CN113903464A - Data processing method and device applied to health monitoring of employees and server - Google Patents
Data processing method and device applied to health monitoring of employees and server Download PDFInfo
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention provides a data processing method, a data processing device and a server applied to practitioner health monitoring. And secondly, determining a second weight attribute characteristic matched with the evaluation weight data based on the obtained first weight attribute characteristic. And then performing data classification on the evaluation weight data according to the second weight attribute characteristics. Therefore, the acquired health data to be processed is subjected to data classification by setting the first weight attribute feature and the second weight attribute feature, so that the health data can be subjected to accurate data classification, and later-period calling is facilitated.
Description
Technical Field
The invention relates to the technical field of personnel health data processing, in particular to a data processing method, a data processing device and a server applied to health monitoring of workers.
Background
With the normalization of public health events, health monitoring needs to be performed on practitioners in various industries, and acquired health data needs to be managed. However, the common method for managing health data is difficult to realize accurate data classification, and is not beneficial to later calling.
Disclosure of Invention
In order to solve the problems, the invention provides a data processing method, a data processing device and a server applied to health monitoring of workers.
The invention provides a data processing method applied to health monitoring of practitioners, which comprises the following steps:
determining evaluation weight data corresponding to health evaluation data in the personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of person health data;
carrying out weight attribute analysis on the evaluation weight data through call paths in various unit execution functions of a preset data processing unit, and determining a first weight attribute characteristic matched with the evaluation weight data;
determining a second weight attribute feature matched with the evaluation weight data through a unit execution function in a plurality of unit execution functions in the preset data processing unit based on the first weight attribute feature;
and according to the second weight attribute characteristics matched with the evaluation weight data, performing data classification on the obtained health data to be processed through a unit execution function of the preset data processing unit to obtain a health data classification result.
In an alternative embodiment, the performing, by using a call path in a function executed by multiple units of the preset data processing unit, a weight attribute analysis on the evaluation weight data to determine a first weight attribute feature matching the evaluation weight data specifically includes:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
In an alternative embodiment, the determining, by a first execution function in the preset data processing unit, multidimensional weight index data corresponding to the evaluation weight data specifically includes:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
In an alternative embodiment, the performing, by a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data, data classification on the obtained to-be-processed health data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
The invention also provides a data processing device applied to health monitoring of practitioners, which comprises:
the data acquisition module is used for determining evaluation weight data corresponding to the health evaluation data in the personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of person health data;
the first determining module is used for performing weight attribute analysis on the evaluation weight data through a calling path in various unit execution functions of a preset data processing unit and determining a first weight attribute feature matched with the evaluation weight data;
a second determining module, configured to determine, based on the first weight attribute feature, a second weight attribute feature that matches the evaluation weight data through a unit execution function of multiple unit execution functions in the preset data processing unit;
and the data classification module is used for performing data classification on the acquired health data to be processed through a unit execution function of the preset data processing unit according to the second weight attribute characteristic matched with the evaluation weight data to obtain a health data classification result.
In an alternative embodiment, the performing, by using a call path in a function executed by multiple units of the preset data processing unit, a weight attribute analysis on the evaluation weight data to determine a first weight attribute feature matching the evaluation weight data specifically includes:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
In an alternative embodiment, the determining, by a first execution function in the preset data processing unit, multidimensional weight index data corresponding to the evaluation weight data specifically includes:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
In an alternative embodiment, the performing, by a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data, data classification on the obtained to-be-processed health data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
The present invention also provides a server, comprising: a memory, a processor, and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The present invention also provides a computer-readable storage medium, comprising a computer program; the computer program controls the electronic device where the readable storage medium is located to execute the method when running.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The invention provides a data processing method, a data processing device and a server applied to health monitoring of practitioners. And secondly, determining a second weight attribute characteristic matched with the evaluation weight data based on the obtained first weight attribute characteristic. And then performing data classification on the evaluation weight data according to the second weight attribute characteristics. Therefore, the acquired health data to be processed is subjected to data classification by setting the first weight attribute feature and the second weight attribute feature, so that the health data can be subjected to accurate data classification, and later-period calling is facilitated.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a data processing method applied to health monitoring of a practitioner according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of a data processing device applied to health monitoring of a practitioner according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a server according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a flow chart of a data processing method applied to health monitoring of a practitioner is shown, which includes the following descriptions of step S110 to step S140.
Step S110, determining evaluation weight data corresponding to health evaluation data in a personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of human health data.
And step S120, performing weight attribute analysis on the evaluation weight data through call paths in various unit execution functions of a preset data processing unit, and determining a first weight attribute feature matched with the evaluation weight data.
Step S130, determining a second weight attribute feature matched with the evaluation weight data through a unit execution function of the plurality of unit execution functions in the preset data processing unit based on the first weight attribute feature.
And step S140, performing data classification on the acquired to-be-processed health data through a unit execution function of the preset data processing unit according to the second weight attribute characteristics matched with the evaluation weight data to obtain a health data classification result.
Executing the contents described in the above steps S110 to S140, first obtaining evaluation weight data, and further performing weight attribute analysis on the evaluation weight data according to call paths in various unit execution functions of the preset data processing unit. And secondly, determining a second weight attribute characteristic matched with the evaluation weight data based on the obtained first weight attribute characteristic. And then performing data classification on the evaluation weight data according to the second weight attribute characteristics. Therefore, the acquired health data to be processed is subjected to data classification by setting the first weight attribute feature and the second weight attribute feature, so that the health data can be subjected to accurate data classification, and later-period calling is facilitated.
In an alternative embodiment, the performing, by using a call path in a function executed by multiple units of the preset data processing unit, a weight attribute analysis on the evaluation weight data to determine a first weight attribute feature matching the evaluation weight data specifically includes:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
In an alternative embodiment, the determining, by a first execution function in the preset data processing unit, multidimensional weight index data corresponding to the evaluation weight data specifically includes:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
In an alternative embodiment, the performing, by a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data, data classification on the obtained to-be-processed health data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
Referring to fig. 2, a functional block diagram of a data processing device 200 for health monitoring of a practitioner is shown, the device including:
the data acquisition module 210 is configured to determine evaluation weight data corresponding to health evaluation data in the personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of person health data;
the first determining module 220 is configured to perform weight attribute analysis on the evaluation weight data through a call path in a multiple unit execution function of a preset data processing unit, and determine a first weight attribute feature matched with the evaluation weight data;
a second determining module 230, configured to determine, based on the first weight attribute feature, a second weight attribute feature matched with the evaluation weight data through a unit execution function of multiple unit execution functions in the preset data processing unit;
and the data classification module 240 is configured to perform data classification on the obtained to-be-processed health data through a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data, so as to obtain a health data classification result.
In an alternative embodiment, the performing, by using a call path in a function executed by multiple units of the preset data processing unit, a weight attribute analysis on the evaluation weight data to determine a first weight attribute feature matching the evaluation weight data specifically includes:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
In an alternative embodiment, the determining, by a first execution function in the preset data processing unit, multidimensional weight index data corresponding to the evaluation weight data specifically includes:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
In an alternative embodiment, the performing, by a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data, data classification on the obtained to-be-processed health data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
Referring to fig. 3, a hardware structure diagram of the server 100 is provided.
Fig. 3 is a block diagram illustrating a server 100 according to an embodiment of the present invention. In an embodiment of the present invention, a server 100 may be a server with data storage, transmission, and processing functions, as shown in fig. 3, the server 100 includes: memory 111, processor 112, network module 113 and data processing apparatus 200 for use in health monitoring of a practitioner.
The memory 111, the processor 112, and the network module 113 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 111 stores therein a data processing device 200 applied to health monitoring of a practitioner, the data processing device 200 applied to health monitoring of a practitioner includes at least one software functional module which can be stored in the memory 111 in the form of software or firmware (firmware), and the processor 112 executes various functional applications and data processing by running software programs and modules stored in the memory 111, for example, the data processing device 200 applied to health monitoring of a practitioner in the embodiment of the present invention, so as to implement the method in the embodiment of the present invention.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction.
The processor 112 may be an integrated circuit chip having data processing capabilities. The Processor 112 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 113 is used for establishing a communication connection between the server 100 and other communication terminal devices through a network, so as to implement transceiving operations of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that a server 100 may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present invention also provides a computer-readable storage medium, which includes a computer program. The computer program controls the readable storage medium to execute the following data processing method applied to health monitoring of the practitioner in a server 100.
In summary, by applying the method, the apparatus and the server, the evaluation weight data is first obtained, and further, the evaluation weight data is subjected to weight attribute analysis according to the call paths in the multiple unit execution functions of the preset data processing unit. And secondly, determining a second weight attribute characteristic matched with the evaluation weight data based on the obtained first weight attribute characteristic. And then performing data classification on the evaluation weight data according to the second weight attribute characteristics. Therefore, the acquired health data to be processed is subjected to data classification by setting the first weight attribute feature and the second weight attribute feature, so that the health data can be subjected to accurate data classification, and later-period calling is facilitated.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a server (which may be a personal computer, an electronic device 10, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A data processing method applied to health monitoring of a practitioner, the method comprising:
determining evaluation weight data corresponding to health evaluation data in the personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of person health data;
carrying out weight attribute analysis on the evaluation weight data through call paths in various unit execution functions of a preset data processing unit, and determining a first weight attribute characteristic matched with the evaluation weight data;
determining a second weight attribute feature matched with the evaluation weight data through a unit execution function in a plurality of unit execution functions in the preset data processing unit based on the first weight attribute feature;
and according to the second weight attribute characteristics matched with the evaluation weight data, performing data classification on the obtained health data to be processed through a unit execution function of the preset data processing unit to obtain a health data classification result.
2. The method according to claim 1, wherein the performing weight attribute analysis on the evaluation weight data through a call path in a plurality of unit execution functions of the preset data processing unit to determine a first weight attribute feature matching the evaluation weight data specifically comprises:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
3. The method according to claim 2, wherein the determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit specifically comprises:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
4. The method according to claim 1, wherein the step of performing data classification on the acquired health data to be processed through a unit execution function of the preset data processing unit according to the second weight attribute feature matched with the evaluation weight data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
5. A data processing apparatus for health monitoring of a practitioner, the apparatus comprising:
the data acquisition module is used for determining evaluation weight data corresponding to the health evaluation data in the personnel health data set; wherein the evaluation weight data comprises a set of evaluation weight data to be identified within a same monitoring period of the set of person health data;
the first determining module is used for performing weight attribute analysis on the evaluation weight data through a calling path in various unit execution functions of a preset data processing unit and determining a first weight attribute feature matched with the evaluation weight data;
a second determining module, configured to determine, based on the first weight attribute feature, a second weight attribute feature that matches the evaluation weight data through a unit execution function of multiple unit execution functions in the preset data processing unit;
and the data classification module is used for performing data classification on the acquired health data to be processed through a unit execution function of the preset data processing unit according to the second weight attribute characteristic matched with the evaluation weight data to obtain a health data classification result.
6. The apparatus according to claim 5, wherein the performing, by using a call path in the multiple unit execution functions of the preset data processing unit, a weight attribute analysis on the evaluation weight data to determine a first weight attribute feature matching the evaluation weight data specifically includes:
determining multidimensional weight index data corresponding to the evaluation weight data through a first execution function in the preset data processing unit;
determining target label data corresponding to the multi-dimensional weight index data through key data in the calling path;
and in response to the target label data, performing weight attribute analysis on feature data of one label data in the multi-dimensional weight index data through multiple data existence forms in the calling path, and determining a first weight attribute feature matched with the evaluation weight data.
7. The apparatus according to claim 5, wherein the determining, by a first execution function in the preset data processing unit, multidimensional weight index data corresponding to the evaluation weight data specifically includes:
filtering the evaluation weight data through the first execution function;
and performing label index division processing on the filtered evaluation weight data according to the target log event and the data expression form of the first execution function to obtain a division result corresponding to the evaluation weight data, performing category data fusion processing on the division result of the evaluation weight data, and determining multi-dimensional weight index data corresponding to the evaluation weight data.
8. The apparatus according to claim 5, wherein the performing, by a unit execution function of the preset data processing unit, data classification on the obtained to-be-processed health data according to the second weight attribute feature matched with the evaluation weight data specifically includes:
determining a data mark position of a second execution function of the preset data processing unit;
and based on a second weight attribute characteristic matched with the evaluation weight data, performing weight index marking on the multidimensional weight index data according to the second execution function of the corresponding data marking position to obtain a marking result, and performing data classification on the obtained health data to be processed according to the marking result to obtain a health data classification result.
9. A server, comprising: a memory, a processor, and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-4 by reading the computer program from the memory and running it.
10. A computer-readable storage medium, characterized in that the readable storage medium comprises a computer program; the computer program controls the electronic device in which the readable storage medium is executed to perform the method of any one of claims 1-4 when executed.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011283523.3A CN113903464A (en) | 2020-11-17 | 2020-11-17 | Data processing method and device applied to health monitoring of employees and server |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011283523.3A CN113903464A (en) | 2020-11-17 | 2020-11-17 | Data processing method and device applied to health monitoring of employees and server |
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| CN113903464A true CN113903464A (en) | 2022-01-07 |
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| CN202011283523.3A Withdrawn CN113903464A (en) | 2020-11-17 | 2020-11-17 | Data processing method and device applied to health monitoring of employees and server |
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2020
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