CN-115145902-B - Data processing method, device, storage medium and electronic equipment

CN115145902BCN 115145902 BCN115145902 BCN 115145902BCN-115145902-B

Abstract

The application discloses a data processing method, a data processing device, a storage medium and electronic equipment. The method comprises the steps of collecting target data reported by target equipment, calling a data cleaning rule mapping, obtaining a target cleaning rule corresponding to the target data according to the identification of the target equipment and the data cleaning rule mapping, wherein the data cleaning rule mapping is at least used for indicating the mapping relation between the equipment identification and the cleaning rule, determining the attribute type of the target data according to the target cleaning rule, and executing different data processing strategies according to the attribute type. The application solves the technical problems that the data can be stored only based on the third-party IOT monitoring tool in the related technology, the accurate data can not be obtained due to the failure of eliminating the error data, the subsequent reasonable and timely early warning can not be carried out, and the prediction analysis is difficult to accurately carry out.

Inventors

ZENG FANYONG
ZHU JIE
MA DONGMEI
ZHANG BEIXIAN
LI RAN

Assignees

北京智能建筑科技有限公司
北京智能建筑科技有限公司

Dates

Publication Date: 20260421
Application Date: 20220704
Priority Date: 20220704

Claims (9)

1. A method of data processing, comprising: Acquiring target data reported by target equipment, wherein the target data comprises target acquisition time for detecting and acquiring the target data reported by the target equipment, acquiring a time length difference value between the target acquisition time and the last acquisition time, and executing different storage operations on the target data according to the time length difference value, wherein the different storage operations on the target data comprise the steps of storing historical data corresponding to the last acquisition time and the target data under the condition that the time length difference value is larger than a second preset threshold value, triggering a data notification, wherein the second preset threshold value is the time length of one period, and the current data time point is the time point after two periods under the condition that the time length difference value is larger than the second preset threshold value; Invoking a data cleaning rule mapping, and acquiring a target cleaning rule corresponding to the target data according to the identification of the target equipment and the data cleaning rule mapping, wherein the data cleaning rule mapping is at least used for indicating the mapping relation between the equipment identification and the cleaning rule; Determining the attribute type of the target data according to a target cleaning rule; and executing different data processing strategies according to the attribute types, wherein the target data processed by adopting the data processing strategies is applied to building intelligent decisions.
2. The method of claim 1, wherein performing different storage operations on the target data based on the time duration difference comprises: Under the condition that the time length difference value is smaller than a first preset threshold value, determining that the target data is outdated, and eliminating the target data; and storing the historical data corresponding to the last acquisition time under the condition that the time length difference value is larger than a first preset threshold value and equal to a second preset threshold value.
3. The method of claim 1, wherein determining the number of points to be complemented based on the duration difference and the data period to be complemented comprises: and determining a first ratio of the time length difference value to the data period to be complemented, and determining a difference value between the first ratio and a first preset value to obtain the number of the points to be complemented.
4. The method of claim 1, wherein determining a point delta to be replenished from the difference to be replenished and the number of points to be replenished comprises: adding the number of the points to be complemented with a second preset value to obtain a sum of the number of the points to be complemented and the second preset value; and determining the ratio of the to-be-compensated difference value to the sum value to obtain the to-be-compensated point increment.
5. The method of claim 1, wherein executing different data processing policies according to the attribute type comprises: and under the condition that the attribute type is increment or fluctuation, checking whether the data value range in the target data is within a preset range or not to obtain a first check result, and if the first check result indicates that the data value range in the target data is not within the preset range, determining that the data is not cleaned, and rejecting the target data.
6. The method of claim 1, wherein executing different data processing policies according to the attribute type comprises: and under the condition that the attribute type is enumeration, checking whether the data in the target data are in an enumeration value list or not to obtain a second check result, and if the second check result indicates that the target data are not in the enumeration value list, determining that the data are not cleaned, and rejecting the target data.
7. A data processing apparatus, comprising: The acquisition module is used for acquiring target data reported by target equipment and comprises the steps of detecting and acquiring target acquisition time of the target data reported by the target equipment; the method comprises the steps of acquiring a time length difference value between a target acquisition time and a last acquisition time, executing different storage operations on the target data according to the time length difference value, wherein the different storage operations on the target data according to the time length difference value comprise the steps of storing historical data corresponding to the last acquisition time and the target data under the condition that the time length difference value is larger than a second preset threshold value, triggering a full data notification, wherein the second preset threshold value is the time length of one period, and the current data time point is the time point after two periods under the condition that the time length difference value is larger than the second preset threshold value; The calling module is used for calling a data cleaning rule mapping, and acquiring a target cleaning rule corresponding to the target data according to the identification of the target equipment and the data cleaning rule mapping, wherein the data cleaning rule mapping is at least used for indicating the mapping relation between the equipment identification and the cleaning rule; The determining module is used for determining the attribute type of the target data according to the target cleaning rule; and the execution module is used for executing different data processing strategies according to the attribute types, wherein the target data processed by adopting the data processing strategies is applied to building intelligent decisions.
8. A non-volatile storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the data processing method according to any one of claims 1 to 6.
9. An electronic device, comprising: A processor; a memory for storing the processor-executable instructions; Wherein the processor is configured to execute the instructions to implement the data processing method of any one of claims 1 to 6.

Description

Data processing method, device, storage medium and electronic equipment Technical Field The present application relates to the field of data processing, and in particular, to a data processing method, apparatus, storage medium, and electronic device. Background The building intellectualization is based on comprehensive application of various intelligent information by taking the building as a platform, integrates architecture, system, application, management and optimization combination, has comprehensive intelligent ability of perception, transmission, memory, reasoning, judgment and decision, forms an integration body with people, building and environment being coordinated with each other, and provides a building with safe, efficient, convenient and sustainable development function environment for people. However, in the building intelligent construction, a large amount of IoT devices and gateways are required to be used for data collection depending on data support, once the gateways or IoT devices fail, even more network failure can generate data loss or erroneous data, lose the reliability of the data and easily cause erroneous decisions. Currently existing data processing technologies are mainly third party IoT monitoring tools and platforms and stored accordingly. This approach can only simply store data, cannot reject or complement erroneous data, and is extremely unreasonable. Therefore, accurate data cannot be obtained in the intelligent process, the guiding function of fault early warning cannot be achieved, reasonable and timely early warning cannot be conducted, whether faults occur or not can be found only when the faults occur, predictive analysis is difficult to conduct, and intelligent decision making of the building is not facilitated. In view of the above problems, no effective solution has been proposed at present. Disclosure of Invention The embodiment of the application provides a data processing method, a device, a storage medium and electronic equipment, which at least solve the technical problems that in the related art, because a third-party IOT (information of presence) monitoring tool can only store data, accurate data cannot be obtained due to incapability of eliminating error data, subsequent reasonable and timely early warning cannot be performed, and prediction analysis is difficult to accurately perform. According to one aspect of the embodiment of the application, a data processing method is provided, which comprises the steps of collecting target data reported by target equipment, calling a data cleaning rule mapping, obtaining a target cleaning rule corresponding to the target data according to the identification of the target equipment and the data cleaning rule mapping, wherein the data cleaning rule mapping is at least used for indicating the mapping relation between the equipment identification and the cleaning rule, determining the attribute type of the target data according to the target cleaning rule, and executing different data processing strategies according to the attribute type. Optionally, the method comprises the steps of collecting target data reported by target equipment, detecting target collecting time for collecting the target data reported by the target equipment, obtaining a time length difference value between the target collecting time and the last collecting time, and executing different storage operations on the target data according to the time length difference value. Optionally, different storage operations are performed on the target data according to the duration difference, wherein the storage operations comprise determining that the target data is out of date and rejecting the target data when the duration difference is smaller than a first preset threshold, storing historical data corresponding to the last acquisition time when the duration difference is larger than the first preset threshold and equal to a second preset threshold, storing the historical data corresponding to the last acquisition time and the target data when the duration difference is larger than the second preset threshold, and triggering completion data notification. The method comprises the steps of determining a first target value corresponding to target acquisition time, a second target value corresponding to last acquisition time and a data period to be complemented respectively, determining the number of points to be complemented according to a time length difference value and the data period to be complemented, obtaining a difference value to be complemented according to the first target value and the second target value, and determining the increment of the points to be complemented according to the difference value to be complemented and the number of points to be complemented. Optionally, determining the number of the points to be complemented according to the time length difference and the data period to be complemented comprises determining a first ratio of t