CN114678130A - Standard rule based evaluation method, terminal equipment and storage medium - Google Patents
Standard rule based evaluation method, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an evaluation method, terminal equipment and storage medium based on standard rules, wherein the method comprises the following steps: s1: splitting the standard rule into a plurality of judgment conditions and logic relations among the judgment conditions, constructing each judgment condition into an atom rule, and constructing the logic relations into a composite rule; s2: according to the hierarchical relation between the atomic rules and the compound rules corresponding to the standard rules, a rule tree is constructed and stored; s3: when data to be evaluated through the standard rule is received, the data are respectively matched with the rule corresponding to each node in the rule tree according to the sequence from the leaf node to the root node, and the evaluation result of the standard rule is obtained according to the matching result of the root node; s4: and taking all matched rules as evaluation basis of the evaluation result. The method and the system can assist medical staff to obtain important indexes which may bring risks from a plurality of dimensionality data.
Description
Technical Field
The present invention relates to the field of standard evaluation, and in particular, to an evaluation method based on a standard rule, a terminal device, and a storage medium.
Background
Whether the newborn baby is healthy and the delivery can be smoothly carried out is closely related to the health condition of the mother body. Maternal health conditions relate to aspects including but not limited to heart, liver and kidney, lung, blood type, thyroid, infectious diseases, etc. The method has the advantages of wide range of design, low efficiency depending on human judgment, and easy occurrence of information omission. With the opening of the policy of two-birth and multi-birth, the number of the elderly pregnant and lying-in women is greatly increased, the physiological functions of the elderly pregnant and lying-in women are relatively reduced, and the dimension which is easy to bring risks is increased.
Disclosure of Invention
In order to solve the above problems, the present invention provides an evaluation method based on a standard rule, a terminal device, and a storage medium.
The specific scheme is as follows:
a method of standard rule based evaluation comprising the steps of:
s1: splitting the standard rule into a plurality of judgment conditions and logic relations among the judgment conditions, constructing each judgment condition into an atom rule, and constructing the logic relations into a composite rule;
s2: according to the hierarchical relation between the atomic rules and the compound rules corresponding to the standard rules, a rule tree is constructed and stored;
s3: when data to be evaluated through the standard rule is received, the data are respectively matched with the rule corresponding to each node in the rule tree according to the sequence from the leaf node to the root node, and the evaluation result of the standard rule is obtained according to the matching result of the root node;
s4: and taking all matched rules as evaluation basis of the evaluation result.
Further, the attributes of the atomic rules include: the attribute name, operator, critical value, and parent node ID are determined.
Further, a compound rule consists of a logical relationship between different atomic rules, a logical relationship between different compound rules, or a logical relationship between different atomic rules and compound rules.
Further, the attributes of the compound rule include: logical operators, parent ID, and all child IDs that make up the composite rule.
Further, step S3 further includes: and reducing the dimension of the received data with the multilayer nested structure into one-dimensional data.
Further, step S3 further includes: and according to a pre-constructed near meaning word list containing all near meaning words of the judging attribute names, uniformly renaming all near meaning words in the data to be corresponding judging attribute names.
Further, step S3 further includes: according to all judgment attribute names contained in the rule tree, extracting values corresponding to the judgment attribute names from the data, forming key value pairs by the extracted values and the corresponding judgment attribute names, setting the keys as the judgment attribute names, and setting the values as the corresponding attribute values.
Further, when the data only includes an attribute value corresponding to a judgment attribute name in the matching in step S3, and when the data matches the atomic rule, directly judging whether the attribute value corresponding to the judgment attribute name included in the data satisfies the judgment condition corresponding to the operator and the critical value in the atomic rule; when the data is matched with the composite rule, obtaining the matching results of the data and all atomic rules under the composite rule, judging whether all the matching results meet the logical relationship corresponding to the composite rule, if so, judging that the matching can be performed, otherwise, judging that the matching cannot be performed; and when the data comprises at least two attribute values corresponding to the judgment attribute names, matching is respectively carried out aiming at each attribute value.
An evaluation terminal device based on standard rules comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
By adopting the technical scheme, the medical staff can be assisted to comprehensively know various clinical data of the staff to be evaluated, important indexes which possibly bring risks can be obtained from data of multiple dimensions, the medical staff are reminded to pay attention to the indexes, and corresponding risk grades can be obtained according to standard rules to provide references for subsequent medical procedures.
Drawings
Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
Fig. 2 is a schematic diagram showing 4 risk factors involved in anemia in this example.
Fig. 3 is a schematic diagram of the multi-layer determination condition corresponding to pregnancy complicated anemia in this embodiment.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides an evaluation method based on standard rules, as shown in fig. 1, the method comprises the following steps:
s1: the standard rule is divided into a plurality of judgment conditions and logic relations among the judgment conditions, each judgment condition is constructed into an atomic rule, and the logic relations are constructed into composite rules.
Standard rules are generally standards promulgated by a professional with some degree of efficacy.
As shown in fig. 2, among the 4 risk factors included in anemia, different risk factors correspond to different risk levels, such as blood system diseases: pregnancy associated anemia (Hb60-110g/L) corresponds to a risk rating of grade 1 (i.e., mild). This example is described in terms of hematological disorders: the standard rule corresponding to pregnancy combined anemia (Hb60-110g/L) is explained as an example, and when the pregnancy combined anemia belongs to a blood system disease: when pregnancy is complicated with anemia (Hb60-110g/L), it is necessary to satisfy the multi-layer judgment conditions shown in FIG. 3, where different judgment conditions are connected by logical relations (and/or), and each judgment condition includes a judgment attribute name (e.g., value), an operator (e.g., greater than or equal to) and a threshold (e.g., 130). Therefore, the hierarchical structure data shown in fig. 3 can be converted into tree structure data, that is, the judgment condition at the bottom layer is used as an atomic rule, and the logical relationship between the middle layer and the top layer is used as a composite rule. Each atomic rule can only yield one result, true or false, which is a match-capable result when the result is true and a mismatch result when the result is false.
A compound rule may consist of a logical relationship between different atomic rules, a logical relationship between different compound rules, or a logical relationship between different atomic rules and compound rules.
S2: and constructing and storing a rule tree according to the hierarchical relationship between the atomic rule and the compound rule corresponding to the standard rule.
The storage contents of different types of rules in storage are different, an atomic rule used as the lowest layer (leaf node) for judgment needs to store at least a judgment attribute name, an operator, a critical value and a parent node ID (code), and a composite rule used as an intermediate node and a root node needs to store at least a logical operator, a parent node ID and all child node IDs constituting the composite rule.
S3: when data to be evaluated through the standard rule is received, the data are respectively matched with the rule corresponding to each node in the rule tree according to the sequence from the leaf node to the root node, and the evaluation result of the standard rule is obtained according to the matching result of the root node.
In a specific matching procedure, firstly, data is matched with rules (namely atomic rules) corresponding to all leaf nodes one by one, when the rules corresponding to a certain leaf node are matched, the data is matched with the rules corresponding to a parent node corresponding to the leaf node, and when the rules corresponding to the parent node are matched, the rules corresponding to the parent node of the parent node are continuously matched until a root node is reached. When a rule (atomic rule or conforming rule) corresponding to a node fails to match, then the matching of the parent node of the node is stopped.
And when the matching result of the rule corresponding to the root node is that the matching can be realized, judging that the data meets the standard rule, and when the matching cannot be realized, judging that the data does not meet the standard rule.
Furthermore, because data has various sources, various data structures and various expression forms of attribute names, and fixed characters are often required to be matched during rule matching, the received data needs to be preprocessed before the rule matching is performed on the data, so that the rule matching is facilitated.
The preprocessing includes dimension reduction, i.e., converting the received data with a multi-layer nested structure into one-dimensional data. Data of a multi-layer nested structure such as a json character string format needs to be converted into one-dimensional data.
The preprocessing further comprises renaming, and according to a pre-constructed near meaning word list containing all near meaning words of the judging attribute names, all near meaning words in the data are renamed to the corresponding judging attribute names in the rule.
Further, in order to facilitate extraction of the judgment attribute name and the corresponding attribute value required in the data, in this embodiment, the preprocessing further includes extracting all key value pairs from the data, where a key value pair key is a judgment attribute name and a value is a corresponding attribute value.
When rule matching is carried out, different matching modes are provided according to different data types and different rule types.
When the data type is a basic type (namely only comprises a group of key value pairs), directly judging whether the attribute values in the key value pairs meet the judgment conditions corresponding to operators and critical values in the atomic rules or not when the data type is matched with the atomic rules; when the matching result is matched with the composite rule, the matching result of the atomic rule under the composite rule and all the atomic rules under the composite rule is obtained, whether all the matching results meet the logical relation corresponding to the composite rule or not is judged, if yes, matching is judged to be possible, and if not, matching is judged not to be possible.
When the data type is a complex type (i.e. includes at least two groups of key value pairs), the data type is split into a plurality of basic type data, and each basic type data is matched according to the above method, which is not described herein again.
If the root node can be matched, the obtained evaluation result is a risk level of 1 (i.e., slight).
Further, since it is often necessary to show the evaluation basis for obtaining the evaluation results when obtaining the evaluation results, the embodiment further includes S4: and outputting all matched rules as evaluation basis of the evaluation result.
The embodiment of the invention can assist medical staff to comprehensively know various clinical data of staff to be evaluated, can acquire important indexes which possibly bring risks from multi-dimensional data, can remind the medical staff to pay attention to the indexes, and can obtain corresponding risk grades according to standard rules so as to provide reference for subsequent medical processes.
Example two:
the invention further provides an evaluation terminal device based on the standard rule, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the above method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the evaluation terminal device based on the standard rule may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The standard rule based evaluation terminal device may include, but is not limited to, a processor, a memory. It is understood by those skilled in the art that the above-mentioned structure of the evaluation terminal device based on the standard rule is only an example of the evaluation terminal device based on the standard rule, and does not constitute a limitation of the evaluation terminal device based on the standard rule, and may include more or less components than the above, or combine some components, or different components, for example, the evaluation terminal device based on the standard rule may further include an input-output device, a network access device, a bus, etc., which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the standard rule based evaluation terminal device, and various interfaces and lines are used to connect various parts of the whole standard rule based evaluation terminal device.
The memory may be used for storing the computer program and/or module, and the processor may implement various functions of the standard rule-based evaluation terminal device by executing or executing the computer program and/or module stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The standard rule-based evaluation terminal integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method of standard rule based evaluation comprising the steps of:
s1: splitting the standard rule into a plurality of judgment conditions and logic relations among the judgment conditions, constructing each judgment condition into an atom rule, and constructing the logic relations into a composite rule;
s2: according to the hierarchical relation between the atomic rules and the compound rules corresponding to the standard rules, a rule tree is constructed and stored;
s3: when data to be evaluated through the standard rule is received, the data are respectively matched with the rule corresponding to each node in the rule tree according to the sequence from the leaf node to the root node, and the evaluation result of the standard rule is obtained according to the matching result of the root node;
s4: and taking all matched rules as evaluation basis of the evaluation result.
2. The standard rule based evaluation method of claim 1, wherein: attributes of atomic rules include: the attribute name, operator, critical value, and parent node ID are determined.
3. The standard rule based evaluation method of claim 1, wherein: the compound rules consist of logical relationships between different atomic rules, logical relationships between different compound rules, or logical relationships between different atomic rules and compound rules.
4. The standard rule based evaluation method of claim 1, wherein: the attributes of the compound rule include: logical operators, parent ID, and all child IDs that make up the composite rule.
5. The standard rule based evaluation method of claim 1, wherein: step S3 further includes: and reducing the dimension of the received data with the multilayer nested structure into one-dimensional data.
6. The standard rule based evaluation method of claim 1, wherein: step S3 further includes: and uniformly renaming all the similar meaning words in the data to corresponding judgment attribute names according to a pre-constructed similar meaning word list containing all the similar meaning words of the judgment attribute names.
7. The standard rule based evaluation method of claim 1, wherein: step S3 further includes: according to all judgment attribute names contained in the rule tree, extracting values corresponding to the judgment attribute names from the data, forming key value pairs by the extracted values and the corresponding judgment attribute names, setting the keys as the judgment attribute names, and setting the values as the corresponding attribute values.
8. The standard rule based evaluation method of claim 1, wherein: when the matching is performed in step S3, when the data only includes an attribute value corresponding to a judgment attribute name, and when the data matches the atomic rule, directly judging whether the attribute value corresponding to the judgment attribute name included in the data satisfies the judgment condition corresponding to the operator and the critical value in the atomic rule; when the data is matched with the composite rule, obtaining the matching results of the data and all atomic rules under the composite rule, judging whether all the matching results meet the logical relationship corresponding to the composite rule, if so, judging that the matching can be performed, otherwise, judging that the matching cannot be performed; and when the data comprises at least two attribute values corresponding to the judgment attribute names, matching is respectively carried out aiming at each attribute value.
9. An evaluation terminal device based on standard rules, characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115858319A (en) * | 2022-12-09 | 2023-03-28 | 中电金信软件有限公司 | Stream data processing method and device |
| CN116012123A (en) * | 2023-03-27 | 2023-04-25 | 湖南三湘银行股份有限公司 | Wind control rule engine method and system based on Rete algorithm |
| CN117708104A (en) * | 2023-11-21 | 2024-03-15 | 深圳计算科学研究院 | Automated entity splitting method, device, equipment and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049516A (en) * | 2012-12-14 | 2013-04-17 | 北京神州绿盟信息安全科技股份有限公司 | Method and device for processing data |
| CN106933889A (en) * | 2015-12-31 | 2017-07-07 | 华为技术有限公司 | For regular collocation method, display methods and the client screened |
| CN109102142A (en) * | 2018-06-15 | 2018-12-28 | 山东鲁能软件技术有限公司 | A kind of personnel evaluation methods and system based on evaluation criterion tree |
-
2022
- 2022-04-02 CN CN202210344327.5A patent/CN114678130A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049516A (en) * | 2012-12-14 | 2013-04-17 | 北京神州绿盟信息安全科技股份有限公司 | Method and device for processing data |
| CN106933889A (en) * | 2015-12-31 | 2017-07-07 | 华为技术有限公司 | For regular collocation method, display methods and the client screened |
| CN109102142A (en) * | 2018-06-15 | 2018-12-28 | 山东鲁能软件技术有限公司 | A kind of personnel evaluation methods and system based on evaluation criterion tree |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115858319A (en) * | 2022-12-09 | 2023-03-28 | 中电金信软件有限公司 | Stream data processing method and device |
| CN115858319B (en) * | 2022-12-09 | 2023-11-28 | 中电金信软件有限公司 | Stream data processing method and device |
| CN116012123A (en) * | 2023-03-27 | 2023-04-25 | 湖南三湘银行股份有限公司 | Wind control rule engine method and system based on Rete algorithm |
| CN117708104A (en) * | 2023-11-21 | 2024-03-15 | 深圳计算科学研究院 | Automated entity splitting method, device, equipment and medium |
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