustrating a data mapping method for measurement and control equipment provided in an embodiment of the present invention. Figure 1 As shown, the data mapping method for this measurement and control equipment may include the following steps:

[0059] Step S101: Acquire the data collected by at least one measurement and control device.

[0060] The acquired data includes data on at least one physical quantity. Specifically, the test equipment monitoring platform can receive data from various measurement and control devices in real time. These devices can be sensors, instruments, monitoring equipment, etc. For each measurement and control device, data on at least one physical quantity can be acquired. Different measurement and control devices can acquire data on the same physical quantity or data on different physical quantities. The test equipment monitoring platform has a universal data acquisition protocol converter tailored to the data interface characteristics of different measurement and control devices. It can automatically identify and adapt to various common data interfaces (such as Modbus, CAN, Profibus, etc.), accurately acquire structured data such as field names, values, units, and timestamps from the measurement and control devices, and perform preliminary data cleaning and preprocessing to remove noise and outliers. In this embodiment of the invention, the format of the acquired data from different measurement and control devices varies. For example, it can be structured data, such as JSON and XML format data, or unstructured data, such as binary streams.

[0061] For example, the measurement and control equipment can be the joint sensors of an industrial robot. The collected data from the joint sensors of the industrial robot includes information such as joint angle (unit: degrees), joint torque (unit: N·m), and sampling time. The data format is JSON, as shown in the example below:

[0062] Json

[0063] {

[0064] "joint1_angle": 30.5,

[0065] "joint1_torque": 15.2,

[0066] "timestamp": "2023-11-15 10:30:00"

[0067] }

[0068] Step S102: Perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data.

[0069] Semantic analysis is the process of transforming raw, collected data into structured information that computers can understand. Key fields are the core attributes that uniquely identify the collected data or the crucial identifiers that determine the data processing logic. Feature information refers to the detailed information in the collected data that reflects the specific attributes, states, or numerical characteristics of physical quantities. Test equipment monitoring platforms can use Natural Language Processing (NLP) technology and pattern recognition algorithms to identify key fields (such as physical quantity names, equipment types, and measurement units) and feature information (such as measurement values, timestamps, measurement unit attributes, and data quality) in the collected data. For example, structured data can be directly extracted using a preset key field parsing template, while unstructured data can be extracted using NLP technology.

[0070] Step S103: Based on the key fields, obtain the verification rules corresponding to the collected data from a preset standard database.

[0071] A pre-defined standard database stores metadata such as standard definitions of physical quantities, data format specifications, and verification rules. Query conditions are constructed using key fields to retrieve corresponding verification rules from the standard database. In this embodiment of the invention, verification rules may include at least one of unit conversion rules, data range rules, and data type rules.

[0072] The construction process for a pre-defined standard database is as follows:

[0073] (1) Rule recording

[0074] Based on the standard: The core basis is the data dictionary of measurement and control equipment, which covers the static attributes of measurement and control equipment (such as equipment model or type, manufacturer, date of manufacture, etc.) and dynamic attributes (such as measurement data (equivalent to acquired data), operating status, etc.).

[0075] Rule collection: Collect rules related to each attribute in the data dictionary from authoritative documents such as national standards, industry standards, and enterprise specifications related to measurement and control equipment. For example, for measurement data, collect its reasonable value range, unit conversion rules, etc.; for operating status, collect the meaning of its status identifier and the normal state range, etc.

[0076] Establish a regular update mechanism, conducting a comprehensive review of the rules at preset time intervals to ensure they are in line with the latest technical standards and industry requirements. For example, as the technology of measurement and control equipment in the new energy field iterates, specific rules should be established to promptly obtain high-precision measurement data from the new sensor manuals released by manufacturers.

[0077] Rule organization and entry: The collected rules are organized and entered into the standard database according to the classification structure of the data dictionary. For example, rules related to measurement data are entered into the rule area corresponding to the measurement data attributes in the data dictionary.

[0078] (2) Index creation

[0079] Index Dimensions: To improve rule query speed, indexes are built from multiple dimensions. For example, indexes can be built according to attributes or attribute identifiers in the data dictionary to quickly locate rules related to specific attributes; composite index dimensions such as "equipment model + physical quantity name + time period" can also be added. For example, for temperature data of different types of reactors in the chemical industry, a composite index can be built using "reactor type + temperature + production batch time" to quickly locate rules in specific scenarios.

[0080] Index storage: The created index information is stored in the index table of the standard database to ensure that the relationship between the index and the rules is accurate.

[0081] Step S104: Perform consistency verification on the feature information according to the verification rules to obtain the verification result.

[0082] Consistency checks are used to determine whether the feature information in the collected data conforms to industry standards. For example, in the power industry, the industry standard requires the unit of current to be 'A' (amperes per liter), while the feature information describing the unit of current in the data collected by the measurement and control equipment, i.e., the measurement unit attribute, is 'mA'. In this case, the collected data does not conform to the industry standard.

[0083] Specifically, the feature information is subjected to consistency verification according to the verification rules. If the feature information conforms to the verification rules, the verification result is successful; if the feature information does not conform to the verification rules, the verification result is unsuccessful. When there are multiple verification rules, the verification result of each verification rule can be determined sequentially in a certain order. If the feature information conforms to all verification rules, the verification result is successful; if the feature information does not conform to any one of the verification rules, the verification result is unsuccessful.

[0084] When the verification rule is a unit conversion rule, it determines whether the measurement unit attribute in the feature information is the same as the unit attribute in the unit conversion rule. If they are the same, the verification is successful; otherwise, the verification fails.

[0085] When the verification rule is a data range rule, it determines whether the measured value in the feature information falls within the data range specified in the data range rule. If it does, the verification succeeds; otherwise, it fails. For example, according to the measurement and control equipment data dictionary, the pressure data range corresponding to the pressure sensor is 0 kPa to 10000 kPa. The measured value in the feature information is 1000 kPa, which is within the data range, so the verification succeeds.

[0086] When the verification rule is a data type rule, it checks whether the numerical type in the feature information is the same as the data type in the data type rule. If they are the same, the verification succeeds; otherwise, the verification fails. For example, according to the data dictionary of the measurement and control equipment, the data type of the pressure data corresponding to the pressure sensor is a floating-point number. The measured value in the feature information is 1000 kPa, and the data type is also a floating-point number, which is the same as the data type in the data type rule, so the verification succeeds.

[0087] Step S105: If the verification result is a verification failure, the key fields and feature information are input into the data mapping model to obtain the mapping data output by mapping the collected data.

[0088] A data mapping model is used to map collected data into mapped data that conforms to the verification rules in a preset standard database. In the mapped data, data of the same physical quantity uses the same data format and naming rules. In this embodiment of the invention, the data mapping model can be an AI agent. The data mapping model is set in the test equipment monitoring platform. The AI ​​agent can store a preset standard database and pre-set data formats and naming rules for multiple physical quantities. Based on the input key fields and feature information, the AI ​​agent determines the physical quantity to be mapped and maps the key fields and feature information according to the data format and naming rules of that physical quantity.

[0089] For example, suppose in an industrial production monitoring and control system, there is a pressure sensor used to measure the pressure inside a pipeline. The data measured by this pressure sensor needs to be entered into a report 1. After semantic analysis, the data output by the sensor yields the following results:

[0090] json

[0091] {

[0092] Pressure value: 12000 kPa

[0093] Measurement Time: 2023-11-20 14:30:00

[0094] Sensor ID: PS-001

[0095] }

[0096] Based on the key field "stress value", the corresponding verification rules for the collected data are retrieved from the preset standard database, including data range rules, unit conversion rules, and data type rules.

[0097] Among them, the data range rule is that the reasonable range of pressure data corresponding to this pressure sensor is 0 kPa to 10000 kPa.

[0098] Unit conversion rules: The unified unit for pressure data is "kilopascal (kPa)," but the standard stipulates that it must be displayed in "megapascal (MPa)" in a certain report 1, and 1MPa = 1000kPa.

[0099] Data type rules: The data type for pressure values ​​is floating-point numbers, and the data type for sensor numbers is strings.

[0100] Unit conversion rule verification:

[0101] The pressure values ​​in the collected data are in "kPa", but a certain report 1 requires them to be displayed in "megapascals (MPa)", which does not comply with the unit conversion rules.

[0102] Data range validation rule verification:

[0103] The pressure value of "12000kPa" exceeds the reasonable range of 0kPa to 10000kPa and is marked as abnormal data.

[0104] Logical relationship validation rules verification:

[0105] This example does not involve complex logical relationships, so this step is successful.

[0106] Data type validation rule verification:

[0107] The pressure value "12000" should be considered a floating-point number (represented as 12000.0) in semantic matching and actual data, which meets the data type requirements; the sensor number "PS - 001" is a string type, which meets the data type requirements.

[0108] However, because the collected data did not conform to the unit conversion rules, the verification result was determined to be a verification failure. After inputting the key fields and feature information into the data mapping model, the data mapping model will determine the data format and naming rules corresponding to the pressure value based on the data format and naming rules used by the pre-stored data of multiple physical quantities. Since the collected data did not conform to the unit conversion rules, the unit conversion rules were followed: the unified unit for pressure data is "kilopascal (kPa)," but the standard stipulates that it must be displayed in "megapascal (MPa)" in a certain report 1, and 1 MPa = 1000 kPa. 12000 kPa was converted to 12 MPa to obtain the mapped data.

[0109] The final mapping data is as follows:

[0110] json

[0111] {

[0112] Pressure value: 12 MPa

[0113] Measurement Time: 2023-11-20 14:30:00

[0114] Sensor ID: PS-001

[0115] }

[0116] This invention provides a data mapping method for measurement and control equipment. It involves collecting and validating the data of at least one physical quantity according to rules. If the validation fails, the collected data is mapped to obtain mapped data with a unified data format and naming rules. This method enables the description of the same physical quantity using a unified data format and naming rules, achieving standardized data processing, facilitating monitoring of the measurement and control equipment by relevant personnel, and improving monitoring efficiency.

[0117] Example 2

[0118] This invention also provides another data mapping method for measurement and control equipment; this method is implemented based on the method of the above embodiments; this method focuses on describing the specific implementation of inputting the key fields and feature information into the data mapping model to obtain the mapped data output for mapping the collected data when the verification result is a verification failure.

[0119] Figure 2 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention is shown below. Figure 2 As shown, the data mapping method for this measurement and control equipment may include the following steps:

[0120] Step S201: Acquire data collected by at least one measurement and control device.

[0121] Step S202: Perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data.

[0122] Step S203: Based on the key fields, obtain the verification rules corresponding to the collected data from a preset standard database.

[0123] Step S204: Perform consistency verification on the feature information according to the verification rules to obtain the verification result.

[0124] Step S205: If the verification result is a verification failure, query the mapping record corresponding to the collected data in the historical record database according to the key fields and feature information to obtain the query result.

[0125] Mapping records are used to document the mapping process of historically acquired data. These records include key fields and feature information of the historically acquired data, the verification rules used during the mapping process, and the data format and naming rules used for at least one physical quantity. A historical record database stores multiple mapping records. Specifically, if the verification fails, the key fields and feature information are matched against the mapping records in the historical record database. If the match is successful, it indicates that the testing equipment monitoring platform has performed a mapping operation on the acquired data of the testing and control equipment, and the query result is considered successful. If the match fails, it indicates that the testing equipment monitoring platform has not performed a mapping operation on the acquired data of the testing and control equipment, and the query result is considered failed. For example, matching the key fields and feature information against the mapping records in the historical record database can be done by matching at least one of the following: physical quantity name, equipment type, measurement unit in the key fields, and measurement unit attribute in the feature information, against the physical quantity name, equipment type, measurement unit in the key fields, and measurement unit attribute in the feature information of the historically acquired data. If they match, the query is successful; if they do not match, the query fails.

[0126] Step S206: If the query result is successful, the collected data is mapped according to the mapping record to obtain the mapping result.

[0127] If the query result is successful, the collected data is mapped according to the verification rules in the mapping record and the data format and naming rules used by at least one physical quantity, to obtain the mapped data.

[0128] For example, suppose in an industrial production monitoring and control system, there is a pressure sensor used to measure the pressure inside a pipeline. The data measured by this pressure sensor needs to be entered into a report 1. After semantic analysis, the data output by the sensor yields the following results:

[0129] json

[0130] {

[0131] Pressure value: 12000 kPa

[0132] Measurement Time: 2023-11-20 15:30:00

[0133] Sensor ID: PS-001

[0134] }

[0135] Based on the key fields and feature information, the corresponding mapping records for the collected data are queried in the historical record database to obtain the historical collected data that matches the collected data as follows:

[0136] json

[0137] {

[0138] Pressure value: 12000 kPa

[0139] Measurement Time: 2023-11-20 14:30:00

[0140] Sensor ID: PS-001

[0141] }

[0142] The mapping record of the historical data collection includes: Unit conversion rules: The unified unit for pressure data is "kilopascal (kPa)," but the standard stipulates that it must be displayed in "megapascal (MPa)" in a certain report 1, and 1MPa = 1000kPa.

[0143] The final mapping data is as follows:

[0144] json

[0145] {

[0146] Pressure value: 12 MPa

[0147] Measurement Time: 2023-11-20 15:30:00

[0148] Sensor ID: PS-001

[0149] }

[0150] Step S207: If the query result is a query failure, the key fields and feature information are input into the data mapping model to map the collected data and obtain the mapped data.

[0151] Specifically, key fields and feature information are input into a data mapping model to obtain a semantic rule base corresponding to the collected data. The data mapping model pre-stores descriptive text for data information corresponding to the measurement and control equipment; the data mapping model can perform text recognition on the descriptive text for the data information corresponding to the measurement and control equipment to establish the semantic rule base corresponding to the collected data; based on the semantic rule base, the key fields and feature information are mapped to obtain mapped fields and mapped information, respectively. The mapped fields and mapped information are used as mapped data.

[0152] The data mapping method for measurement and control equipment provided in this embodiment of the invention can prioritize querying mapping records when the verification result is a verification failure. When a mapping record is found, the collected data can be mapped directly without using a data mapping model, thereby reducing the number of times the data mapping model is used, saving the time used to call the data mapping model, and improving mapping efficiency.

[0153] Example 3

[0154] This invention also provides another data mapping method for measurement and control equipment; this method is implemented based on the method in the above embodiments; this method focuses on describing the specific implementation of inputting the key fields and feature information into the data mapping model to map the collected data and obtain the mapped data.

[0155] Figure 3 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention is shown below. Figure 3 As shown, the data mapping method for this measurement and control equipment may include the following steps:

[0156] Step S301: Acquire data collected by at least one measurement and control device.

[0157] Step S302: Perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data.

[0158] Step S303: Based on the key fields, obtain the verification rules corresponding to the collected data from the preset standard database.

[0159] Specifically, the verification rules can be obtained through steps A1-A2.

[0160] Step A1: Query the attribute index corresponding to the key field in the standard database based on the key field.

[0161] Step A2: Based on the attribute index, obtain the verification rules corresponding to the collected data from the standard database.

[0162] Specifically, the attribute index is used to indicate the storage location of the verification rules corresponding to the collected data in the standard database. An index table is stored in the pre-defined standard database, recording the correspondence between key fields and verification rules, as well as the storage location of the verification rules corresponding to the key fields in the standard database. For example, based on the device model and physical quantity name in the key fields, the index table is queried for attribute indexes that match the device model and physical quantity name. The verification rule stored at the location indicated by the attribute index is then used as the verification rule corresponding to the collected data.

[0163] Querying the corresponding verification rules for collected data through attribute indexes can improve the efficiency and accuracy of verification rule queries.

[0164] Step S304: Perform consistency verification on the feature information according to the verification rules to obtain the verification result.

[0165] Step S305: If the verification result is a verification failure, input the key fields and feature information into the data mapping model to obtain the semantic rule library corresponding to the collected data.

[0166] The data mapping model pre-stores data information description text corresponding to the measurement and control equipment; the data mapping model can perform text recognition on the data information description text corresponding to the measurement and control equipment and establish the semantic rule base corresponding to the collected data.

[0167] The data description text corresponding to the measurement and control equipment can be unstructured text such as equipment manuals, operation manuals, and maintenance logs. The data mapping model can use advanced OCR technology and deep learning-based NLP technology to perform text recognition on the data description text, extract semantic information such as field definitions and measurement ranges, and establish a temporary semantic rule base to provide rich semantic support for subsequent data mapping. The field definition can be "joint1_angle" corresponding to the angle data of joint 1, in degrees. The measurement range can be understood as the data range of a physical quantity.

[0168] Specifically, in the event of a verification failure, key fields and feature information are input into the data mapping model. The model can determine the corresponding data information description text for the measurement and control equipment based on the equipment type in the key fields, and then perform text recognition to establish a semantic rule base. Furthermore, based on the physical quantity names in the key fields, the model can identify the semantic information corresponding to the physical quantity names from the data information description text, and establish a semantic rule base. It is understandable that the data information description text can include semantic information about a large number of physical quantities. However, in different application scenarios, the measurement and control equipment may only collect data from a subset of physical quantities. The data mapping model can perform text recognition only on the semantic information of the physical quantities that need to be collected, establishing a semantic rule base, thereby reducing the size of the semantic rule base and improving its efficiency.

[0169] Step S306: Based on the semantic rule base, map the key fields and feature information to obtain the mapped fields and mapping information respectively.

[0170] Mapping fields refer to the results obtained after mapping key fields. Mapping information refers to the results obtained after mapping feature information. Specifically, in the semantic rule base, the mapping relationship between key fields and feature information is queried sequentially, and the key fields and feature information are mapped to obtain mapping fields and mapping information. If a mapping relationship for a certain key field or feature information does not exist in the semantic rule base, no mapping is performed.

[0171] Step S307: Use the mapping field and mapping information as mapping data.

[0172] For example, the data acquired by the joint sensor is as follows:

[0173] json

[0174] {

[0175] "joint1_angle": 30.5,

[0176] "joint1_torque": 15.2,

[0177] "timestamp": "2023-11-15 10:30:00"

[0178] }

[0179] Mapped to a unified rule, the output is as follows:

[0180] json

[0181] {

[0182] "Uniform Rule_Joint 1_Angle": 30.5,

[0183] "Unified Rules_Joint 1_Torque": 15.2,

[0184] Mapping Time: 2023-11-15 10:30:00

[0185] }

[0186] The data mapping method for measurement and control equipment provided in this invention uses a data mapping model to perform text recognition on the data information description text of the measurement and control equipment. This automatically generates a semantic rule base, eliminating the need for manual writing of mapping rules and reducing rule omissions or configuration errors caused by manual operation. It is particularly suitable for complex measurement and control scenarios involving multiple devices and multiple physical quantities. The semantic rule base can define standardized mapping rules, ensuring that the same physical quantity data from different measurement and control devices uses a consistent format and naming rules. The data information description text can flexibly define the data characteristics of different devices. The model dynamically generates the rule base through text recognition, eliminating the need to develop a dedicated mapping module for each device. When the standards or protocols of the measurement and control equipment are updated, only the data information description text needs to be modified (such as adding data fields or adjusting unit conversion rules). The model can automatically regenerate the semantic rule base without modifying the underlying logic of the mapping model, reducing system maintenance costs.

[0187] Example 4

[0188] This invention also provides another data mapping method for measurement and control equipment; this method is implemented based on the method of the above embodiments; the method focuses on describing the specific implementation of inputting the key fields and feature information into the data mapping model to obtain the mapped data output after mapping the collected data when the verification result is a verification failure.

[0189] Figure 4 A flowchart of another data mapping method for measurement and control equipment provided in an embodiment of the present invention is shown below. Figure 4 As shown, the data mapping method for this measurement and control equipment may include the following steps:

[0190] Step S401: Acquire data collected by at least one measurement and control device.

[0191] Step S402: Perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data.

[0192] Step S403: Based on the key fields, obtain the verification rules corresponding to the collected data from a preset standard database.

[0193] Step S404: Perform consistency verification on the feature information according to the verification rules to obtain the verification result.

[0194] Step S405: If the verification result is a verification failure, the key fields and feature information are input into the data mapping model to obtain the mapping data output by mapping the collected data.

[0195] Step S406: Based on the key fields, obtain the logical verification rules corresponding to the collected data from a preset standard database.

[0196] Logical verification rules refer to the logical relationship rules for retrieving collected data from a standard database based on business requirements and the working principles of measurement and control equipment. For example, in a power measurement and control system, there is a specific relationship between voltage, current, and power (power = voltage × current). The data dictionary records the rules corresponding to this logical relationship. If the physical quantity names of the collected data from the measurement and control equipment include voltage, current, and power, the logical verification rules corresponding to voltage, current, and power can be retrieved from a preset standard database.

[0197] Step S407: Perform logical verification on the mapping data according to the logical verification rules to obtain the logical verification result.

[0198] Logical verification rules are used to check whether logical relationships are satisfied between multiple collected data. If not, the reasons are analyzed, which may include data acquisition errors, semantic matching errors, or equipment malfunctions. Since there is a one-to-one correspondence between the mapped data and the collected data, the logical relationships between the collected data are the same as those between the mapped data. Logical verification rules can be used to perform logical verification on the mapped data to obtain the logical verification results.

[0199] Specifically, the mapped data is fed into the logical verification rules to determine whether the mapped data meets the verification rules. If it does, the logical verification result is determined to be a failure; otherwise, the logical verification result is determined to be a success.

[0200] For example, the collected data shows a voltage of 220V and a current of 5A, with a calculated power of 1200W. However, the collected data shows a power of 1100W, which deviates significantly from the calculated power value. (A power error threshold can be preset; if the difference between the collected power and the calculated power exceeds the error threshold, the power value is considered to have a large deviation.) This does not meet the logic verification rules, and the logic verification result is determined to be a failure, indicating a possible data problem.

[0201] Step S408: If the logical verification result fails, the mapping data is marked and an alarm message is generated.

[0202] Alarm messages describe problems with the mapped data, requiring manual intervention. For example, an alarm message may include at least the mapped data, and this message can be displayed on the testing equipment monitoring platform. The alarm message may also include a pre-set reason, such as a logical error or abnormal data acquisition if a logic verification fails. Alarms can be sent via system messages, emails, SMS, etc., ensuring relevant personnel are promptly informed of data anomalies.

[0203] Furthermore, in the case of a verification failure, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output by mapping the collected data, the method further includes: obtaining the range verification rule corresponding to the collected data from a preset standard database based on the key fields; performing range verification on the mapped data according to the range verification rule to obtain the range verification result; and marking the mapped data and generating alarm information in the case of a range verification failure.

[0204] The range verification rule is used to check whether the mapped data is within a reasonable range. If the mapped data is within a reasonable range, the range verification result is considered successful; if the mapped data is outside the range verification result, the range verification result is considered unsuccessful, the mapped data is marked, and an alarm message is generated.

[0205] For example, for temperature measurement data from specific monitoring and control equipment, according to the rules in the equipment specifications and standard database, the reasonable range for its value is specified to be between -50℃ and 150℃. If the converted temperature value falls within this range, it meets the requirements, and the range verification result is successful. If the converted temperature value does not fall within this range, it does not meet the requirements, the range verification result is unsuccessful, the mapped data is marked, and an alarm message is generated. The alarm reason in the alarm message can be preset to "range exceeded".

[0206] Furthermore, in the case of a verification failure, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output by mapping the collected data, the method further includes: obtaining the data type verification rules corresponding to the collected data from a preset standard database based on the key fields; performing data type verification on the mapped data according to the data type verification rules to obtain a data type verification result; and marking the mapped data and generating alarm information in the case of a data type verification failure.

[0207] Data type validation rules are used to validate the data type of the mapping information in the mapping data. If the data type in the mapping information is the same as the data type in the data type validation rules, the data type validation result is successful; otherwise, the data type validation result is unsuccessful.

[0208] For example, from a standard database, the data type requirements for each data field are obtained according to the rules of the corresponding attributes in the data dictionary, such as integer, floating-point number, string, boolean value, etc. For instance, for the device number data in the mapping information, the data dictionary specifies that its data type is string.

[0209] The system checks the actual data type of the device number in the mapping information. If the actual data type is a string, the data type verification result is successful; otherwise, the data type verification result is unsuccessful. The mapped data is then marked, and an alarm message is generated. The alarm reason in the alarm message can be preset to type mismatch.

[0210] The technical solution of this invention constructs a protective network from three dimensions—"business logic rationality," "numerical physical rationality," and "format compatibility"—through logic verification, range verification, and type verification, forming a closed-loop process of "verification-mapping-re-verification." Compared to a single verification mechanism, this improves the accuracy of verification and reduces the cost of manual data verification. Simultaneously, real-time anomaly marking and alarms enhance the response speed of the testing equipment monitoring platform.

[0211] Example 5

[0212] Corresponding to the above method embodiments, this invention provides a data mapping device for measurement and control equipment. Figure 5 This is a schematic diagram of the structure of a data mapping device for measurement and control equipment provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the data mapping device for the measurement and control equipment may include:

[0213] The data acquisition module 501 is used to acquire data collected by at least one measurement and control device; wherein the acquired data includes data of at least one physical quantity.

[0214] The information extraction module 502 is used to perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data;

[0215] The rule acquisition module 503 is used to acquire the verification rules corresponding to the collected data from a preset standard database based on the key fields.

[0216] The verification module 504 is used to perform consistency verification on the feature information according to the verification rules and obtain the verification result.

[0217] The mapping module 505 is used to input the key fields and feature information into the data mapping model when the verification result is a verification failure, so as to obtain the mapping data that maps the collected data; wherein the data format and naming rules used for the same physical quantity in the mapping data are the same.

[0218] This invention provides a data mapping device for measurement and control equipment. It collects and verifies the data of at least one physical quantity according to rules. If the verification fails, it maps the collected data to obtain mapped data with unified data format and naming rules. This enables the description of the same physical quantity using a unified data format and naming rules, achieving standardized data processing, facilitating relevant personnel to monitor the measurement and control equipment, and improving monitoring efficiency.

[0219] In some embodiments, the mapping module 505 is further configured to:

[0220] If the verification result is a verification failure, the corresponding mapping record of the collected data is queried in the historical record database based on the key fields and feature information to obtain the query result;

[0221] If the query result is successful, the collected data is mapped according to the mapping record to obtain the mapped data;

[0222] If the query result is a query failure, the key fields and feature information are input into the data mapping model to map the collected data and obtain the mapped data.

[0223] In some embodiments, the data mapping model pre-stores data information description text corresponding to the measurement and control equipment; the mapping module 505 is further configured to:

[0224] The key fields and feature information are input into the data mapping model to obtain the semantic rule base corresponding to the collected data; wherein, the data mapping model can perform text recognition on the data information description text corresponding to the measurement and control equipment to establish the semantic rule base corresponding to the collected data;

[0225] Based on the semantic rule base, the key fields and feature information are mapped to obtain the mapped fields and mapping information, respectively.

[0226] The mapping fields and mapping information are used as mapping data.

[0227] In some embodiments, the rule acquisition module 503 is further configured to:

[0228] Based on the key field, query the attribute index corresponding to the key field in the standard database;

[0229] Based on the attribute index, the verification rules corresponding to the collected data are obtained from the standard database.

[0230] In some embodiments, the device further includes:

[0231] The logic verification rule acquisition module is used to acquire the logic verification rule corresponding to the collected data from a preset standard database based on the key field.

[0232] The logic verification module is used to perform logical verification on the mapping data according to the logic verification rules and obtain the logic verification result.

[0233] The first alarm module is used to mark the mapping data and generate alarm information when the logic verification result fails.

[0234] In some embodiments, the device further includes:

[0235] The range verification rule acquisition module is used to acquire the range verification rule corresponding to the collected data from a preset standard database based on the key field.

[0236] The range verification module is used to perform range verification on the mapping data according to the range verification rules and obtain the range verification result.

[0237] The second alarm module is used to mark the mapping data and generate alarm information when the range verification result fails.

[0238] In some embodiments, the device further includes:

[0239] The type validation rule acquisition module is used to obtain the data type validation rule corresponding to the collected data from a preset standard database based on the key field.

[0240] The type verification module is used to perform data type verification on the mapped data according to the data type verification rules and obtain the data type verification result.

[0241] The third alarm module is used to mark the mapped data and generate alarm information when the data type verification result fails.

[0242] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0243] Example 6

[0244] This invention also provides an electronic device for running the above-described measurement and control equipment data mapping method; see [link to previous document]. Figure 6 The diagram shows the structure of an electronic device, which includes a memory 600 and a processor 601. The memory 600 stores one or more computer instructions, which are executed by the processor 601 to implement the aforementioned data mapping method for measurement and control equipment.

[0245] Furthermore, Figure 6 The electronic device shown also includes a bus 602 and a communication interface 603, with the processor 601, communication interface 603 and memory 600 connected via the bus 602.

[0246] The memory 600 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 603 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 602 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0247] Processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 601 or by instructions in software form. Processor 601 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 600. Processor 601 reads information from memory 600 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0248] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the aforementioned data mapping method for measurement and control equipment. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0249] The computer program product for the method of mapping measurement and control equipment data provided in this embodiment of the invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0250] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0251] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0252] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0253] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0254] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0255] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A telemetry device data mapping method, comprising: include: Acquire data from at least one measurement and control device; wherein the acquired data includes data of at least one physical quantity; Semantic analysis is performed on the collected data to obtain key fields and feature information of the collected data; Based on the key fields, obtain the verification rules corresponding to the collected data from a preset standard database; The feature information is subjected to consistency verification according to the verification rules to obtain the verification result; If the verification result is a verification failure, the corresponding mapping record of the collected data is queried in the historical record database based on the key fields and feature information to obtain the query result; If the query result is successful, the collected data is mapped according to the mapping record to obtain the mapped data; If the query result is a query failure, the key fields and feature information are input into the data mapping model to map the collected data and obtain the mapped data; wherein, the data format and naming rules used for data with the same physical quantity in the mapped data are the same.

2. The method of claim 1, wherein, The data mapping model pre-stores data information description text corresponding to the measurement and control equipment; the step of inputting the key fields and feature information into the data mapping model to map the collected data and obtain mapped data includes: The key fields and feature information are input into the data mapping model to obtain the semantic rule base corresponding to the collected data; wherein, the data mapping model can perform text recognition on the data information description text corresponding to the measurement and control equipment to establish the semantic rule base corresponding to the collected data; Based on the semantic rule base, the key fields and feature information are mapped to obtain the mapped fields and mapping information, respectively. The mapping fields and mapping information are used as mapping data.

3. The method of claim 1, wherein, The step of obtaining the verification rules corresponding to the collected data from a preset standard database based on the key fields includes: Based on the key field, query the attribute index corresponding to the key field in the standard database; Based on the attribute index, the verification rules corresponding to the collected data are obtained from the standard database.

4. The method of claim 1, wherein, In the event that the verification result is a verification failure, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output after mapping the collected data, the method further includes: Based on the key fields, obtain the logical verification rules corresponding to the collected data from a preset standard database; The mapping data is logically verified according to the logical verification rules to obtain the logical verification result. If the logical verification result fails, the mapping data is marked and an alarm message is generated.

5. The method of claim 1, wherein, In the event that the verification result is a verification failure, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output after mapping the collected data, the method further includes: Based on the key fields, obtain the range verification rules corresponding to the collected data from a preset standard database; The range verification is performed on the mapping data according to the range verification rules to obtain the range verification result; If the range verification result fails, the mapping data is marked and an alarm message is generated.

6. The method of claim 1, wherein, In the event that the verification result is a verification failure, after inputting the key fields and feature information into the data mapping model to obtain the mapped data output after mapping the collected data, the method further includes: Based on the key fields, obtain the data type verification rules corresponding to the collected data from a preset standard database; The data type of the mapped data is validated according to the data type validation rules to obtain the data type validation result. If the data type verification result fails, the mapped data is marked and an alarm message is generated.

7. A data mapping device for measurement and control equipment, characterized in that, include: A data acquisition module is used to acquire data collected by at least one measurement and control device; wherein the acquired data includes data of at least one physical quantity; The information extraction module is used to perform semantic analysis on the collected data to obtain the key fields and feature information of the collected data; The rule acquisition module is used to acquire the verification rules corresponding to the collected data from a preset standard database based on the key fields. The verification module is used to perform consistency verification on the feature information according to the verification rules and obtain the verification result; The mapping module is used to: query the mapping record corresponding to the collected data in the historical record database according to the key fields and feature information when the verification result is a verification failure, and obtain the query result; if the query result is a successful query, map the collected data according to the mapping record to obtain the mapped data; if the query result is a query failure, input the key fields and feature information into the data mapping model to map the collected data to obtain the mapped data; wherein, the data format and naming rules used for data of the same physical quantity in the mapped data are the same.

8. An electronic device, comprising: It includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the measurement and control equipment data mapping method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the data mapping method for measurement and control equipment as described in any one of claims 1 to 6.

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