Multi-protocol dynamic adaptive industrial equipment data integration system and method

Abstract

The invention discloses a multi-protocol dynamic adaptive industrial equipment data integration system and method, and relates to the field of industrial equipment data processing, and the system comprises an identification module which is used for receiving a protocol signal sent by industrial equipment, analyzing a protocol type identifier carried in the signal, and synchronously creating a data connection channel between the equipment and the system; the generation module is used for automatically generating corresponding data interaction adaptation logic based on the signal analysis result in the identification module; according to the method, optimized interaction adaptation logic is generated by combining equipment protocol parameters and historical interaction data, reliability and real-time performance of data interaction are improved, invalid fragments are filtered during data collection, collection frequency is dynamically adjusted, data quality and equipment load balance are considered, different protocol data are processed in a standardized mode according to a unified model so as to achieve format unification, and data interaction efficiency is improved. Storage resources are dynamically allocated according to the data generation frequency and the key degree, the associated index is established, and the storage utilization rate and the data retrieval efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial equipment data processing, in particular to a multi-protocol dynamic adaptation industrial equipment data integration system and method. BACKGROUND

[0002] With the deepening of the industrial 4.0 strategy and the popularization of the intelligent manufacturing system, the types of equipment in industrial scenarios are increasingly diverse, covering PLC, sensors, frequency converters, industrial robots and other types of terminals, and different manufacturers often use differentiated communication protocols, such as Modbus, Profinet, OPCUA, EtherNet / IP, etc. In order to realize the transparent control of the production process, real-time monitoring of the equipment state and in-depth analysis of the production data, enterprises need to integrate the data scattered in different equipment into a unified management platform. At present, the industry mostly develops custom interfaces, deploys special protocol conversion gateways or uses fixed protocol integration solutions to preliminarily meet the equipment data interconnection and intercommunication needs in some scenarios.

[0003] The application number 202410522475.0 discloses a device dynamic self-adaption multi-source heterogeneous data safe and reliable collection method, which aims to solve the problem that most of the multi-source heterogeneous industrial equipment data collection in the prior art is point-to-point, that is, each industrial equipment needs to be collected separately, which is very labor-intensive and has high maintenance cost in the later period. On the other hand, the equipment collection may be affected by the working conditions, causing the collected data information to be fine-tuned, which may bring huge adjustment workload to the upper layer application.

[0004] However, how to solve the problems of multi-protocol adaptation difficulty, non-standardization of data, unreasonable storage allocation, and high external application adaptation cost in industrial automation data integration is still the technical core research and development goal in the current field.

[0005] Therefore, we propose a multi-protocol dynamic adaptation industrial equipment data integration system and method. SUMMARY

[0006] In view of the above shortcomings of the prior art, the present application provides a multi-protocol dynamic adaptation industrial equipment data integration system and method, which can effectively solve the problems of the prior art.

[0007] To achieve the above purpose, the present application realizes the following technical scheme; The present application discloses a multi-protocol dynamic adaptation industrial equipment data integration system, comprising: The identification module is used for receiving a protocol signal sent by the industrial equipment, analyzing a protocol type identifier carried in the signal, and synchronously creating a data connection channel between the equipment and the system; the generation module is used for automatically generating corresponding data interaction adaptation logic based on the signal analysis result in the identification module; the collection module is used for obtaining the interaction adaptation logic generated in the generation module, applying the interaction adaptation logic to collect real-time running data from the industrial equipment connected by itself, and synchronously filtering invalid data segments in the collection process; the normalization module is used for receiving the running data collected by the collection module in real time, performing structure standardization processing on data of different protocol sources according to a preset unified data model, and obtaining a unified format data set; the storage module is used for receiving the unified format data set, dynamically allocating storage resources based on data generation frequency and key degree, and synchronously creating an association index; the adaptation module is used for obtaining a calling target of an external application system, calling corresponding data in the storage module, dynamically adapting an interface protocol of the external application, and outputting; The identification module is connected with the generation module through a wireless network interaction, the generation module is connected with the collection module, the normalization module and the storage module through a wireless network interaction, and the storage module is connected with the adaptation module through a wireless network interaction. The data interaction adaptation logic includes data analysis logic, format conversion logic and transmission control logic.

[0008] Further, when the identification module analyzes the protocol type identifier, a characteristic field at a preset position in the protocol signal is extracted, and the protocol type is determined by matching the characteristic field with a system pre-stored protocol characteristic library. After the data connection channel is created, the identification module sends a heartbeat detection signal at a preset period, and if a heartbeat response feedback from the industrial equipment is not received for a continuous preset number of times, the protocol signal analysis and data connection channel creation process are re-executed. The characteristic field at the preset position in the protocol signal includes a protocol version number, a data frame start identifier and a device manufacturer code.

[0009] Further, before the generation module generates the data interaction adaptation logic, protocol inherent parameters and historical interaction data of the industrial equipment are preferentially collected, and an adaptation logic optimization coefficient is calculated based on the collected parameters and data, and the generation module determines the final data interaction adaptation logic according to the value priority of the adaptation logic optimization coefficient; The protocol inherent parameters include a data frame length, a check algorithm type and a data bit width, and the historical interaction data includes a historical data frame analysis success rate and a historical format conversion error number. ; In the formula: is the adaptation logic optimization coefficient. is the historical data frame analysis success rate. 、 is a preset weight coefficient; is a format conversion time consumption of the current adaptation logic draft; is a preset format conversion time consumption threshold; The generation module is provided with a preset optimization coefficient comparison threshold, when K is not less than the preset optimization coefficient comparison threshold, the generation module directly determines the adaptation logic draft as the final data interaction adaptation logic; if K is less than the preset optimization coefficient comparison threshold, the data parsing logic and the format conversion logic are adjusted based on the protocol inherent parameters, K is recalculated until K is not less than the preset optimization coefficient comparison threshold; The current adaptation logic draft is a version of the adaptation logic that has not been optimized and verified, which is initially constructed by the generation module based on the initially collected protocol parameters and the historical interaction data before the final data interaction adaptation logic is determined; 、 are positive numbers and the sum is 1.

[0010] Further, when the collection module filters invalid data segments, it is subject to: determining whether the collected data meets a preset verification logic, the preset verification logic at least includes CRC verification and parity verification, and verification failure is determined as invalid data; determining whether the value of the collected data is within a preset range of the corresponding operating parameters of the industrial equipment, and determining as invalid data if it is out of range; The collection module dynamically adjusts the data collection frequency based on the real-time operating state of the industrial equipment: In the formula: is the adjusted collection frequency; is a preset basic collection frequency; is the amount of device data generated by the collection module in real time, measured in pieces per second; is a preset data amount threshold; 、 is a preset adjustment coefficient; is the real-time load of the device; is a preset load threshold; The collection module recalculates f every preset time period and performs data collection operations according to the updated f.

[0011] Further, the preset unified data model in the normalization module includes a data identification layer, a data attribute layer, and a data value layer. The data identification layer is used to store unique identification information, including device ID, data collection timestamp, and parameter code. The data attribute layer is used to store data element information, including data type, data unit, and accuracy level.​ The data value layer is used for storing the collected device operation data original values; When the normalization module performs the structure standardization processing, three layers of information are extracted from data of different protocols, and information mapping and filling are completed according to the hierarchical structure of the unified data model; If the data attribute layer information of some protocol data is missing, the missing data attribute layer information is automatically completed based on the pre-stored device parameter attribute library, and after the completion, unit uniform conversion is performed on the original values of the data value layer to obtain a unified format data set with consistent structure.

[0012] Further, when the storage module dynamically allocates storage resources, the total amount of storage resource requirements of the data to be stored is calculated synchronously: ; In the formula: is the total amount of storage resource requirements of the data to be stored; is a preset basic storage resource unit; is the generation frequency of the data; is the data importance weight; is a preset resource redundancy coefficient; According to the , the storage module allocates a storage space with a corresponding capacity from the idle storage resources of the device system: If the idle storage resources are insufficient, the storage resources of part of non-critical data are released in order from low to high according to the data importance weight, until the requirements of are met; When the storage module creates an associated index, the device ID and data collection timestamp in the data identification layer of the unified data model are used as core index fields to construct a two-level index structure, and through the index structure, all operation data of any device within a specified time period can be located.

[0013] Further, when the adaptation module obtains the calling target of the external application system, it is subject to: If the external application system initiates a data calling request through an API interface, the adaptation module parses the fields of the target device ID, target data parameter, and data time range in the API request header to determine the calling target; If the external application system obtains data in a preset push mode, the adaptation module receives a push configuration request sent in advance by the external application system, extracts the fields of the target device list, the concerned parameter list, and the data push frequency from the request to determine the calling target; The adaptive module dynamically adapts to the external application interface protocol stage, identifies the interface protocol type of the external application, calls the system built-in protocol conversion component, converts the uniform format data set called from the storage module into a data format conforming to the external application interface protocol requirements, and then performs data transmission output after the conversion is completed.

[0014] Further, during the running of the modules in the system, the data flow state between the modules is synchronously monitored, and the module cooperation efficiency is calculated. When the module cooperation efficiency is less than a preset efficiency threshold, the resource scheduling instruction is sent to the module with the longest time consumption by the cooperation optimization module, the computing resources of the module, including the CPU occupancy rate and the memory allocation amount, are increased, and the transmission link bandwidth between the module and other modules is optimized, until the module cooperation efficiency is not less than the preset efficiency threshold. The module cooperation efficiency is calculated by the following formula: ; In the formula, is the processing time consumption of the i-th module, i=1, 2,..., N; is the delay of the d-th transmission link, d=1, 2,..., M; is the number of modules participating in data flow; is the preset maximum allowed processing time consumption of a single module; is the number of data transmission links between modules; is the preset maximum allowed transmission delay of a single link.

[0015] On the other hand, a multi-protocol dynamic adaptation industrial equipment data integration method, comprising: Receiving the protocol signal sent by the industrial equipment, analyzing the protocol type identifier in the signal and creating a data connection channel, while sending a heartbeat detection signal at a preset period, and if no response is received for a continuous preset number of times, the analysis and channel creation operation is re-executed; collecting the protocol inherent parameters and historical interaction data of the industrial equipment, calculating the adaptation logic optimization coefficient, adjusting the data analysis and format conversion logic according to the comparison result of the coefficient and the preset threshold, to determine the final data interaction adaptation logic; collecting real-time running data of the industrial equipment by applying the interaction adaptation logic, filtering invalid data according to the verification logic and parameter preset range, and dynamically adjusting the data collection frequency based on the device data generation and load; extracting the collected data in a unified data model, completing the missing attribute information and unifying the data unit, completing the structure standardization processing to obtain a uniform format data set; calculating the storage resource requirement of the uniform format data set, dynamically allocating storage resources based on the data generation frequency and importance, and synchronously constructing a two-level association index based on the device ID and the collection timestamp; analyzing the calling target of the external application system to call the corresponding data.

[0016] Compared with the known prior art, the technical scheme provided by the application has the following beneficial effects: The application can automatically identify industrial equipment protocol signals and establish data connection, combine equipment protocol parameters and historical interaction data to generate optimized interaction adaptation logic, improve the reliability and real-time performance of data interaction, filter invalid fragments when collecting data and dynamically adjust the collection frequency, balance the data quality and equipment load, standardize different protocol data according to a unified model to realize format unification, dynamically allocate storage resources and establish an associated index according to the data generation frequency and criticality, improve the storage utilization and data retrieval efficiency, and can also adapt to external application interface protocol to output data to meet various calling requirements, monitor the data flow state and collaborative efficiency, timely allocate resources to ensure stable and efficient operation of the system, effectively improve the flexibility, accuracy and overall efficiency of industrial equipment data integration, and reduce the data integration cost to a certain extent. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical scheme in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0018] Figure 1 It is a structural schematic diagram of a multi-protocol dynamic adaptation industrial equipment data integration system. Figure 2 It is a flowchart of a multi-protocol dynamic adaptation industrial equipment data integration method. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0020] The application will be further described below in combination with the embodiments.

[0021] Embodiment 1 A multi-protocol dynamic adaptation industrial equipment data integration system of the present embodiment, as shown in Figure 1 It comprises: The identification module is used to receive protocol signals sent by industrial equipment, parse the protocol type identifier carried in the signal, and synchronously create a data connection channel between the equipment and the system. When the identification module parses the protocol type identifier, it extracts the feature field at a preset position in the protocol signal and determines the protocol type by the matching degree between the feature field and the system's pre-stored protocol feature library. After the data connection channel is created, the identification module sends a heartbeat detection signal at a preset cycle. If no heartbeat response is received from the industrial equipment for a preset number of consecutive times, the protocol signal parsing and data connection channel creation process is re-executed. Among them, the feature fields at preset positions in the protocol signal include the protocol version number, the data frame start identifier, and the device manufacturer code; The generation module is used to automatically generate the corresponding data interaction adaptation logic based on the signal analysis results in the recognition module. Before generating the data interaction adaptation logic, the generation module prioritizes collecting the inherent protocol parameters and historical interaction data of the industrial equipment, and calculates the adaptation logic optimization coefficient based on the collected parameters and data. The generation module determines the final data interaction adaptation logic according to the priority of the adaptation logic optimization coefficient values. The protocol's inherent parameters include data frame length, verification algorithm type, and data bit width; historical interaction data includes historical data frame parsing success rate and historical format conversion error count. ; In the formula: To adapt the logic optimization coefficients; For historical data frame parsing success rate; , Preset weighting coefficients; The time consumed by the format conversion of the current adaptation logic draft; Set a preset time threshold for format conversion; The generation module has a preset optimization coefficient comparison threshold. When K is not less than the preset optimization coefficient comparison threshold, the generation module directly determines the adaptation logic draft as the final data interaction adaptation logic. If K is less than the preset optimization coefficient comparison threshold, the data parsing logic and format conversion logic are adjusted based on the inherent parameters of the protocol, and K is recalculated until K is not less than the preset optimization coefficient comparison threshold. The above formulas and their corresponding logical settings are based on the historical data frame parsing success rate and the format conversion time of the current adaptation logic draft. The parsing and conversion logic is optimized according to the inherent parameters of the protocol. If the iteration still does not meet the requirements, the backup adaptation template is called to ensure that the adaptation logic fits the scenario requirements and ensures parsing accuracy and real-time interaction. Among them, the current adaptation logic draft is the adaptation logic version that the generation module initially constructs based on the initially collected protocol parameters and historical interaction data before determining the final data interaction adaptation logic; , All are positive numbers, and their sum is 1; When industrial scenarios demand higher accuracy in data parsing The larger the value, the lower the requirement for parsing success rate in the scenario. The smaller the value, the higher the real-time requirements for data interaction in industrial scenarios. The larger the value, the lower the real-time requirement of the scenario. The smaller the value; If K is less than the preset optimization coefficient comparison threshold, the process of adjusting the data parsing logic and format conversion logic based on the inherent parameters of the protocol and recalculating K includes the following example: For the data parsing logic: If the data frame length in the protocol's inherent parameters is greater than the preset frame length threshold, the frame segmentation method in the parsing logic will be changed from "fixed length segmentation" to "dynamic identifier segmentation". It achieves segmentation by identifying the frame end identifier in the data frame. If the verification algorithm type is CRC16, a check bit pre-calculation caching mechanism will be added to the parsing logic to pre-store the CRC16 check values ​​of commonly used data segments in order to reduce the time consumed by real-time verification. For format conversion logic: If the data width is 16 bits or more, the numerical precision retention logic in the format conversion will be changed from "retain all decimal places" to "retain the corresponding decimal places according to the preset industrial scenario precision requirements". If there are duplicate field conversion errors in the historical interaction data, a field mapping anomaly check table will be added to the format conversion logic to perform secondary verification on error-prone field conversion logic. After each adjustment, the format conversion time of the current adaptation logic is re-collected and the K value is calculated. If the K value still does not meet the requirements after the number of consecutive adjustments reaches the preset maximum number of iterations, the generation module automatically calls the backup adaptation logic template (pre-stored based on the optimal adaptation scheme of the same protocol) and uses this template as the final data interaction adaptation logic. At the same time, the parameter changes and K value fluctuation curves during the adjustment process are recorded for subsequent iterative optimization of the adaptation logic template. The acquisition module is used to acquire the interaction adaptation logic generated in the generation module, and to collect real-time operating data from the industrial equipment that it has established a connection with using the interaction adaptation logic, while simultaneously filtering invalid data fragments during the acquisition process. When the acquisition module filters invalid data segments, it follows the following rules: Determine whether the collected data meets the preset verification logic. The preset verification logic includes at least CRC check and parity check. If the verification fails, the data is determined to be invalid. Determine whether the collected data values ​​are within the preset range of the corresponding operating parameters of the industrial equipment; if they are outside the range, they are considered invalid data. The data acquisition module dynamically adjusts the data acquisition frequency based on the real-time operating status of the industrial equipment. ; In the formula: The adjusted sampling frequency; The preset base sampling frequency; The data generated by the acquisition module in real time is measured in data entries per second. Set a preset data volume threshold; , The preset adjustment coefficient; Real-time load of the equipment; The preset load threshold; The above formula uses the preset basic acquisition frequency as a benchmark, and calculates and adjusts the target frequency in real time by combining the real-time data generation of the equipment, the real-time load, and two preset adjustment coefficients. It dynamically adapts to the equipment operating status, avoids insufficient acquisition when the data generation is high and excessive acquisition when the load is high, balances the timeliness of data and the operating pressure of the equipment, and reduces invalid data acquisition. The data acquisition module recalculates f at preset time intervals and performs data acquisition operations based on the updated f. in, ∈ (0,1), and when the timeliness requirements for real-time equipment operation data are higher in industrial scenarios... The larger the value, the lower the requirement for data timeliness in the scenario. The smaller the value, ∈ (0,1), when the load-bearing capacity of the industrial equipment itself is weaker The larger the value, the stronger the equipment's load-bearing capacity. The smaller the value; The normalization module receives the real-time operational data collected by the acquisition module and performs structural standardization processing on the data from different protocol sources according to the preset unified data model to obtain a unified format dataset. The pre-defined unified data model in the normalization module includes: a data identifier layer, a data attribute layer, and a data value layer; The data identification layer is used to store unique identification information, including device ID, data acquisition timestamp, and parameter encoding; The data attribute layer is used to store data element information, including data type, data unit, and precision level; The data value layer is used to store the raw values ​​of the collected device operation data; When the normalization module performs structural standardization processing, it extracts three layers of information from data from different protocol sources, and then completes information mapping and filling according to the hierarchical structure of the unified data model. If a protocol data is missing data attribute layer information, it will be automatically completed based on the system's pre-stored device parameter attribute library. After completion, the original values ​​of the data value layer will be uniformly converted to obtain a unified format dataset with consistent structure. The storage module receives datasets in a unified format, dynamically allocates storage resources based on the frequency and importance of the data, and synchronously creates associated indexes. When the storage module dynamically allocates storage resources, it simultaneously calculates the total storage resource requirement of the data to be stored: ; In the formula: The total storage resource requirement for the data to be stored; This is the preset basic storage resource unit; The frequency of this type of data generation; Weights based on data importance; Set the preset resource redundancy coefficient; The above formula is obtained by multiplying the preset basic storage resource unit by the data generation frequency and data importance weight, and then taking into account the preset resource redundancy coefficient. When resources are insufficient, low-importance data is released according to weight, and a secondary index is built with device ID and collection timestamp. This not only allocates resources according to data value, but also copes with fluctuations through redundancy, helping the system to efficiently locate data. Storage module according to Allocate storage space of the corresponding capacity from the available storage resources of the device system: If there are insufficient free storage resources, release some storage resources for non-critical data in ascending order of data importance weight, until the requirement is met. The demand; When the storage module creates an associated index, it uses the device ID and data acquisition timestamp in the data identification layer of the unified data model as the core index fields to build a secondary index structure. This index structure can be used to locate all the running data of any device within a specified time period. in, The initial value range is set to [1, 5]. The higher the impact of the data on industrial production monitoring and fault diagnosis, the more likely it is to be used. The larger the value, the lower the impact of the data on production monitoring and fault diagnosis. The smaller the value, The initial value range is set to (0, 0.5). As the fluctuation range of industrial equipment operating data increases... The larger the value, the smaller the fluctuation range of the device's operating data. The smaller the value; The adaptation module is used to obtain the calling target of the external application system, retrieve the corresponding data from the storage module, dynamically adapt to the interface protocol of the external application, and output it. When the adapter module obtains the calling target of the external application system, it follows the following rules: If an external application system initiates a data call request through an API interface, the adaptation module parses the target device ID, target data parameters, and data time range fields in the API request header to determine the call target; If an external application system uses a preset push mode to obtain data, the adaptation module receives a push configuration request sent in advance by the external application system, and extracts the fields of target device list, attention parameter list, and data push frequency from the request to determine the calling target; During the stage of dynamic adaptation of external application interface protocols, the adaptation module identifies the interface protocol type of the external application, calls the system's built-in protocol conversion component, converts the unified format dataset retrieved from the storage module into a data format that meets the requirements of the external application interface protocol, and then performs data transmission and output after the conversion is completed. The data interaction adaptation logic includes data parsing logic, format conversion logic, and transmission control logic. During the operation of the modules in the system, the data flow status between each module is monitored synchronously, and the module collaboration efficiency is calculated. When the module collaboration efficiency is less than the preset efficiency threshold, the collaboration optimization module sends a resource scheduling instruction to the module with the longest time consumption, increases the computing resources of that module, including CPU utilization and memory allocation, and optimizes the transmission link bandwidth between that module and other modules until the module collaboration efficiency is not less than the preset efficiency threshold. Module collaboration efficiency is calculated using the following formula: ; In the formula: Let be the processing time of the i-th module, where i = 1, 2, ..., N; Let be the delay of the d-th transmission link, where d = 1, 2, ..., M; The number of modules involved in the data flow; The maximum allowed processing time for a single module is preset; This refers to the number of data transmission links between modules; The maximum allowable transmission delay for a single link is preset; in, This represents the sum of the processing times for a single operation across all modules involved in the data flow. This represents the sum of the single transmission delays of all data transmission links between modules; The above formula calculates the total processing time of all modules involved in the data flow and the total delay of the transmission links between modules, and then compares it with the preset maximum allowable processing time of a single module and the preset maximum allowable transmission delay of a single link to comprehensively evaluate the coordination. When the efficiency is lower than the threshold, the module with the longest processing time can be accurately located. By increasing its computing resources and optimizing the transmission bandwidth, the system can avoid local bottlenecks from affecting the overall data flow and further improve the overall coordination capability of the system. The identification module interacts with the generation module via a wireless network. The generation module interacts with the acquisition module, normalization module, and storage module via a wireless network. The storage module interacts with the adaptation module via a wireless network.

[0022] In this embodiment, the identification module receives protocol signals sent by industrial equipment, parses the protocol type identifier carried in the signals, and synchronously creates a data connection channel between the equipment and the system. The generation module, running subsequently, automatically generates corresponding data interaction adaptation logic based on the signal parsing results in the identification module. The acquisition module then acquires the interaction adaptation logic generated in the generation module and uses it to collect real-time operating data from the industrial equipment it has established a connection with, while simultaneously filtering invalid data fragments during the acquisition process. The normalization module further receives the real-time operating data collected by the acquisition module and performs structural standardization processing on the data from different protocol sources according to a preset unified data model to obtain a unified format dataset. The storage module synchronously receives the unified format dataset, dynamically allocates storage resources based on the data generation frequency and criticality, and synchronously creates associated indexes. Finally, the adaptation module obtains the calling target of the external application system, retrieves the corresponding data from the storage module, dynamically adapts the interface protocol of the external application, and outputs it.

[0023] The system in the above embodiments, when applied to industrial production scenarios, can be compatible with industrial equipment with different protocols, collect and filter effective operating data in real time, standardize data from different sources, dynamically allocate storage resources according to data characteristics and facilitate indexing, adapt to external application calling requirements, optimize overall operating efficiency, effectively improve data integration efficiency, reduce processing costs, and provide data support for production monitoring and fault diagnosis.

[0024] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 A more detailed description is provided of the multi-protocol dynamic adaptation industrial equipment data integration system in Example 1: A method for integrating industrial equipment data with dynamic multi-protocol adaptation, comprising: It receives protocol signals sent by industrial equipment, parses the protocol type identifier in the signal and creates a data connection channel, and sends heartbeat detection signals at a preset period. If no response is received after a preset number of consecutive times, the parsing and channel creation operations are re-executed. Collect the inherent protocol parameters and historical interaction data of industrial equipment, calculate the adaptation logic optimization coefficient, and adjust the data parsing and format conversion logic based on the comparison results of the coefficient and the preset threshold to determine the final data interaction adaptation logic; The application's interactive adaptation logic collects real-time operating data from industrial equipment, filters invalid data according to the verification logic and preset parameter range, and dynamically adjusts the data collection frequency based on the amount of data generated by the equipment and the load. The collected data is extracted using a unified data model, missing attribute information is filled in and data units are standardized, and structural standardization is completed to obtain a unified format dataset. Calculate the storage resource requirements of a unified format dataset, dynamically allocate storage resources based on data generation frequency and importance, and simultaneously build a secondary association index based on device ID and collection timestamp; Parse the call target of the external application system to retrieve the corresponding data.

[0025] In summary, the system and method described in the above embodiments can automatically identify industrial equipment protocol signals and establish data connections. By combining equipment protocol parameters with historical interaction data to generate optimized interaction adaptation logic, the reliability and real-time performance of data interaction are improved. During data collection, invalid segments are filtered and the collection frequency is dynamically adjusted to balance data quality and equipment load. Different protocol data are processed in a standardized manner according to a unified model to achieve format uniformity. Storage resources are dynamically allocated and associated indexes are established based on the data generation frequency and criticality to improve storage utilization and data retrieval efficiency. Furthermore, the system can adapt to external application interface protocol output data to meet diverse calling needs. At the same time, it monitors the data flow status and collaboration efficiency, and allocates resources in a timely manner to ensure the stable and efficient operation of the system. This effectively improves the flexibility, accuracy, and overall efficiency of industrial equipment data integration, and reduces data integration costs to a certain extent.

[0026] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will 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.

Claims

1. A multi-protocol dynamic adaptation industrial equipment data integration system, characterized in that, include: The identification module is used to receive protocol signals sent by industrial equipment, parse the protocol type identifier carried in the signal, and synchronously create a data connection channel between the equipment and the system. The generation module is used to automatically generate the corresponding data interaction adaptation logic based on the signal analysis results in the recognition module. The acquisition module is used to acquire the interaction adaptation logic generated in the generation module, and to collect real-time operating data from the industrial equipment that it has established a connection with using the interaction adaptation logic, while simultaneously filtering invalid data fragments during the acquisition process. The normalization module receives the real-time operational data collected by the acquisition module and performs structural standardization processing on the data from different protocol sources according to the preset unified data model to obtain a unified format dataset. The storage module receives datasets in a unified format, dynamically allocates storage resources based on the frequency and importance of the data, and synchronously creates associated indexes. The adaptation module is used to obtain the calling target of the external application system, retrieve the corresponding data from the storage module, dynamically adapt to the interface protocol of the external application, and output it. The data interaction adaptation logic includes data parsing logic, format conversion logic, and transmission control logic.

2. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, When the identification module parses the protocol type identifier, it extracts the feature field at a preset position in the protocol signal and determines the protocol type by the matching degree between the feature field and the system's pre-stored protocol feature library. After the data connection channel is created, the identification module sends a heartbeat detection signal at a preset period. If no heartbeat response is received from the industrial equipment for a preset number of consecutive times, the protocol signal parsing and data connection channel creation process is re-executed. Among them, the feature fields at preset positions in the protocol signal include the protocol version number, the data frame start identifier, and the device manufacturer code.

3. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, Before generating the data interaction adaptation logic, the generation module first collects the inherent protocol parameters and historical interaction data of the industrial equipment, and calculates the adaptation logic optimization coefficient based on the collected parameters and data. The generation module determines the final data interaction adaptation logic according to the priority of the values ​​of the adaptation logic optimization coefficient. The protocol's inherent parameters include data frame length, verification algorithm type, and data bit width; the historical interaction data includes historical data frame parsing success rate and historical format conversion error count. ； In the formula: To adapt the logic optimization coefficients; For historical data frame parsing success rate; , Preset weighting coefficients; The time consumed by the format conversion of the current adaptation logic draft; Set a preset time threshold for format conversion; The generation module is set with a preset optimization coefficient comparison threshold. When K is not less than the preset optimization coefficient comparison threshold, the generation module directly determines the adaptation logic draft as the final data interaction adaptation logic. If K is less than the preset optimization coefficient comparison threshold, the data parsing logic and format conversion logic are adjusted based on the inherent parameters of the protocol, and K is recalculated until K is not less than the preset optimization coefficient comparison threshold. Among them, the current adaptation logic draft is the adaptation logic version that the generation module initially constructs based on the initially collected protocol parameters and historical interaction data before determining the final data interaction adaptation logic; , All are positive numbers, and their sum is 1.

4. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, When the acquisition module filters invalid data segments, it follows the following rules: Determine whether the collected data meets the preset verification logic. The preset verification logic includes at least CRC check and parity check. If the verification fails, the data is determined to be invalid. Determine whether the collected data values ​​are within the preset range of the corresponding operating parameters of the industrial equipment; if they are outside the range, they are considered invalid data. The data acquisition module dynamically adjusts the data acquisition frequency based on the real-time operating status of the industrial equipment. ； In the formula: The adjusted sampling frequency; The preset base sampling frequency; The data generated by the acquisition module in real time is measured in data entries per second. Set a preset data volume threshold; , The preset adjustment coefficient; Real-time load of the equipment; The preset load threshold; The data acquisition module recalculates f at preset time intervals and performs data acquisition operations based on the updated f.

5. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, The preset unified data model in the normalization module includes: a data identifier layer, a data attribute layer, and a data value layer; The data identification layer is used to store unique identification information, including device ID, data acquisition timestamp, and parameter encoding; The data attribute layer is used to store data element information, including data type, data unit, and precision level; The data value layer is used to store the raw values ​​of the collected device operation data; When the normalization module performs structural standardization processing, it extracts three layers of information from data from different protocol sources, and then completes information mapping and filling according to the hierarchical structure of the unified data model. If a protocol data is missing data attribute layer information, it will be automatically completed based on the system's pre-stored device parameter attribute library. After completion, the original values ​​of the data value layer will be uniformly converted to obtain a unified format dataset with consistent structure.

6. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, When the storage module dynamically allocates storage resources, it simultaneously calculates the total storage resource requirement of the data to be stored: ； In the formula: The total storage resource requirement for the data to be stored; This is the preset basic storage resource unit; The frequency of this type of data generation; Weights based on data importance; Set the preset resource redundancy coefficient; The storage module according to Allocate storage space of the corresponding capacity from the available storage resources of the device system: If there are insufficient free storage resources, release some storage resources for non-critical data in ascending order of data importance weight, until the requirement is met. The demand; When the storage module creates the associated index, it uses the device ID and data acquisition timestamp in the data identification layer of the unified data model as the core index fields to build a secondary index structure. Through this index structure, it can locate all the running data of any device within a specified time period.

7. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, When the adaptation module obtains the calling target of the external application system, it follows the following: If an external application system initiates a data call request through an API interface, the adaptation module parses the target device ID, target data parameters, and data time range fields in the API request header to determine the call target; If an external application system uses a preset push mode to obtain data, the adaptation module receives a push configuration request sent in advance by the external application system, and extracts the fields of target device list, attention parameter list, and data push frequency from the request to determine the calling target; The adaptation module dynamically adapts to the external application interface protocol stage, identifies the interface protocol type of the external application, calls the system's built-in protocol conversion component, converts the unified format dataset retrieved from the storage module into a data format that meets the requirements of the external application interface protocol, and then executes the data transmission and output after the conversion is completed.

8. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, During the operation of the modules in the system, the data flow status between each module is monitored synchronously, and the module collaboration efficiency is calculated. When the module collaboration efficiency is less than the preset efficiency threshold, the collaboration optimization module sends a resource scheduling instruction to the module with the longest time consumption, increases the computing resources of that module, including CPU utilization and memory allocation, and optimizes the transmission link bandwidth between that module and other modules until the module collaboration efficiency is not less than the preset efficiency threshold. The module collaboration efficiency is calculated using the following formula: ； In the formula: Let be the processing time of the i-th module, where i = 1, 2, ..., N; Let be the delay of the d-th transmission link, where d = 1, 2, ..., M; The number of modules involved in the data flow; The maximum allowed processing time for a single module is preset; This refers to the number of data transmission links between modules; This is the preset maximum allowable transmission delay for a single link.

9. The industrial equipment data integration system with multi-protocol dynamic adaptation according to claim 1, characterized in that, The identification module is interconnected with the generation module via a wireless network. The generation module is interconnected with the acquisition module, the normalization module, and the storage module via a wireless network. The storage module is interconnected with the adaptation module via a wireless network.

10. A method for multi-protocol dynamic adaptation of industrial equipment data integration, wherein the method is an implementation method of the multi-protocol dynamic adaptation of industrial equipment data integration system as described in any one of claims 1-9, characterized in that, include: It receives protocol signals sent by industrial equipment, parses the protocol type identifier in the signal and creates a data connection channel, and sends heartbeat detection signals at a preset period. If no response is received after a preset number of consecutive times, the parsing and channel creation operations are re-executed. Collect the inherent protocol parameters and historical interaction data of industrial equipment, calculate the adaptation logic optimization coefficient, and adjust the data parsing and format conversion logic based on the comparison results of the coefficient and the preset threshold to determine the final data interaction adaptation logic; The application's interactive adaptation logic collects real-time operating data from industrial equipment, filters invalid data according to the verification logic and preset parameter range, and dynamically adjusts the data collection frequency based on the amount of data generated by the equipment and the load. The collected data is extracted using a unified data model, missing attribute information is filled in and data units are standardized, and structural standardization is completed to obtain a unified format dataset. Calculate the storage resource requirements of a unified format dataset, dynamically allocate storage resources based on data generation frequency and importance, and simultaneously build a secondary association index based on device ID and collection timestamp; Parse the call target of the external application system to retrieve the corresponding data.

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