Method and system for automatically identifying data type of acquisition point location

Abstract

The invention relates to the technical field of industrial data acquisition and data processing, and particularly provides a method and a system for automatically identifying data types of acquisition point locations. Setting a proportion of the number of judgment collection pieces and a preset data type, collecting point location data of a non-preset data type, and judging whether the number of the collected point location data reaches the number of judgment collection pieces or not; and judging whether the proportion of the number of the data types to the number of the collected point data reaches the proportion of the preset data type or not. Therefore, the system can quickly judge the data type after collecting a sufficient number of data samples, so that the data collection period is shortened. The data processing speed is accelerated, the overall working efficiency is improved, and the accuracy and efficiency of data acquisition are improved. And by presetting a meeting condition for judging the type of the point location data and a condition for triggering an artificial mechanism, the judgment of the type of the point location data with automatic as a main mode and manual as an auxiliary mode is realized, the accuracy of the type of the point location data is improved, and the loss of acquired data is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of industrial data collection and data processing, and in particular to a method and system for automatically identifying the data type of a collection point. Background Art

[0002] The management system for industrial data collection must have the management functions of equipment points and gateway machines. Equipment is a piece of hardware in industrial production, and a point is an operating monitoring parameter in the hardware. For example, a piece of equipment has operating parameters such as speed, temperature, whether it is turned on, voltage, etc. The operating parameter is called a point. One device corresponds to multiple points, and the points carry the collection frequency field information. The gateway machine is an Internet of Things gateway, which can realize protocol conversion between perception networks, communication networks and different types of perception networks, realize wide-area and local-area connections, and realize the collection of operating parameter data of equipment points to the gateway machine for temporary storage and push out or for collection by the collection end.

[0003] Most of the databases selected for industrial data collection are relational or time-series databases. These types of databases have the characteristics of strong data types, which means that the data to be stored must be set with the data type of the data storage in advance. If the data type of the data storage is set incorrectly in advance, the collected data will fail to be stored.

[0004] The existing collection mode is that the collection personnel must first configure the point information on the system, such as the name of the point, the data type of the point, and the collection protocol of the point, before they can collect data from the point. In the step of the point data type, the collection personnel often configure the wrong data type, resulting in the inability to write data into the database after collection and data loss. Summary of the invention

[0005] In order to solve the problem that the existing manual collection may configure the wrong data type, resulting in the inability to write data into the database and data loss after data collection, the present application provides a method for automatically identifying the data type of the collection point, including the steps of: Create points and configure point acquisition protocols; If the data type is not preset for the point, the number of collected items and the ratio of the preset data type are set; Collect point data; Determine whether the number of the collected point data has reached the determined number of collected data; If not, continue to collect the point data; If so, determine whether the proportion of the number of items of a certain data type to the number of items of the collected point data reaches the preset data type proportion; If not, continue to collect the point data; If so, it is determined that the point data type is a data type that reaches the preset data type ratio.

[0006] In a feasible implementation, the step of setting and determining the proportion of the number of collected items and the preset data type further includes: setting a maximum collection amount that triggers manual intervention; The step of determining whether the number of items of a certain data type accounts for a proportion of the number of items of the collected point data that reaches the preset data type proportion also includes: When the proportion of the number of items of non-existent data types to the number of collected point data items reaches the preset data type proportion; when determining whether the number of collected point data items is less than the maximum collection amount that triggers manual intervention, if not, an alarm is triggered.

[0007] In a feasible implementation, before determining whether the number of the collected point data has reached the determined number of collected data, the step further includes: A scheduled task is set, and the scheduled task scans and collects the number of point data at a specified time to determine the data type.

[0008] In a feasible implementation, the preset data types account for more than fifty percent.

[0009] In a feasible implementation, if the point does not have a preset data type, setting the number of collection items and the proportion of the preset data type also includes the steps of: A first table whose data type is a string is automatically generated, and the point data whose data type is not configured are all stored in the first table.

[0010] In a feasible implementation, after determining that the point data type is a data type that reaches the preset data type ratio, the step of: Generate a second table of data types whose data types reach the proportion of the data types, store the collected point data in the second table, and delete the first table.

[0011] In a feasible implementation, the step of setting the proportion of the number of collected items and the preset data type further includes: setting a minimum proportion of the data type; Before the step of determining whether the number of items of a certain data type accounts for the proportion of the number of items of the collected point data that reaches the preset data type proportion, the following step further includes: The data types whose proportion is lower than the minimum proportion of the data types in the collected point data are screened out, and the number of the screened out data types is subtracted from the number of the collected point data.

[0012] In a feasible implementation, the steps are also included: Set the number of verifications, and collect and verify the point data for which the data type judgment has been completed again; Recalculating whether the proportion of the data type after increasing the number of verification items to the number of point data items collected can still reach the preset data type proportion; If so, verify that the data type is correct; If not, the point data type is re-determined for the point.

[0013] In a feasible implementation, the step of determining whether the number of the collected point data has reached the determined number of collected data further includes the following steps: The collected point data is cleaned to remove erroneous or invalid data.

[0014] On the other hand, the present application provides a system for automatically identifying the data type of a collection point, which is used to implement any of the above methods for automatically identifying the data type of a collection point, including: a collection module, a creation module and a judgment module; The creation module is configured to: create points and configure point acquisition protocols; The acquisition module is configured to: collect the point data from the creation module, and send the number of the collected point data and the proportion of each data type to the judgment module; The judgment module is configured to: set the judgment collection number and the ratio of preset data types, receive the ratio of the number of the collected point data and the number of each data type to the number of the collected point data; The judgment module is further configured to: judge whether the number of the collected point data reaches the determined collection number; If not, continue to collect the point data; If so, determine whether the proportion of the number of items of a certain data type to the number of items of the collected point data reaches the preset data type proportion; If not, continue to collect the point data; If so, it is determined that the point data type is a data type that reaches the preset data type ratio.

[0015] As can be seen from the above content, the present application provides a method and system for automatically identifying the data type of a collected point. Set the number of collection items and the ratio of preset data types, collect point data without preset data types, and judge whether the number of point data items collected reaches the determined number of collection items; and whether the number of items of a certain data type accounts for the ratio of the number of point data items collected to the preset data type ratio. After collecting a sufficient number of data samples, the system can quickly make a data type judgment, thereby shortening the data collection cycle. This helps to speed up data processing and improve overall work efficiency. At the same time, this method avoids possible errors in manually configuring data types and improves the accuracy and efficiency of data collection. Then, by presetting the conditions for satisfying the judgment of the point data type and the conditions for triggering the manual mechanism, the judgment of the point data type with automatic as the main and manual as the auxiliary is realized, the accuracy of the point data type is improved, and the loss of collected data is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the implementation of the present invention, and together with the specification are used to explain the principles of the embodiments of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the implementation of the present invention, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0017] Figure 1 A schematic flow chart of a method for automatically identifying the data type of a collection point according to an exemplary embodiment of the present application; Figure 2 A schematic flow chart of a method for automatically identifying the data type of a collection point according to another exemplary embodiment of the present application; Figure 3 A schematic flow chart of a method for automatically identifying the data type of a collection point according to another exemplary embodiment of the present application; Figure 4 A schematic flow chart of a method for automatically identifying the data type of a collection point according to another exemplary embodiment of the present application; Figure 5 A schematic flow chart of a method for automatically identifying the data type of a collection point according to yet another exemplary embodiment of the present application; Figure 6 This is a schematic diagram of the structure of a system for automatically identifying the type of data at a collection point, shown as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the embodiments of the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, mechanisms, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the implementation of the embodiments of the present invention.

[0019] In the industrial data collection and management system, equipment represents the hardware in industrial production, and points are the operating monitoring parameters of these hardware, such as speed, temperature, etc. Each device corresponds to multiple points, and the points contain collection frequency information. Industrial data collection often uses relational or time-series databases, which require that the data stored must be set in advance. However, in the existing collection mode, the collector must first configure the point information, including the point name, data type, and collection protocol, before data collection can be performed. However, when configuring the data type, the collector is prone to errors, resulting in the inability to write data into the database or loss of data.

[0020] To solve the above problems, the first aspect of the present application embodiment provides a method for automatically identifying the data type of a collection point, referring to Figure 1 As shown, the steps include: S100: Create points and configure point acquisition protocols.

[0021] The acquisition protocol defines how to read data from the device, including communication methods, data formats, etc.

[0022] S200: If the data type is not preset for the point, the number of collected items and the ratio of the preset data type are set and determined.

[0023] If there is no preset data type (i.e. the specific format or type of data, such as integer, floating point, string, etc.) when the point is created, you need to set a judgment condition. This condition includes two parameters: the number of judgment collections and the proportion of preset data types.

[0024] S300: Collect point data.

[0025] According to the configured acquisition protocol, the point data is read from the device. This step obtains the actual data sample for subsequent data type judgment.

[0026] S400: Determine whether the number of collected point data has reached the determined collection number; if not, continue to collect point data.

[0027] Check whether the number of data collected has reached the previously set number of data collection. When the collected data is insufficient, continue to collect to ensure that there are enough data samples to judge the data type to avoid misjudgment due to insufficient data.

[0028] Among them, the number of collections for determination ensures that a sufficient number of data samples have been collected before the data type is determined. Sufficient data samples can more accurately reflect the true distribution and characteristics of the data, thereby improving the accuracy of data type determination. By setting a reasonable number of collections for determination, it is possible to avoid waste of resources and time delays caused by excessive data collection while ensuring the accuracy of data type judgment. The setting of the number of collections for determination needs to be weighed according to the actual situation to ensure the best balance between efficiency and accuracy.

[0029] S500: If yes, determine whether the proportion of the number of items of a certain data type to the number of items of collected point data reaches a preset data type proportion; if not, continue to collect point data.

[0030] If the number of collected data items reaches the determined number of collected data items, it is checked whether the proportion of the number of items of a certain data type reaches the preset proportion of the data type.

[0031] S600: If yes, determine that the point data type is a data type that reaches a preset data type ratio.

[0032] If a data type that meets the conditions is found, the data type is used as the final data type of the point. Specifically, the preset data type ratio is used as the basis for data type judgment, which helps to automatically identify the dominant data type in the system. When the number of entries of a certain data type reaches the preset data type ratio, the system can automatically use the data type as the final data type of the point. By presetting the data type ratio, the impact of human factors on data type judgment can be reduced and the objectivity of judgment can be improved. The setting of the preset data type ratio can be adjusted according to the needs of the actual application scenario to adapt to different data type judgment needs.

[0033] In the method of the embodiment of the present application, by introducing the determination of the number of collected items and the proportion of preset data types, the system can quickly make a data type judgment after collecting a sufficient number of data samples, thereby shortening the data collection cycle. This helps to speed up data processing and improve overall work efficiency. By automatically determining the data type of the point, a basis is provided for subsequent data processing. At the same time, this method avoids possible errors in manually configuring data types and improves the accuracy and efficiency of data collection.

[0034] In some embodiments of the present application, reference Figure 2As shown, step S200 of setting and determining the number of collected items and the proportion of the preset data type also includes: S201: Setting the maximum collection amount that triggers manual intervention.

[0035] This step sets the maximum amount of data collected that triggers manual intervention, which can prevent excessive data collection. That is, the maximum amount of data collected that triggers manual intervention is set to prevent the system from falling into an infinite loop or excessive data collection during the data collection process. When the amount of collected data reaches this set value, if the data type is still not determined, the system will take further measures, such as triggering an alarm or prompting manual intervention.

[0036] Excessive data collection may consume a lot of system resources, including storage space and computing power. By setting a maximum collection volume, you can ensure that system resources are allocated reasonably and avoid resource waste. When the maximum collection volume is reached and the data type is still undetermined, human intervention may be required to make a judgment. This provides a clear trigger point so that relevant personnel know when they need to be involved in the decision-making process.

[0037] The step S500 of determining whether the number of items of a certain data type accounts for a proportion of the number of items of collected point data that reaches a preset data type proportion also includes: S501: When the number of items of non-existent data types accounts for the proportion of the number of collected point data items that reaches the preset data type proportion; it is determined whether the number of collected point data items is less than the maximum collection amount that triggers manual intervention. If not, an alarm is triggered.

[0038] When the amount of collected data exceeds the maximum amount, if the dominant data type is still not found, this may mean that there is a data anomaly or configuration error. By judging the proportion of data types, these problems can be discovered in time and corresponding measures can be taken. If data continues to be collected without any processing, the risk of data loss or corruption may increase. By triggering an alarm, relevant personnel can be reminded to take timely measures to protect the integrity and security of the data.

[0039] Through this step, the data collection process can be optimized to ensure that data types are determined more efficiently in future collection processes. This helps improve the accuracy and efficiency of data collection and reduce the cost and time of manual intervention.

[0040] In this embodiment, the two additional steps in S200 and S500 play an important role in preventing over-collection, protecting system resources, timely discovering problems, avoiding data loss or damage, and optimizing the data collection process. The introduction of these steps makes the method of automatically identifying the data type of the collection point more perfect and improves the reliability and stability of the system.

[0041] In some embodiments of the present application, before determining whether the number of point data collected reaches the determination collection number, the step is also included: setting a scheduled task, the scheduled task scans and collects the number of point data at a specified time to determine the data type.

[0042] Specifically, the scheduled task can automatically execute the data collection task at a preset time interval or a specific time point without manual triggering. This improves the automation of data collection and reduces the frequency of manual operations and the risk of errors. By setting a scheduled task, you can ensure the real-time nature of data collection, that is, obtain the latest point data in a timely manner.

[0043] It is understandable that scheduled tasks can be set to execute periodically, such as daily, weekly or monthly. This makes data collection regular and predictable, which is convenient for subsequent data analysis and processing. Scheduled tasks can automatically execute data collection tasks, avoiding the tediousness and inefficiency of manual collection. At the same time, through reasonable timing settings, system resources can be fully utilized to improve the concurrency and throughput of data collection. Scheduled tasks can ensure the continuity of data collection and reduce the risk of data loss due to missed or forgotten collection. It helps to ensure the integrity and accuracy of data.

[0044] Setting a scheduled task to periodically scan and collect point data items for data type judgment plays a role in automated data collection, improving real-time performance, and periodic data collection in the embodiments of the present application, and brings beneficial effects such as improving data collection efficiency, reducing the risk of data loss, optimizing system resource allocation, facilitating data analysis and processing, and enhancing system scalability.

[0045] In some embodiments of the present application, the proportion of preset data types is greater than 50%. By setting the proportion of data types to be greater than 50%, it can be ensured that only one data type can meet the requirements, and this type of data naturally becomes dominant, making the subsequent processing and analysis of the data clearer, avoiding the judgment of multiple data types, and improving the accuracy and efficiency of the judgment.

[0046] In some embodiments of the present application, reference Figure 3 As shown, if the data type is not preset for the point, setting the number of collection items and the ratio of the preset data type also includes the steps: S110: Automatically generate a first table whose data type is a string, and store all point data whose data type is not configured in the first table.

[0047] Step S110 is responsible for automatically generating a first table with a data type of string in the system, which is used to temporarily store point data that are not configured with data types. The first table provides a buffer area for the system, so that the point data can be safely stored before the data type configuration is completed, avoiding data loss. As a temporary storage area, the first table allows the system to flexibly process the point data in subsequent stages, such as configuring the data type or performing other operations according to actual needs.

[0048] After determining that the point data type is a data type that reaches a preset data type ratio, the following steps are also included: S610: Generate a second table of data types whose data types reach the proportion of the data types, store the collected point data in the second table, and delete the first table.

[0049] Step S610 is executed after determining that the point data type reaches the preset data type ratio, and is responsible for generating a second table whose data type reaches the required ratio, and storing the collected point data in the table. At the same time, this step will also delete the first table previously used to temporarily store point data of unconfigured data types. In the second table, all point data are stored according to the same data type, which enables the system to process these data more efficiently and accurately. By generating the second table and deleting the first table, the system realizes the reorganization and optimization of the point data. This makes the storage of data in the system more orderly and efficient, which is convenient for subsequent data analysis and processing.

[0050] In this embodiment, by creating the first table and the second table, data security and processing flexibility are improved, data organization and storage methods are optimized, and resource utilization and overall system performance are improved.

[0051] In some embodiments of the present application, reference Figure 4 As shown, step S200 of setting and determining the number of collected items and the proportion of the preset data type also includes: S210: Setting the minimum proportion of data types.

[0052] Before determining whether the number of items of a certain data type accounts for a proportion of the number of items of collected point data that reaches a preset data type proportion in step S500, the method further includes: S410: Filter out data types that are lower than the minimum data type ratio in the collected point data, and subtract the number of the filtered data types from the number of the collected point data.

[0053] By setting the minimum percentage of data types, you can exclude data types that are too low and may not be representative or important, which helps improve the quality of the overall data and makes subsequent analysis and processing more accurate and effective. In addition, data types that are lower than the minimum percentage of data types may often be noise data or abnormal data. By screening and excluding these data types, you can reduce the interference of noise data on the overall data analysis results.

[0054] The embodiment of the present application further optimizes data quality and reduces noise data by setting a minimum proportion of data types. The system can analyze and judge data types based on more accurate data, which helps to improve the accuracy and reliability of judgments.

[0055] In some embodiments of the present application, reference Figure 5 As shown, the steps include: S700: Set the number of verification items, and collect and verify the point data for which the data type judgment has been completed again; S800: recalculating whether the proportion of the data type after the number of verification items is increased to the number of collected point data items can still reach the preset data type proportion; If so, verify that the data type is correct; If not, re-determine the point data type.

[0056] Step S700 provides additional data support for data type judgment by setting the number of verification items and re-collecting. Step S800 ensures the robustness of the system in data type judgment by recalculating the data type ratio and verifying the data type judgment based on the result.

[0057] The embodiment of the present application collects additional data and recalculates the proportion of data types. In the newly collected data, if the proportion of the dominant data type still meets the requirements, the type can be confirmed as the dominant data type again; if the proportion decreases and no longer meets the conditions of the preset data type proportion, re-evaluation is required. It helps to reduce misjudgments caused by initial collection anomalies, thereby improving the accuracy of data type judgment. By adding verification steps, the system can more accurately judge the type of point data, thereby improving the reliability of the system. It can reduce system errors and failure rates caused by misjudgment of data types and improve the stability and availability of the system.

[0058] In some embodiments of the present application, before determining whether the number of points of data collected reaches the number of points of data collected, the method further includes the step of cleaning the collected points of data to remove erroneous or invalid data. The cleaned data set is purer and more accurate, which helps to optimize the data judgment results, improve the data quality, reduce the calculation burden, and avoid misleading conclusions.

[0059] Specifically, this application uses a specific case to illustrate the system of automatic identification of data types of collection points of this application. In a certain energy data collection project, there are 119,156 points. Before the automatic determination of point data types function is used, the number of mismatches of point data types reaches 489, and the mismatch rate reaches 0.41%. After setting this point to automatically determine the data type, 78 points automatically determine the data type incorrectly, and 23 trigger manual intervention to manually set the point data type. The overall automatic determination of data type error rate is 0.08%, which reduces the point data type error rate by about 80%. Effectively improve the accuracy of point data type configuration.

[0060] Another aspect of the present application embodiment provides a system for automatically identifying the data type of a collection point. Figure 6 As shown, the method for realizing any one of the above-mentioned methods for automatically identifying the data type of the collected point includes: a collection module, a creation module and a judgment module.

[0061] The creation module is configured to: create points and configure point acquisition protocols; responsible for creating points and configuring point acquisition protocols. Through the creation module, users can define the points to be collected and the corresponding acquisition rules, such as acquisition frequency, data type, etc. The creation module is used to create point information, which may include point name, location, attributes, etc.; configure the acquisition protocol to determine the data collection method, format, time interval, etc. for each point.

[0062] The acquisition module is configured to: collect point data from the creation module, and send the number of points data collected and the proportion of each data type to the judgment module. The acquisition module performs data collection tasks, obtains data from points according to the preset collection protocol; counts the number of points data collected, and the proportion of each data type in the total data; and sends the statistical results and the collected data to the judgment module.

[0063] The judgment module is configured to: set and judge the number of collected items and the ratio of preset data types, receive the number of collected point data items and the ratio of the number of items of each data type to the number of collected point data items.

[0064] The judgment module is further configured to: judge whether the number of collected point data has reached the determined number of collected data; If not, continue to collect point data; If so, determine whether the number of items of a certain data type accounts for a proportion of the number of items of collected point data that reaches a preset data type proportion; If not, continue to collect point data; If so, the point data type is determined to be a data type that reaches a preset data type ratio.

[0065] The system for automatically identifying the data type of the collection point provided in the embodiment of the present application greatly reduces manual intervention and improves work efficiency through automated collection and judgment processes. The system supports user-defined points and collection protocols, and can meet data collection requirements in different scenarios. By setting the number of collection items and the proportion of preset data types, the system can more accurately identify the type of point data and avoid misjudgment due to insufficient data volume or unclear data types.

[0066] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure of the specification and the examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and the examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

Claims

1. A method for automatically identifying the data type of a collection point, characterized in that: Includes steps: Create points and configure point acquisition protocols; If the data type is not preset for the point, the number of collected items and the ratio of the preset data type are set; Collect point data; Determine whether the number of the collected point data has reached the determined number of collected data; If not, continue to collect the point data; If so, determine whether the proportion of the number of items of a certain data type to the number of items of the collected point data reaches the preset data type proportion; If not, continue to collect the point data; If so, it is determined that the point data type is a data type that reaches the preset data type ratio.

2. A method for automatically identifying the data type of a collection point according to claim 1, characterized in that: The step of setting and determining the proportion of the number of collected items and the preset data type also includes: setting a maximum collection amount that triggers manual intervention; The step of determining whether the number of items of a certain data type accounts for a proportion of the number of items of the collected point data that reaches the preset data type proportion also includes: When the proportion of the number of items of non-existent data types to the number of collected point data items reaches the preset data type proportion; when determining whether the number of collected point data items is less than the maximum collection amount that triggers manual intervention, if not, an alarm is triggered.

3. The method for automatically identifying the data type of a collection point according to claim 1, characterized in that: Before judging whether the number of the collected point data has reached the determined number of collected data, the step further includes: A scheduled task is set, and the scheduled task scans and collects the number of point data at a specified time to determine the data type.

4. A method for automatically identifying the data type of a collection point according to claim 1, characterized in that: The preset data types account for more than fifty percent.

5. The method for automatically identifying the data type of a collection point according to claim 1, characterized in that: If the point does not have a preset data type, setting the number of collected items and the proportion of the preset data type also includes the steps of: A first table whose data type is a string is automatically generated, and the point data whose data type is not configured are all stored in the first table.

6. A method for automatically identifying the data type of a collection point according to claim 5, characterized in that: After determining that the point data type is a data type that reaches the preset data type ratio, the following steps are further included: Generate a second table of data types whose data types reach the proportion of the data types, store the collected point data in the second table, and delete the first table.

7. The method for automatically identifying the data type of a collection point according to claim 1, characterized in that: The step of setting and determining the number of collected items and the proportion of preset data types also includes: setting a minimum proportion of data types; Before the step of determining whether the number of items of a certain data type accounts for the proportion of the number of items of the collected point data that reaches the preset data type proportion, the following step further includes: The data types whose proportion is lower than the minimum proportion of the data types in the collected point data are screened out, and the number of the screened out data types is subtracted from the number of the collected point data.

8. The method for automatically identifying the data type of a collection point according to claim 1, characterized in that: Also includes the steps: Set the number of verifications, and collect and verify the point data for which the data type judgment has been completed again; Recalculating whether the proportion of the data type after increasing the number of verification items to the number of point data items collected can still reach the preset data type proportion; If so, verify that the data type is correct; If not, the point data type is re-determined for the point.

9. The method for automatically identifying the data type of a collection point according to claim 1, characterized in that: The step of judging whether the number of the collected point data has reached the number of collected data also includes the following steps: The collected point data is cleaned to remove erroneous or invalid data.

10. A system for automatically identifying the data type of a collection point, characterized in that: A method for automatically identifying the data type of a collection point as described in any one of claims 1 to 9, comprising: a collection module, a creation module and a judgment module; The creation module is configured to: create points and configure point acquisition protocols; The acquisition module is configured to: collect the point data from the creation module, and send the number of the collected point data and the proportion of each data type to the judgment module; The judgment module is configured to: set and determine the number of collected data and the ratio of preset data types, receive the number of collected point data and the ratio of the number of each data type to the number of collected point data; The judgment module is further configured to: judge whether the number of the collected point data reaches the determined collection number; If not, continue to collect the point data; If so, determine whether the proportion of the number of items of a certain data type to the number of items of the collected point data reaches the preset data type proportion; If not, continue to collect the point data; If so, it is determined that the point data type is a data type that reaches the preset data type ratio.

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