A method and system for automatically identifying the data type of a collection point

Abstract

This application relates to the technical field of industrial data acquisition and data processing, and specifically provides a method and system for automatically identifying the data types of acquisition points. Set the determined number of acquisitions and the preset proportion of data types, collect the point data of points without preset data types, and judge whether the number of the collected point data reaches the determined number of acquisitions; and whether the proportion of the number of a certain data type in the collected point data reaches the preset proportion of data types. This enables the system to quickly make a data type judgment after collecting a sufficient number of data samples, thereby shortening the data acquisition cycle. It speeds up the data processing speed, improves the overall work efficiency, and improves the accuracy and efficiency of data acquisition. Then, by presetting the conditions for judging the data types of point data and the conditions for triggering the manual mechanism, the judgment of the data types of points is realized with automatic as the main and manual as the auxiliary, improving the accuracy of the data types of points and reducing the loss of collected data.

Description

Technical Field

[0001] This application relates to the technical field of industrial data acquisition and data processing, and particularly relates to a method and system for automatically identifying the data types of acquisition points. Background Art

[0002] In the management system for industrial data acquisition, it is necessary to have the management functions of device points and network gateways. The device is a certain hardware in industrial production, and the point is an operation monitoring parameter in the hardware. For example, a certain device has operating parameters such as rotational speed, temperature, whether it is turned on, voltage, etc. The operating parameter is called a point. One device corresponds to multiple points, and the point is provided with acquisition frequency field information. The network gateway is an Internet of Things gateway, which can realize protocol conversion between the sensing network, the communication network, and different types of sensing networks, realize wide-area connection and local connection, and realize the acquisition of the operating parameter data of the device points into the network gateway for temporary storage and pushing out or for the acquisition end to collect.

[0003] Most of the database selections for industrial acquisition data are relational or time-series databases. These types of databases have the characteristics of strong data types, that is, the data types to be stored in the data must be set in advance. If the data types set in advance for data storage are incorrect, it will lead to the failure of collecting data into the database.

[0004] The existing acquisition mode is that the acquisition personnel first configure the point information on the system, such as configuring the name of the point, the data type of the point, and the acquisition protocol of the point, before the data of the point can be acquired. In this step of the data type of the point, the acquisition personnel often configure the wrong data type, resulting in the inability to write the data into the database and data loss after data acquisition. Summary of the Invention

[0005] To solve the above problem that the existing manual acquisition may configure the wrong data type, resulting in the inability to write the data into the database and data loss after data acquisition, on the one hand, this application provides a method for automatically identifying the data types of acquisition points, including the steps of:

[0006] Create a point and configure the point acquisition protocol;

[0007] If the data type of the point is not preset, set the determined number of acquisition records and the preset data type ratio;

[0008] Collect the point data;

[0009] Judge whether the number of the collected point data reaches the determined number of acquisition records;

[0010] If not, continue to collect the point data;

[0011] If so, determine whether the proportion of the number of a certain data type in the number of the collected point data reaches the preset data type proportion;

[0012] If not, continue to collect the point data;

[0013] If so, determine that the point data type is the data type that reaches the preset data type proportion.

[0014] In a feasible implementation manner, the step of setting the determined collection number and the proportion of the preset data type further includes: setting the maximum collection amount for triggering manual intervention;

[0015] The step of determining whether the proportion of the number of a certain data type in the number of the collected point data reaches the preset data type proportion further includes:

[0016] When the proportion of the number of a data type in the number of the collected point data does not reach the preset data type proportion; determine whether the number of the collected point data is less than the maximum collection amount for triggering manual intervention. If not, trigger an alarm.

[0017] In a feasible implementation manner, before determining whether the number of the collected point data reaches the determined collection number, the following steps are further included:

[0018] Set a timing task, and the timing task scans and collects the number of the point data at regular intervals according to the specified time for data type judgment.

[0019] In a feasible implementation manner, the preset data type proportion is greater than 50%.

[0020] In a feasible implementation manner, if no data type is preset for the point, the step of setting the determined collection number and the proportion of the preset data type further includes:

[0021] Automatically generate a first table with the data type of string, and store all the point data without configured data type in the first table.

[0022] In a feasible implementation manner, after determining that the point data type is the data type that reaches the preset data type proportion, the following steps are further included:

[0023] Generate a second table with the data type that reaches the proportion of the data type, store the collected point data in the second table, and delete the first table.

[0024] In a feasible implementation manner, the step of setting the determined collection number and the proportion of the preset data type further includes: setting the minimum data type proportion;

[0025] Before the step of determining whether the proportion of the number of a certain data type in the collected point data reaches the preset data type proportion, the following steps are further included:

[0026] Filter out the data types in the collected point data that are lower than the minimum proportion of the data type, and subtract the number of the filtered data types from the number of the collected point data.

[0027] In a feasible implementation manner, the following steps are further included:

[0028] Set the verification number, and perform collection verification on the point data for which the data type has been determined again;

[0029] Recalculate whether the proportion of the data type after adding the verification number in the collected point data can still reach the preset data type proportion;

[0030] If so, verify that the data type judgment is correct;

[0031] If not, re-judge the point data type of the point.

[0032] In a feasible implementation manner, before determining whether the number of the collected point data reaches the determined collection number, the following steps are further included:

[0033] Clean the collected point data to remove incorrect or invalid data.

[0034] On the other hand, the present application provides a system for automatically identifying the data type of collected point data, which is used to implement the method for automatically identifying the data type of collected point data described in any one of the above, and includes: a collection module, a creation module, and a judgment module;

[0035] The creation module is configured to: create points and configure point collection protocols;

[0036] The collection 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;

[0037] The judgment module is configured to: set the determined collection number and the preset data type proportion, and receive the number of the collected point data and the proportion of the number of each data type in the collected point data;

[0038] The judgment module is further configured to: judge whether the number of the collected point data reaches the determined collection number;

[0039] If not, continue to collect the point data;

[0040] If so, determine whether the proportion of the number of a certain data type in the number of the collected point data reaches the preset data type proportion.

[0041] If not, continue to collect the point data.

[0042] If so, determine that the point data type is the data type that reaches the preset data type proportion.

[0043] As can be seen from the above, the present application provides a method and system for automatically identifying the data type of collected point data. Set the determination collection number and the preset data type proportion, collect the point data of the unpreset data type, judge whether the number of the collected point data reaches the determination collection number; and whether the proportion of the number of a certain data type in the number of the collected point data reaches the preset data type proportion. So that 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 the data processing speed and improve the overall work efficiency. At the same time, this method avoids the errors that may occur in manually configuring the data type, and improves the accuracy and efficiency of data collection. Then, by presetting the conditions for judging the satisfaction 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 the collected data is reduced. Brief Description of the Drawings

[0044] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments that conform to the implementation of the present invention, and are used together with the specification to explain the principles of the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the implementation of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic flowchart of the method for automatically identifying the data type of collected point data shown in an exemplary embodiment of the present application;

[0046] Figure 2 It is a schematic flowchart of the method for automatically identifying the data type of collected point data shown in another exemplary embodiment of the present application;

[0047] Figure 3 It is a schematic flowchart of the method for automatically identifying the data type of collected point data shown in yet another exemplary embodiment of the present application;

[0048] Figure 4 It is a schematic flowchart of the method for automatically identifying the data type of collected point data shown in still another exemplary embodiment of the present application;

[0049] Figure 5 Schematic flowchart of a method for automatically identifying the data type of a collection point shown in yet another exemplary embodiment of the present application;

[0050] Figure 6 Schematic structural diagram of a system for automatically identifying the data type of a collection point shown in an exemplary embodiment of the present application. Detailed implementation manners

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present invention.

[0052] In an industrial data collection and management system, devices represent the hardware in industrial production, and points are the operating monitoring parameters in this hardware, such as rotational speed, temperature, etc. Each device corresponds to multiple points, and the points include collection frequency information. Industrial data collection often selects relational or time-series databases, and these databases require that the data to be stored must be pre-set with data types. However, in the existing collection mode, collection personnel need to first configure point information, including point name, data type, collection protocol, etc., before data collection can be carried out. However, when configuring the data type, collection personnel are prone to errors, resulting in data being unable to be written into the database or being lost.

[0053] To solve the above problems, a first aspect of the embodiments of the present application provides a method for automatically identifying the data type of a collection point. Referring to Figure 1 as shown, it includes the steps:

[0054] S100: Create a point and configure the point collection protocol.

[0055] The collection protocol defines how to read data from a device, including communication methods, data formats, etc.

[0056] S200: If the data type of the point is not preset, set the determination collection count and the preset data type ratio.

[0057] If the data type of the point is not preset when it is created (i.e., the specific format or type of the data, such as integer, floating-point number, string, etc.), a determination condition needs to be set. This condition includes two parameters: the determination collection count and the preset data type ratio.

[0058] S300: Collect point data.

[0059] Read the data of the points from the device according to the configured collection protocol. In this step, actual data samples are obtained for subsequent data type judgment.

[0060] S400: Judge whether the number of collected point data reaches the determined collection number; if not, continue to collect point data.

[0061] Check whether the number of collected data has reached the previously set determined collection number. When the collected data is insufficient, continue to collect to ensure that there are enough data samples for data type judgment and avoid judgment errors caused by insufficient data.

[0062] Among them, the determined collection number ensures that enough data samples have been collected before data type judgment. Sufficient data samples can more accurately reflect the true distribution and characteristics of the data, thereby improving the accuracy of data type judgment. By setting a reasonable determined collection number, it is possible to ensure the accuracy of data type judgment while avoiding resource waste and time delay caused by over-collecting data. The setting of the determined collection number needs to be weighed according to the actual situation to ensure the best balance between efficiency and accuracy.

[0063] S500: If so, judge whether the proportion of the number of a certain data type in the number of collected point data reaches the preset data type proportion; if not, continue to collect point data.

[0064] If the number of collected data reaches the determined collection number, check whether the proportion of the number of a certain data type reaches the preset data type proportion.

[0065] S600: If so, judge that the point data type is the data type that reaches the preset data type proportion.

[0066] If a data type that meets the conditions is found, use this data type as the final data type of the point. Specifically, the preset data type proportion is used as the basis for data type judgment, which helps to automatically identify the dominant data type in the system. When the proportion of the number of a certain data type reaches the preset data type proportion, the system can automatically use this data type as the final data type of the point. By presetting the data type proportion, the influence 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 proportion can be adjusted according to the requirements of the actual application scenario to adapt to different data type judgment needs.

[0067] In the method of the embodiment of the present application, by introducing the determination of the number of collected data and the proportion of the preset data type, after the system collects a sufficient number of data samples, it can quickly make a data type judgment, thereby shortening the data collection cycle. This helps to speed up the data processing speed and improve the overall work efficiency. By automatically determining the data type of the points, it provides a basis for subsequent data processing. At the same time, this method avoids the errors that may occur in manually configuring the data type, and improves the accuracy and efficiency of data collection.

[0068] In some embodiments of the present application, referring to Figure 2 As shown, the S200 step of setting the number of collected data for determination and the proportion of the preset data type further includes:

[0069] S201: Set the maximum amount of data collection that triggers manual intervention.

[0070] This step sets the maximum amount of data collection that triggers manual intervention, which can prevent excessive data collection. That is, setting the maximum amount of data collection that triggers manual intervention is to prevent the system from falling into an infinite loop or over-collecting data during the data collection process. When the amount of collected data reaches this set value, if the data type has not been determined yet, the system will take further measures, such as triggering an alarm or prompting for manual intervention.

[0071] Over-collecting data may consume a large amount of system resources, including storage space and computing power. By setting the maximum amount of data collection, it can ensure the reasonable allocation of system resources and avoid resource waste. When the maximum amount of data collection is reached and the data type has not been determined yet, manual intervention may be required for judgment. This provides a clear trigger point for relevant personnel to know when to participate in the decision-making process.

[0072] The S500 step of determining whether the proportion of the number of data of a certain data type in the number of collected point data reaches the preset data type proportion further includes:

[0073] S501: When the proportion of the number of data of a certain data type in the number of collected point data does not reach the preset data type proportion; judge whether the number of collected point data is less than the maximum amount of data collection that triggers manual intervention. If not, trigger an alarm.

[0074] When the amount of collected data exceeds the maximum amount of data collection, if the dominant data type has not been found yet, this may mean that there are data anomalies or configuration errors. By judging the data type proportion, these problems can be discovered in time and corresponding measures can be taken. If data collection continues without any processing, the risk of data loss or damage may increase. By triggering an alarm, it can remind relevant personnel to take measures in time to protect the integrity and security of the data.

[0075] Through this step, the data collection process can be optimized to ensure more efficient determination of data types in future collection processes. This helps improve the accuracy and efficiency of data collection, and reduces the cost and time of manual intervention.

[0076] In this embodiment, the two supplementary steps play an important role in preventing over-collection, ensuring system resources, detecting problems in a timely manner, avoiding data loss or damage, and optimizing the data collection process in S200 and S500. The introduction of these steps makes the method for automatically identifying the data types of collection points more perfect, and improves the reliability and stability of the system.

[0077] In some embodiments of the present application, before determining whether the number of point data collected reaches the determined collection number, the following step is further included: setting a timing task, and the timing task scans and collects the number of point data at regular intervals according to the specified time for data type judgment.

[0078] Specifically, the timing task can automatically execute the data collection task according to a preset time interval or a specific time point without manual triggering. This improves the automation degree of data collection, reduces the frequency of manual operations and the risk of errors. By setting the timing task, the real-time nature of data collection can be ensured, that is, the latest point data can be obtained in a timely manner.

[0079] It can be understood that the timing task can be set to execute periodically, such as daily, weekly or monthly, etc. This makes data collection regular and predictable, facilitating subsequent data analysis and processing. The timing task can automatically execute the data collection task, avoiding the cumbersome and inefficient manual collection. At the same time, through reasonable timing settings, system resources can be fully utilized, improving the concurrency and throughput of data collection. The timing task can ensure the continuity of data collection, reducing the risk of data loss caused by missed or forgotten collection. This helps ensure the integrity and accuracy of data.

[0080] Setting a timing task to scan and collect the number of point data at regular intervals for data type judgment plays roles such as automating 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.

[0081] In some embodiments of the present application, the preset data type proportion is greater than 50%. By setting the data type proportion 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 data more clear, avoiding the judgment of multiple data types, and improving the accuracy and efficiency of judgment.

[0082] In some embodiments of the present application, referring to Figure 3 as shown, if the data type is not preset for a point position, then setting the determination collection count and the preset data type ratio further includes the steps of:

[0083] S110: Automatically generate a first table with the data type of string, and store all the point position data without configured data type in the first table.

[0084] Step S110 is responsible for automatically generating a first table with the data type of string in the system, which is used to temporarily store the point position data of those without configured data type. The first table provides a buffer area for the system, enabling the point position data to 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 position data in subsequent stages, such as configuring the data type according to actual needs or performing other operations.

[0085] After determining that the data type of the point position data reaches the data type of the preset data type ratio, the following steps are further included:

[0086] S610: Generate a second table with the data type of the data type reaching the data type ratio, store the collected point position data in the second table, and delete the first table.

[0087] Step S610 is executed after determining that the data type of the point position data reaches the preset data type ratio, and is responsible for generating a second table with the data type of the data type reaching the ratio requirement, and storing the collected point position data in this table. At the same time, this step will also delete the first table that was previously used to temporarily store the point position data without configured data type. In the second table, all the point position data is 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 position data. This makes the storage of data in the system more orderly and efficient, facilitating subsequent data analysis and processing.

[0088] In this embodiment, by creating the first table and the second table, the data security and processing flexibility are improved, the data organization and storage method are optimized, and the resource utilization rate and the overall performance of the system are improved.

[0089] In some embodiments of the present application, referring to Figure 4 as shown, step S200 of setting the determination collection count and the ratio of the preset data type further includes:

[0090] S210: Set the minimum data type ratio.

[0091] Before step S500 of determining whether the proportion of the number of a certain data type in the collected point data reaches the preset data type proportion, the following steps are also included:

[0092] S410: Screen out the data types in the collected point data that are lower than the minimum proportion of the data type, and subtract the number of the screened data types from the number of the collected point data.

[0093] By setting the minimum proportion of the data type, data types with too low proportions, which may not be representative or important, can be excluded, helping to improve the quality of the overall data and making subsequent analysis and processing more accurate and effective. And data types lower than the minimum proportion of the data type are often likely to be noise data or abnormal data. By screening and excluding these data types, the interference of noise data on the overall data analysis results can be reduced.

[0094] In the embodiments of the present application, by setting the minimum proportion of the data type, the data quality is further optimized and the noise data is reduced. The system can analyze and judge the data type based on more accurate data, which helps to improve the accuracy and reliability of the judgment.

[0095] In some embodiments of the present application, referring to Figure 5 as shown, the following steps are also included:

[0096] S700: Set the verification number, and perform a re - collection verification on the point data for which the data type judgment has been completed;

[0097] S800: Recalculate whether the proportion of the data type after adding the verification number in the collected point data can still reach the preset data type proportion;

[0098] If so, the verification of the data type judgment is correct;

[0099] If not, re - judge the point data type for the point.

[0100] Step S700 provides additional data support for the data type judgment by setting the verification number and performing a re - collection. Step S800 ensures the robustness of the system in data type judgment by recalculating the data type proportion and verifying the data type judgment based on the result.

[0101] In the embodiments of the present application, by collecting additional data and recalculating the proportion of data types, in the newly collected data, the proportion of the dominant data type still meets the requirements, and it can be confirmed again that this type is the dominant data type; if the proportion decreases and no longer meets the conditions of the preset data type proportion, re-evaluation is required. This helps to reduce misjudgment caused by abnormal initial collection, thereby improving the accuracy of data type judgment. By adding a verification step, the system can more accurately judge the type of point data, thereby enhancing 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.

[0102] In some embodiments of the present application, before determining whether the number of collected point data reaches the determination collection number, the steps further include: cleaning the collected point data to remove incorrect or invalid data. The data set after data cleaning is more pure and accurate, which helps to optimize the data judgment result, improve data quality, reduce the calculation burden, and avoid misleading conclusions.

[0103] Specifically, the present application gives a specific case to illustrate the system for automatically identifying the type of collected point data. In a certain energy data collection project, there are 119,156 points. Before using the function of automatically determining the type of point data, the number of misconfigured point data types reaches 489, and the misconfiguration rate reaches 0.41%. After setting these points to automatically determine the data type, 78 points are automatically judged with incorrect data types, and 23 trigger manual intervention to manually set the point data type. The overall error rate of automatically judging the data type is 0.08%, reducing the error rate of point data types by about 80%. It effectively improves the accuracy of point data type configuration.

[0104] On the other hand, the embodiments of the present application provide a system for automatically identifying the type of collected point data. Referring to Figure 6 as shown, for implementing any of the above methods for automatically identifying the type of collected point data, including: a collection module, a creation module, and a judgment module.

[0105] The creation module is configured to: create points and configure point collection protocols; be responsible for creating points and configuring point collection protocols. Through the creation module, the user can define the points to be collected and the corresponding collection rules, such as collection frequency, data type, etc. The creation module is used to create point information, which may include point name, location, attributes, etc.; configure the collection protocol to determine the data collection method, format, time interval, etc. for each point.

[0106] The acquisition module is configured to: acquire point data from the creation module, and send the number of acquired point data and the proportion of each data type to the judgment module. The acquisition module executes the data acquisition task, obtains data from the points according to the preset acquisition protocol; counts the number of acquired point data, and the proportion of each data type in the total data; and sends the statistical results and the acquired data to the judgment module.

[0107] The judgment module is configured to: set the determined acquisition number and the preset data type proportion, and receive the number of acquired point data and the proportion of the number of each data type in the number of acquired point data.

[0108] The judgment module is further configured to: judge whether the number of acquired point data reaches the determined acquisition number;

[0109] If not, continue to acquire point data;

[0110] If so, judge whether there is a proportion of the number of a certain data type in the number of acquired point data that reaches the preset data type proportion;

[0111] If not, continue to acquire point data;

[0112] If so, judge that the point data type is the data type that reaches the preset data type proportion.

[0113] The system for automatically identifying the type of acquired point data provided by the embodiments of the present application greatly reduces manual intervention and improves work efficiency through an automated acquisition and judgment process. The system supports users to customize points and acquisition protocols, and can meet the data acquisition requirements in different scenarios. By setting the determined acquisition number and the preset data type proportion, the system can more accurately identify the type of point data, avoiding misjudgment caused by insufficient data volume or unclear data type.

[0114] After considering the specification and the disclosure of the embodiments, those skilled in the art will easily think of other implementation schemes of the present disclosure. This application aims to cover any variations, uses or adaptations of the present disclosure, and these variations, uses or adaptations follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out 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 number of collected items and the proportion of preset data types 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; determine whether the number of collected point data items is less than the maximum collection amount that triggers manual intervention, if not, trigger an alarm.

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. The 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 whose data type is a data type that reaches the data type ratio, 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 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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