CN113574482A - Rule checking method and device and computer equipment - Google Patents
Rule checking method and device and computer equipment Download PDFInfo
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Abstract
A rule checking method and device and a computer device (600). The method comprises the following steps: obtaining target historical state data and target historical operation data (101); the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object; determining a probability based on the target historical state data and the target historical operation data, the probability being a probability that rule data meets a preset condition (102); wherein the rule data includes: an operation trigger condition parameter and an operation set.
Description
The application relates to the technical field of Internet of things, in particular to a rule checking method and device and computer equipment.
The scene rules of the internet of things are mainly created in a system pre-defined mode or a manual mode of a user. The predefined creation mode of the system is generally that system maintenance personnel check whether the scene rule can work normally; the manual creation mode of the user is generally that the user checks whether the scene rule can work normally. Such checking depends on how well the system and environment are known to the inspector, and may result in situations where the scenario rule description is incomplete or inaccurate.
Disclosure of Invention
In order to solve the existing technical problem, embodiments of the present application provide a rule checking method and apparatus, and a computer device.
In order to achieve the above purpose, the technical solution of the embodiment of the present application is implemented as follows:
the rule checking method provided by the embodiment of the application comprises the following steps: obtaining target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;
determining a probability based on the target historical state data and the target historical operation data, wherein the probability is the probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
The rule verifying device provided by the embodiment of the application comprises: an acquisition unit and a first determination unit; wherein the content of the first and second substances,
the acquisition unit is configured to acquire target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;
the first determining unit is configured to determine a probability based on the target historical state data and the target historical operation data, wherein the probability is a probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
The computer device provided by the embodiment of the application comprises a processor and a memory. The memory is used for storing a computer program, and the processor is used for calling and running the computer program stored in the memory and executing the rule checking method.
The chip provided by the embodiment of the application is used for realizing the rule checking method.
Specifically, the chip includes: and the processor is used for calling and running the computer program from the memory so that the equipment provided with the chip executes the rule checking method.
The computer-readable storage medium provided in the embodiments of the present application is used for storing a computer program, and the computer program enables a computer to execute the rule checking method described above.
The computer program product provided by the embodiment of the present application includes computer program instructions, and the computer program instructions enable a computer to execute the rule checking method.
The computer program provided by the embodiment of the present application, when running on a computer, causes the computer to execute the rule checking method described above.
According to the technical scheme, the probability that the rule data meet the preset conditions is determined through the target historical state data and the target historical operation data, whether the rule data are in compliance is automatically checked, human resources are greatly saved, the rule checking efficiency is improved, on the other hand, the condition of manual misjudgment is avoided, and the rule checking accuracy is greatly improved.
Fig. 1 is a schematic flowchart of a rule checking method according to an embodiment of the present application;
fig. 2 is a first schematic flow chart of an alternative rule checking method according to an embodiment of the present disclosure;
fig. 3 is a schematic view illustrating an alternative flow chart of a rule checking method according to an embodiment of the present application;
fig. 4 is a schematic view illustrating an alternative flow chart of a rule checking method according to an embodiment of the present application;
fig. 5 is a first schematic structural diagram of a rule checking device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a component of a rule checking device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a third component of the rule checking apparatus according to the embodiment of the present application;
FIG. 8 is a schematic block diagram of a computer device according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of a chip according to an embodiment of the present application.
Technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical scheme of the embodiment of the application can be applied to the Internet of things system. The Internet of things system can comprise a cloud platform server, gateway equipment and various Internet of things equipment. The cloud platform server may be in communication with the gateway device. Optionally, for a certain area range, for example, a home range, corresponding gateway devices may be respectively provided. The gateway equipment can communicate with the Internet of things equipment in the corresponding area range through the local area network.
Optionally, the cloud platform server may communicate with the internet of things device through various mobile communication systems. Mobile communication systems, for example: a Global System for Mobile communications (GSM) System, a Code Division Multiple Access (CDMA) System, a Wideband Code Division Multiple Access (WCDMA) System, a General Packet Radio Service (GPRS), a Long Term Evolution (Long Term Evolution, LTE) System, an LTE Frequency Division Duplex (FDD) System, an LTE Time Division Duplex (TDD), a Universal Mobile Telecommunications System (UMTS), a Worldwide Interoperability for Microwave Access (WiMAX) communication System, or a 5G System.
Before explaining the embodiments of the present application in detail, the terms related to the embodiments of the present application will be explained first.
And in the Internet of things system, the scene is that a specific device automation and device linkage are established by using an intelligent home system comprising devices, a network, a platform and applications so as to realize specific applications and services. For example:
the service description may be: at 10 am on weekends, Sara woke up from nightmare and opens half of the novels that read to continue reading after washing. As the sun rises, the indoor temperature becomes higher. After a while, the air conditioner is automatically turned on and set to a cooling mode.
The functional description may be: the electrical appliances such as the fresh air system, the heater, the air conditioner, the humidifier, the air purifier and the like are automatically switched on and off and controlled according to air data so as to ensure comfortable indoor environment.
The scene preconditions may be: the user (or system) first sets the desired ambient temperature, humidity, PM2.5 content, formaldehyde content range.
The triggers for a scenario may be: the temperature and humidity sensor, the PM2.5 sensor and the formaldehyde sensor acquire environmental data at regular time and transmit the environmental data to the gateway of the Internet of things.
And (3) the gateway equipment judges whether the current environment state is comfortable, namely whether the temperature and humidity, PM2.5 and formaldehyde content are in a specified range, and if the current environment state is out of the range, a specified electric appliance is started and set: if the temperature is higher than the limit value, starting the air conditioner and setting the air conditioner to be in a refrigeration mode; if the temperature is lower than the limit value, starting the heater; if the humidity is higher than the limit value, starting the air conditioner and setting the air conditioner to be in a dehumidification mode; if the humidity is lower than the limit value, starting the humidifier; if the PM2.5 or the formaldehyde content is higher than the limit value, starting a fresh air or air purifier; if the environment is comfortable, namely the environmental indexes are all within the limit value range, the related electric appliances are closed.
It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The embodiment of the invention provides a rule checking method. Fig. 1 is a schematic flowchart of a rule checking method according to an embodiment of the present application; as shown in fig. 1, the method includes:
step 101: obtaining target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;
step 102: determining a probability based on the target historical state data and the target historical operation data, wherein the probability is the probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
The rule checking method of the embodiment can be applied to a cloud platform server, and certainly can also be applied to other computer equipment.
In this embodiment, the state data (assumed to be statusttx) at a certain time in a specific range (for example, in a home area) can be described by means of the object attribute value set. For example:
StatusT1：{Device1.Attr1：Value1，Device1.Attr2：Value2，Device2.Attr3：Value3,Device3.Attr4：Value4,…}。
wherein, the Device represents the equipment object (such as a certain air conditioner in the range of the family area) in the specific range; attr represents some property of the device object (e.g., current temperature); value denotes the attribute Value of the device object (attribute Value contains units, e.g., 25 ℃).
When all parameters (including device objects, attributes, and attribute values) in StatusT1 and StatusT2 are the same, it may be determined that StatusT1 is StatusT 2. When either of the parameters Statust1 and Statust2 (including device object, attribute, or device attribute values) are different, then Statust 1! StatusT 2.
In this embodiment, the operation on the device object (assumed to be Action) may be described in a manner of object operation and parameter. For example:
Action1:Device1.Action1(Param1,Param2,…)。
wherein, the Device represents the equipment object (such as a certain air conditioner in the range of the family area) in the specific range; an Action represents some operation of a device object (e.g., setting a temperature); param denotes the parameter value (e.g. 20 c).
In this embodiment, the StatusTx in each specific range (for example, in each home area) may be recorded in the cloud platform server to form the historical status data. As an embodiment, a record may be saved when a state change occurs, i.e. the changed state data is saved. In practical application, the internet of things equipment sends own state data to the gateway equipment, and the gateway equipment reports the state data to the cloud platform server after acquiring the state data of at least part of the internet of things equipment in a corresponding specific range; the cloud platform server records the state data; subsequently, further receiving the state data reported by the gateway equipment, and comparing the state data with the previously recorded state data; if the subsequently received state data is different from the previously recorded state data, recording the subsequently received state data, and taking the subsequently received state data and the previously recorded state data together as historical state data; and if the subsequently received state data is the same as the previously recorded state data, deleting the subsequently received state data. As another embodiment, the recording is done periodically, for example once per minute. In practical application, the internet of things equipment sends own state data to the gateway equipment, and the gateway equipment reports the state data to the cloud platform server after acquiring the state data of at least part of the internet of things equipment in a corresponding specific range; the cloud platform server may record according to a preconfigured recording period, for example, record one state data per minute.
Correspondingly, the present embodiment may also record the operation of the device and the state after the operation in the cloud platform server to form historical operation data.
Based on this, in this embodiment, the target historical state data includes historical data of an attribute corresponding to an equipment object, and the historical data of the attribute corresponding to the equipment object may include an attribute corresponding to the equipment object and a corresponding attribute value; correspondingly, the target historical operation data includes historical data of an operation corresponding to the device object, and the historical data of the operation corresponding to the device object may include an operation corresponding to the device object and a corresponding operation parameter.
In this embodiment, the obtained target historical state data and target historical operation data may be historical state data and historical operation data in the cloud platform server, or may also be partial historical state data and partial historical operation data in the cloud platform server.
As an embodiment, the obtaining target historical state data and target historical operation data includes: and acquiring historical state data and historical operation data recorded in a cloud platform server, wherein the historical state data is used as the target historical state data, and the historical operation data is used as the target historical operation data.
In this embodiment, all historical state data and all historical operation data recorded in the cloud platform server may be used as the target historical state data and the target historical operation data, so as to determine the probability that the rule data satisfies the preset condition. All the historical state data and all the historical operation data recorded in the cloud platform server are historical state data and historical operation data corresponding to a plurality of specific ranges, and are not limited to historical state data and historical operation data corresponding to a certain specific range (for example, a specific range corresponding to the rule data (for example, a family corresponding to the rule data)).
As another embodiment, the obtaining target historical state data and target historical operation data includes: acquiring historical state data and historical operation data recorded in a cloud platform server, selecting partial state data from the historical state data as target historical state data, and selecting partial operation data from the historical operation data as the target historical operation data; the number and the type of the equipment objects corresponding to the selected part of the state data and the selected part of the operation data are the same as those of the equipment objects contained in the specific range; or the types of the equipment objects corresponding to the selected part of state data and the selected part of operation data are partially the same as the types of the equipment objects contained in the specific range; or the types of the device objects corresponding to the selected partial state data and the selected partial operation data are the same as the types of the device objects contained in the specific range, and the number of the device objects corresponding to the selected partial state data and the selected partial operation data is different from the number of the device objects contained in the specific range.
In this embodiment, when the data volume of all the historical state data and the historical operation data recorded by the cloud platform server is too large, the execution difficulty of determining the probability that the rule data meets the preset condition by using the target historical state data and the target historical operation data with large data volume is also too large, so that part of the historical state data and part of the historical operation data need to be selected from all the historical state data and the historical operation data recorded by the cloud platform server as the target historical state data and the target historical operation data.
As a first implementation manner, the type and the number of the corresponding device objects in the rule data can be determined, and the historical state data and the historical operation data which are matched with the type and the number of the device objects corresponding to the rule data are selected from all the historical state data and the historical operation data recorded by the cloud platform server and serve as the target historical state data and the target historical operation data.
As a second implementation manner, the type and the number of the corresponding device objects in the rule data may be determined, and historical state data and historical operation data, of which the types of the device objects are matched and the number is not matched, are selected from all historical state data and historical operation data recorded by the cloud platform server as target historical state data and target historical operation data. The device object types corresponding to the selected target historical state data and target historical operation data are the same as the device object types corresponding to the rule data, and the number of the device object types can be different, but at least one device is required to be corresponding to each device object type.
As a third implementation manner, the type and the number of the corresponding device objects in the rule data may be determined, and historical state data and historical operation data partially matching the device object type corresponding to the rule data are selected from all historical state data and historical operation data recorded by the cloud platform server as target historical state data and target historical operation data.
In this embodiment, the following abstract manner may be used to describe a scene and Rule data corresponding to the scene (the Rule data is represented by Rule, for example):
Rule:(Trigger,ActionSet,Output)。
wherein Trigger represents a Trigger condition of a scene action described with a relational expression of object state values (e.g., equal to (═), not equal to (| >), greater than (>), less than (<), greater than or equal to (>), less than (< ═), etc.) and a logical expression (e.g., with (&), or (|), not (|), etc.), for example, (((device1. attrr 1 ═ Value1) & (device2.attr2> Value2)) | (device3.attr3 ═ Value 2));
the ActionSet represents a scene action set described by adopting a set of object operations and parameters. For example: { device1.action1(Param1, Param2, …), device2.action2(Param3, Param4, …), … };
here, Output indicates an Output result generated after the scene action is performed (the content of the Output result is a set of state changes of each device object). For example: { Device1.Attr 1: value1, device2.attr 2: value2, … }. Optionally, the data rule may be set as an estimated output when being created, and if the estimated output cannot be estimated, the result may be marked as null.
In this embodiment, one or more Rule data may constitute a Rule data set, which may be denoted as RuleSet, which may be denoted as { Rule1, Rule2, Rule3, … }, for example.
In this embodiment, a Scene may be implemented by one or more rule data, for example, Scene may be represented as: scene: { Rule1, Rule2, Rule3, … }, which implement specific applications and services under specific scenarios through the execution of one or more Rule data.
For example, the temperature and humidity related part in the foregoing scenario example can be described by the following rule data:
Rule1:(((tempsensor.temperature>＝28℃)&&((！airconditioner.isopen)||(！airconditioner.iscold))),{airconditioner.turn(on),airconditioner.setmode(cold)},null)；
Rule2:(((tempsensor.temperature<＝18℃)&&(！heater.isopen)),{heater.turn(on)},null)；
Rule3:(((tempsensor.temperature>18℃)&&(heater.isopen)),{heater.turn(off)},null)；
Rule4:(((tempsensor.temperature<28℃)&&(humiditysensor.humidity>＝80％rh)&&((！airconditioner.isopen)||(！airconditioner.isdehumidification))),{airconditioner.turn(open),airconditioner.setmode(dehumidification)},null)；
Rule5:(((tempsensor.temperature<28℃)&&(humiditysensor.humidity<80％rh)&&(airconditioner.isopen)),{airconditioner.turn(close)},null)。
as can be seen from the above examples, the rule data may include operation trigger condition parameters (i.e., trigger conditions for scene actions described by relational expressions and logical expressions) and operation sets; optionally, the rule data may also include state result data characterizing a change in state of the device object.
However, whether the rule data is created in a manner predefined by the system or created manually by the user, situations may arise where the description of the rule data is incomplete or inaccurate, and thus the created rule data needs to be checked. Based on this, in this embodiment, the rule data is verified based on the target historical state data and the target historical operation data by obtaining the target historical state data and the target historical operation data, and the probability that the rule data meets the preset condition is determined.
In this embodiment, the determining the probability based on the target historical state data and the target historical operation data includes at least one of:
determining a first probability based on the target historical state data and the target historical operational data, the first probability being a probability that first rule data may not be executed;
determining a second probability based on the target historical state data and the target historical operation data, the second probability being a probability of an operation execution conflict between at least two second rule data; the at least two second rule data are rule data for the same equipment object;
determining a third probability based on the target historical state data and the target historical operation data, the third probability being a probability of at least one third rule data loop execution.
Wherein the first rule data, the second rule data, and the third rule data are all arbitrary rule data.
For the situation that the rule data cannot be executed, for example:
rule1: (temperature. temperature > 28 ℃), { air adjuster. setmode (cold) }, null); i.e. the rule data cannot be executed without the air conditioner being turned on.
For the case of multiple rule data conflicts, for example:
Rule1:((tempsensor.temperature>＝28℃),{airconditioner.turn(on),airconditioner.setmode(cold)},null)；
Rule2:((humiditysensor.humidity>＝80％rh),{airconditioner.turn(on), airconditioner.setmode(dehumidification)},null)。
execution conflicts arise in the set mode operation of the air conditioner within the above two rule data in the case where the temperature is greater than 28 ℃ and the humidity is greater than 80% rh.
For the case of regular loop execution, for example:
Rule1(Ta,Aa,Oa),Rule2(Tb,Ab,Ob),Rule3(Tc,Ac,Oc),
when Tb, Tc, and Ta are Oa, three rule loops are executed.
The rule checking method of the present embodiment is described in detail below with respect to the above three cases.
Situation one
For the case that rule data cannot be executed, then the determining a first probability based on the target historical state data and the target historical operation data comprises: obtaining a first portion of historical state data from the target historical state data; the first part of historical state data is state data which meets a first operation triggering condition parameter in the first rule data in the target historical state data; querying the target historical operation data to obtain a first record, and determining a first probability based on the first record; wherein the first record is operation data corresponding to first historical state data in the target historical operation data, and executing a first operation in a first operation set in the first rule data; the first historical state data is any one of the first part of historical state data; the first operation is any operation in the first set of operations.
In this embodiment, for each state data in the target historical state data, screening is performed according to a first operation triggering condition parameter (i.e., Trigger in the first rule data) in the first rule data, so as to obtain a first part of historical state data that meets the first operation triggering condition parameter in the first rule data.
Taking the first Rule data as Rule1: ((temperature: 28 ℃)) & (| | air conditioner. isopen) |), { air conditioner. turn (on), air conditioner. section code (null) as an example, if the air conditioner temperature in the target historical state data is 29 ℃, it indicates that the target historical state data satisfies the first operation triggering condition parameter in the first Rule data; if the air conditioner temperature in the target historical state data is 26 ℃, the target historical state data is indicated to be not satisfied with the first operation triggering condition parameter in the first rule data, the target historical state data which is not satisfied with the first operation triggering condition parameter in the first rule data needs to be deleted, the target historical state data which is satisfied with the first operation triggering condition parameter in the first rule data is reserved, and the first part of historical state data is generated.
Further traversing each state data in the first part of historical state data, and inquiring whether a first record corresponding to each first historical state data and executing a first operation in a first operation set in the first rule data exists in the target historical operation data; a first probability that the first rule data may not be executed is determined based on the first record.
In an optional embodiment of the subject application, the determining a first probability based on the first record comprises: respectively obtaining the number of successful records and the number of failed records based on the first record, and determining the first probability based on the number of successful records and the number of failed records.
Fig. 2 is a schematic view illustrating an optional flow chart of a rule checking method according to an embodiment of the present application; as shown in fig. 2, the verification process of the present scenario may include:
step 201: traversing each different state data (StatusTx) in the target historical state data;
step 202: judging whether traversal is finished; if yes, go to step 207; if the judgment result is no, executing step 203;
step 203: corresponding to the state data (StatusTx), judging whether the state data (StatusTx) meets a first operation triggering condition parameter (Trigger) in the first rule data; if yes, go to step 204; if the judgment result is negative, re-executing the step 201;
step 204: querying in target historical operation data whether a first operation (ActionX) in a first set of operations (ActionSet) in the first rule data at StatusTx executed successfully; if yes, go to step 205: adding one to the number of successful records of the first rule data; when the determined result is that there is an execution failure record, executing step 206: adding one to the number of the failure records of the first rule data, and further executing the step 201 again; if the determination result is that the execution record of the first operation (ActionX) in the first operation set (ActionSet) in the first rule data does not exist in the target historical operation data at statusttx, re-executing step 201;
step 207: a first probability that the first rule data may not be executed is determined.
In this embodiment, the first probability may be determined by the number of successful records of the first rule data being greater than the number of failed records of the first rule data; alternatively, the sum of the number of successful records and the number of failed records of the first rule data may be obtained first, and the first probability may be determined by using the ratio of the number of successful records to the sum of the number of failed records.
Situation two
Determining a second probability based on the target historical state data and the target historical operation data for a case where operations between a plurality of second rule data perform a conflict, comprising: determining a second operation triggering condition parameter, and obtaining a second part of historical state data from the target historical state data based on the second operation triggering condition parameter; the second part of historical state data is state data which meets the second operation triggering condition parameter in the target historical state data; the second operation trigger condition parameter is an operation trigger condition parameter for the same device object included in the at least two pieces of second rule data; determining a second operation set, judging whether a second operation in the second operation set has a mutual exclusion relationship with other operations, and obtaining a first result; the second set of operations comprises operations in the set of operations for the same device object in the at least two second rule data; the second operation is any operation in the second operation set; the other operation is other operation in the second operation set except the second operation; under the condition that the first result is that the second operation in the second operation set does not have a mutual exclusion relationship with other operations, querying the target historical operation data to obtain a second record; the second record is operation data which corresponds to second historical state data and executes the second operation in the target historical operation data; the second historical state data is any historical state data in the second part of historical state data; determining the second probability based on the first result and the second record.
In the present embodiment, for determination of an operation execution conflict between a plurality of second rule data, it is first determined that the plurality of second rule data are rule data for the same device object. Further for one second rule data, whether the operation in the operation set in the second rule data conflicts with the operation in the operation sets in other second rule data is judged. For example:
Rule1:(Trigger1,{Device1.Action1(Param1,Param2,…),Device2.Action2(Param3，Param4，…),…},Output1)；
Rule2:(Trigger2,{Device1.Action3(Param5，Param6，…)，Device2.Action4(Param7，Param8，…)；Device3.Action5(Param9，Param10，…),…},Output2)；
Rule3:(Trigger3,{Device3.Action6(Param11,Param12,…),…},Output3),
then, for the Rule1, Rule2 and Rule3, the conflict judgment of the operation execution of the Device1 and the Device2 in the set { Rule1, Rule2} and the conflict judgment of the operation execution of the Device3 in the set { Rule2, Rule3} are needed.
In this embodiment, first, a logical and operation is performed on the operation triggering condition parameters (i.e., Trigger in the rule) in the at least two pieces of second rule data to obtain second operation triggering condition parameters; and performing union operation on the operation sets in the at least two second rule data to obtain a second operation set.
Based on this, in an optional embodiment of the present application, the determining the second set of operations includes: performing union processing on operations in the operation sets aiming at the same equipment object in the at least two pieces of second rule data to obtain a second operation set; the determining a second operation triggering condition parameter includes: and performing logic and processing on the operation triggering condition parameters aiming at the same equipment object in the at least two pieces of second rule data to obtain second operation triggering condition parameters.
In the embodiment, first, a second operation triggering condition parameter obtained through logic and processing is used for screening state data in target historical state data based on the second operation triggering condition parameter, and second part of historical state data matched with the second operation triggering condition parameter is obtained; performing mutual exclusion judgment through operations included in the second operation set; in the case that the exclusive operation is not included in the second operation set, a second record corresponding to second historical state data and used for executing the second operation is inquired further based on target historical operation data; a second probability of an operation execution conflict between at least two second rule data is determined based on the result.
In an optional embodiment of the present application, the determining a second probability based on the first result and the second record comprises: obtaining a first number of second operations having a mutual exclusion relationship when the first result is that the second operations in the second operation set have a mutual exclusion relationship with other operations; respectively obtaining the number of successful records and the number of failed records based on the second record; determining the second probability based on the first number, the number of successful records, and the number of failed records.
In an optional embodiment of the present application, the determining whether a second operation in the second operation set has a mutual exclusion relationship with other operations to obtain a first result includes: judging whether a second operation in the second operation set and other operations are the operations with the same operation type and different operation parameters to obtain a first result; determining that a second operation in the second operation set has a mutually exclusive relationship with other operations when the first result is that the second operation and the other operations are of the same operation type and have different operation parameters; and determining that the second operation and other operations in the second operation set do not have a mutually exclusive relationship when the first result is that the second operation and other operations in the second operation set are not of the same operation type and have different operation parameters.
For example, if the device object targeted is an air conditioner, if the operation types of the two second operations are both mode setting operations, one of the second operations has an operation parameter of 26 ℃ and the other second operation has an operation parameter of 18 ℃, it may indicate that the two second operations have a mutually exclusive relationship.
Exemplarily, fig. 3 is a schematic view illustrating an optional flow chart of a rule checking method provided in the embodiment of the present application; as shown in fig. 3, the verification process of the present scenario may include:
step 301: at least two second rule data corresponding to the same device object (DeviceX) among the plurality of rule data are acquired, and a rule data set (RuleSetX) is generated.
Step 302: performing union operation on operations in an operation set (ActionSet) corresponding to the same device object (DeviceX) in all second rule data in the rule data set (RuleSetX) to obtain a second operation set (ActionSetX);
step 303: performing logic and processing on operation triggering condition parameters (Trigger) of all second rule data in the rule data set corresponding to the same equipment object to obtain second operation triggering condition parameters (Trigger);
step 304: traversing each different state data (StatusTx) in the target historical state data;
step 305: judging whether traversal is finished; if yes, go to step 311; if the judgment result is negative, executing step 306;
step 306: corresponding to the state data (statusttx), judging whether the state data (statusttx) meets a second operation triggering condition parameter (Trigger) in the second rule data; if yes, go to step 307; if the result of the determination is negative, re-executing step 304;
step 307: judging whether a second operation in the second operation set has a mutual exclusion relationship with other operations; if yes, go to step 308: adding one to the number of conflict records of the second operation set, and re-executing the step 304; when the judgment result is no, go to step 309;
step 309: querying in target historical operation data whether operations in a second set of operations (ActionSetX) in the second rule data at statusttx were successfully executed; if the result of the determination is that there is a successful record, execute step 310: -adding one to the number of successful records of the second set of operations (ActionSetX); if the determined result is that there is a failed execution record, step 308 is executed, and step 304 is further executed again: if the determination result is that the execution record of the operations in the second operation set (ActionSetX) in the second rule data does not exist in the target historical operation data at statusttx, re-executing step 304;
step 311: a second probability that an operation between at least two second rule data performs a conflict is determined.
In this embodiment, the second probability may be determined by the number of successful records to the number of failed records corresponding to certain second rule data; alternatively, the sum of the number of successful records and the number of failed records corresponding to certain second rule data may be obtained first, and the second probability may be determined by the ratio of the number of successful records (or the number of failed records) to the sum of the number of successful records and the number of failed records.
Situation three
For the case of a third rule data loop execution, said determining a third probability based on said target historical state data and said target historical operation data comprises: determining target data in the target historical state data, and simulating and executing the third rule data according to the target data; determining a third probability of execution of the third rule data loop as a function of a second number of simulated executions of the third rule data and a third number of state data in the target historical state data.
In this embodiment, whether cyclic execution may occur in rule data corresponding to a specific range (for example, a home area range) is determined by a simulation execution mode. Specifically, the initial state data in the third rule data is changed according to each state data in the target historical state data, and the third rule data is simulated and executed; estimating a simulation execution result according to the target historical operation data; a third probability of whether the third rule data is likely to be executed in a loop is determined by determining a first number of simulated executions of the third rule data and a second number of target data in the target historical state data.
First, target data for simulation execution is determined from the target historical state data.
In an optional embodiment of the present application, the determining target data in the target historical state data includes: traversing the state data in the target historical state data, and traversing the third rule data in the at least one third rule data under the condition that the state data is not completely traversed; under the condition that the third rule data is not traversed completely, judging whether the currently traversed state data meets the condition corresponding to the currently traversed third rule data; wherein the condition corresponding to the third rule data meeting the current traversal includes at least one of: satisfying a third operation trigger condition parameter in the third rule data, satisfying a time parameter in a third action set in the third rule data; under the condition that the currently traversed state data meet the condition corresponding to the currently traversed third rule data, judging whether the currently traversed third rule data have simulated execution records corresponding to the currently traversed state data; under the condition that the currently traversed state data are judged not to meet the condition corresponding to the currently traversed third rule data, re-traversing the state data in the target historical state data; and under the condition that no simulated execution record exists, determining the currently traversed state data as the target data.
In this embodiment, the determination is performed for each state data in the target historical state data, and the determination is performed for all the third rule data corresponding to a specific range (for example, a home area range), that is, each state data in the target historical state data is traversed in a traversal manner; and under the condition that the state data is not traversed completely, traversing third rule data in the plurality of third rule data, and further judging whether the currently traversed state data meets the condition corresponding to the currently traversed third rule data. As an implementation manner, it is determined whether the currently traversed state parameter meets a third operation triggering condition parameter (i.e., Trigger in the third rule data) in the currently traversed third rule data.
Optionally, if the third operation Trigger condition parameter (Trigger) in the currently traversed third rule data includes a timing condition (for example, the third rule data includes a timer, current time ═ 9), the state data at the specific time (for example, 9:00) needs to be selected, that is, the currently traversed state parameter needs to match the timing condition included in the third operation Trigger condition parameter (Trigger), so as to improve the accuracy of the determination.
Optionally, if the third action set (ActionSet) in the currently traversed third rule data includes a timing operation (for example, the third rule data includes a timer, which is 9min), the state data of the specific time (for example, after 9 minutes of the time corresponding to the certain state data) also needs to be selected, that is, the currently traversed state parameter needs to match the timing operation included in the third action set (ActionSet), so as to improve the accuracy of the determination.
In an optional embodiment of the present application, the simulating execution of the third rule data according to the target data in the target historical state data includes: simulating and executing a third operation in a third operation set corresponding to the target data in the third rule data, and recording the target data as simulated and executed; determining output status data simulating execution of the third rule data based on the target historical operation data.
In this embodiment, operations in the third operation set in the third rule data are simulated and executed corresponding to a certain target data, and the simulated execution of the target data is recorded. For example:
after the simulation executes the rule data { air conditioner. turn (on) }, air conditioner. setmode (cold) }, an output { air conditioner. oneff: on, air conditioner. mode: cold, … } is generated, which is estimated based on the target historical operation data.
In this embodiment, if the third rule data corresponds to the same target data, the third rule data is simulated again, and the first number of simulated executions corresponding to the target data is increased by one. It is to be understood that the third rule data loop in the present embodiment may be executed by a single third rule data loop, or may be executed by a plurality of third rule data loops. For example, the result output after the simulation execution of rule1 jumps to rule2 for simulation execution; if rule2 has been previously simulated, then the first number is incremented by one.
Exemplarily, fig. 4 is a schematic view illustrating an optional flow chart of a rule checking method provided in an embodiment of the present application; as shown in fig. 4, the verification process of the present scenario may include:
step 401: traversing each different state data (StatusTx) in the target historical state data as initial state data (StatusTn);
step 402: judging whether traversal is finished; if yes, go to step 412; if the judgment result is no, executing step 403;
step 403: traversing each of all third rule data (RuleX);
step 404: judging whether traversal is finished; if yes, go to step 411: adding one to the second amount; if the determination result is negative, go to step 405;
step 405: corresponding to the initial state data (StatusTn), judging whether the initial state data (StatusTn) meets a third operation triggering condition parameter (Trigger) in third rule data (Rulex); if yes, go to step 406; if the judgment result is negative, re-executing the step 401;
step 406: judging whether the third rule data has simulation execution records when in initial state data (StatusTn); if yes, go to step 410: the first amount plus one; if the result of the determination is negative, go to step 407;
step 407: simulating to execute the operation in the third operation set in the third rule data based on the initial state data (StatusTn), and recording the initial state data (StatusTn) as being simulated to execute;
step 408: estimating output state data after simulation execution according to target historical operation data;
step 409: re-executing step 403 with the output status data as initial status data;
step 412: a third probability is determined based on the first number and the second number.
In an optional embodiment of the present application, the method further comprises: and determining the probability that the rule data meet the preset conditions according to a deep learning model.
In this embodiment, the deep learning model may be trained in advance, so as to determine, through the deep learning model, whether the rule data satisfies the probability of the preset condition, so as to improve the accuracy of the determination. In practical application, different deep learning models can be trained respectively according to a first probability that the first rule data cannot be executed, a second probability that operation execution conflicts among a plurality of second rule data and a third probability that the third rule data is executed circularly, and the first probability, the second probability and the third probability corresponding to the rule data are respectively determined based on the different deep learning models.
In an optional embodiment of the present application, the method further comprises: under the condition that the probability that the rule data meets the preset condition reaches a preset threshold value, outputting prompt information, wherein the prompt information can comprise at least one of the following: the rule data processing method comprises the steps that first prompt information corresponding to the rule data inexecutable, second prompt information corresponding to rule data conflict and third prompt information corresponding to rule data circular execution are output, so that an inspector is prompted how to modify the rule data to avoid rule data failure or inaccuracy through the output prompt information, and user experience is improved.
By adopting the technical scheme of the embodiment of the application, on one hand, the probability that the rule data meet the preset conditions is determined through the target historical state data and the target historical operation data, so that whether the rule data are in compliance is automatically verified, the rule data are not limited by the understanding degree of an inspector on the system and the environment, the manpower resources are greatly saved, the rule verification efficiency is improved, on the other hand, the condition of manual misjudgment is also avoided, the rule verification accuracy is greatly improved, and on the other hand, the user experience is also improved.
The embodiment of the invention also provides a rule checking device. Fig. 5 is a first schematic structural diagram of a rule checking device according to an embodiment of the present disclosure; as shown in fig. 5, the apparatus includes: an acquisition unit 41 and a first determination unit 42; wherein the content of the first and second substances,
the acquiring unit 41 configured to acquire target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;
the first determining unit 42 is configured to determine a probability based on the target historical state data and the target historical operation data, wherein the probability is a probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
As a first implementation, as shown in fig. 6, the first determining unit 42 includes a first determining module 421 configured to determine a first probability based on the target historical state data and the target historical operation data, the first probability being a probability that the first rule data cannot be executed.
In an optional embodiment of the present application, the first determining module 421 is configured to obtain a first part of historical state data from the target historical state data; the first part of historical state data is state data which meets a first operation triggering condition parameter in the first rule data in the target historical state data; querying the target historical operation data to obtain a first record, and determining the first probability based on the first record; wherein the first record is operation data corresponding to first historical state data in the target historical operation data, and executing a first operation in a first operation set in the first rule data; the first historical state data is any one of the first part of historical state data; the first operation is any operation in the first set of operations.
In an optional embodiment of the present application, the first determining module 421 is configured to obtain a number of successful records and a number of failed records based on the first record, and determine the first probability based on the number of successful records and the number of failed records.
As a second embodiment, as shown in fig. 6, the determining unit includes a second determining module 422 configured to determine a second probability that an operation execution conflict between at least two second rule data is a probability based on the target historical state data and the target historical operation data; the at least two second rule data are rule data for the same device object.
In an optional embodiment of the present application, the second determining module 422 is configured to determine a second operation triggering condition parameter, and obtain a second part of historical state data from the target historical state data based on the second operation triggering condition parameter; the second part of historical state data is state data which meets the second operation triggering condition parameter in the target historical state data; the second operation trigger condition parameter is an operation trigger condition parameter for the same device object included in the at least two pieces of second rule data; determining a second operation set, judging whether a second operation in the second operation set has a mutual exclusion relationship with other operations, and obtaining a first result; the second set of operations comprises operations in the set of operations for the same device object in the at least two second rule data; the second operation is any operation in the second operation set; the other operation is other operation in the second operation set except the second operation; under the condition that the first result is that the second operation in the second operation set does not have a mutual exclusion relationship with other operations, querying the target historical operation data to obtain a second record; the second record is an operation record which corresponds to second historical state data and executes the second operation in the target historical operation data; determining the second probability based on the first result and the second record.
In an optional embodiment of the present application, the second determining module 422 is configured to, in a case that the first result is that a second operation in the second operation set has a mutually exclusive relationship with other operations, obtain a first number of second operations having a mutually exclusive relationship; respectively obtaining the number of successful records and the number of failed records based on the second record; determining the second probability based on the first number, the number of successful records, and the number of failed records.
In an optional embodiment of the present application, the second determining module 422 is configured to determine whether a second operation in the second operation set and other operations are operations with the same operation type and different operation parameters, and obtain a first result; determining that a second operation in the second operation set has a mutually exclusive relationship with other operations when the first result is that the second operation and the other operations are of the same operation type and have different operation parameters; and determining that the second operation and other operations in the second operation set do not have a mutually exclusive relationship when the first result is that the second operation and other operations in the second operation set are not of the same operation type and have different operation parameters.
In an optional embodiment of the present application, the second determining module 422 is configured to perform union processing on operations in the operation sets for the same device object in the at least two pieces of second rule data to obtain a second operation set; and the second rule data is also configured to logically and process the operation triggering condition parameters aiming at the same equipment object in the at least two pieces of second rule data to obtain second operation triggering condition parameters.
As a third embodiment, as shown in fig. 6, the determining unit includes a third determining module 423 configured to determine a third probability based on the target historical state data and the target historical operation data, the third probability being a probability of at least one third rule data loop being executed.
In an optional embodiment of the present application, the third determining module 423 is configured to determine target data in the target historical state data, and simulate to execute the third rule data according to the target data; determining the third probability in dependence on a second amount that the third rule data is executed in simulation and a third amount of state data in the target historical state data.
In an optional embodiment of the present application, the third determining module 423 is further configured to traverse the state data in the target historical state data, and traverse the third rule data in the at least one third rule data if the state data is not completely traversed; under the condition that the third rule data is not traversed completely, judging whether the currently traversed state data meets the condition corresponding to the currently traversed third rule data; wherein the condition corresponding to the third rule data meeting the current traversal includes at least one of: satisfying a third operation trigger condition parameter in the third rule data, satisfying a time parameter in a third action set in the third rule data; under the condition that the currently traversed state data meet the condition corresponding to the currently traversed third rule data, judging whether the currently traversed third rule data have simulated execution records corresponding to the currently traversed state data; under the condition that the currently traversed state data are judged not to meet the condition corresponding to the currently traversed third rule data, re-traversing the state data in the target historical state data; and under the condition that no simulated execution record exists, determining the currently traversed state data as the target data.
In an optional embodiment of the present application, the third determining module 423 is configured to simulate to execute a third operation in a third operation set corresponding to the target data in the third rule data, and record the target data as simulated execution; determining output status data simulating execution of the third rule data based on the target historical operation data.
In an alternative embodiment of the present application, as shown in fig. 7, the apparatus further comprises a second determining unit 43 configured to determine a probability that the rule data satisfies a preset condition according to a deep learning model.
In an optional embodiment of the present application, the apparatus further includes an output unit, configured to output a prompt message when the probability that the rule data satisfies the preset condition determined by the determination unit reaches a preset threshold.
In an optional embodiment of the present application, the obtaining unit 41 is configured to obtain historical state data and historical operation data recorded in a cloud platform server, use the historical state data as the target historical state data, and use the historical operation data as the target historical operation data.
In an optional embodiment of the present application, the obtaining unit 41 is configured to obtain historical state data and historical operation data recorded in a cloud platform server, select partial state data from the historical state data as the target historical state data, and select partial operation data from the historical operation data as the target historical operation data; the number and the type of the equipment objects corresponding to the selected part of the state data and the selected part of the operation data are the same as those of the equipment objects contained in the specific range; or the types of the equipment objects corresponding to the selected part of state data and the selected part of operation data are partially the same as the types of the equipment objects contained in the specific range; or the types of the device objects corresponding to the selected partial state data and the selected partial operation data are the same as the types of the device objects contained in the specific range, and the number of the device objects corresponding to the selected partial state data and the selected partial operation data is different from the number of the device objects contained in the specific range.
In the embodiment of the present application, the obtaining Unit 41, the first determining Unit 42 (for example, including the first determining module 421, the second determining module 422, the third determining module 423), the second determining Unit 43, and the output Unit in the apparatus may be implemented by a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Micro Control Unit (MCU) or a Programmable Gate Array (FPGA) in practical applications.
It should be noted that: in the rule checking device provided in the above embodiment, only the division of each program module is taken as an example for the case of performing the rule checking, and in practical applications, the processing distribution may be completed by different program modules as needed, that is, the internal structure of the device may be divided into different program modules to complete all or part of the processing described above. In addition, the rule checking device and the rule checking method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application. The computer device 600 shown in fig. 8 includes a processor 610, and the processor 610 can call and run a computer program from a memory to implement the method in the embodiment of the present application.
Optionally, as shown in FIG. 8, the computer device 600 may also include a memory 620. From the memory 620, the processor 610 may call and run a computer program to implement the method in the embodiment of the present application.
The memory 620 may be a separate device from the processor 610, or may be integrated into the processor 610.
Optionally, as shown in fig. 8, the computer device 600 may further include a transceiver 630, and the processor 610 may control the transceiver 630 to communicate with other devices, and specifically, may transmit information or data to the other devices or receive information or data transmitted by the other devices.
The transceiver 630 may include a transmitter and a receiver, among others. The transceiver 630 may further include one or more antennas.
Fig. 9 is a schematic structural diagram of a chip of an embodiment of the present application. The chip 700 shown in fig. 9 includes a processor 710, and the processor 710 can call and run a computer program from a memory to implement the method in the embodiment of the present application.
Optionally, as shown in fig. 9, the chip 700 may further include a memory 720. From the memory 720, the processor 710 can call and run a computer program to implement the method in the embodiment of the present application.
The memory 720 may be a separate device from the processor 710, or may be integrated into the processor 710.
Optionally, the chip 700 may further include an input interface 730. The processor 710 may control the input interface 730 to communicate with other devices or chips, and in particular, may obtain information or data transmitted by other devices or chips.
Optionally, the chip 700 may further include an output interface 740. The processor 710 may control the output interface 740 to communicate with other devices or chips, and in particular, may output information or data to the other devices or chips.
Optionally, the chip may be applied to the computer device in the embodiment of the present application, and the chip may implement the corresponding process implemented by the computer device in each method in the embodiment of the present application, and for brevity, details are not described here again.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as a system-on-chip, a system-on-chip or a system-on-chip, etc.
It should be understood that the processor of the embodiments of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a DSP, an Application Specific Integrated Circuit (ASIC), an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application 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 steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
It will be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous link SDRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that the above memories are exemplary but not limiting illustrations, for example, the memories in the embodiments of the present application may also be Static Random Access Memory (SRAM), dynamic random access memory (dynamic RAM, DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (enhanced SDRAM, ESDRAM), Synchronous Link DRAM (SLDRAM), Direct Rambus RAM (DR RAM), and the like. That is, the memory in the embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.
The embodiment of the application also provides a computer readable storage medium for storing the computer program. Optionally, the computer-readable storage medium may be applied to the computer device in the embodiment of the present application, and the computer program enables the computer to execute the corresponding process implemented by the computer device in each method in the embodiment of the present application, which is not described herein again for brevity.
Embodiments of the present application also provide a computer program product comprising computer program instructions. Optionally, the computer program product may be applied to the computer device in the embodiment of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the computer device in the methods in the embodiment of the present application, which are not described herein again for brevity.
The embodiment of the application also provides a computer program. Optionally, the computer program may be applied to the computer device in the embodiment of the present application, and when the computer program runs on a computer, the computer is enabled to execute the corresponding process implemented by the computer device in each method in the embodiment of the present application, and for brevity, details are not described here again.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (39)
- A method of rule checking, the method comprising:obtaining target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;determining a probability based on the target historical state data and the target historical operation data, wherein the probability is the probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
- The method of claim 1, wherein the determining a probability based on the target historical state data and the target historical operational data comprises:determining a first probability based on the target historical state data and the target historical operation data, the first probability being a probability that first rule data may not be executed.
- The method of claim 2, wherein the determining a first probability based on the target historical state data and the target historical operational data comprises:obtaining a first portion of historical state data from the target historical state data; the first part of historical state data is state data which meets a first operation triggering condition parameter in the first rule data in the target historical state data;querying the target historical operation data to obtain a first record, and determining a first probability based on the first record; wherein the first record is operation data corresponding to first historical state data in the target historical operation data, and executing a first operation in a first operation set in the first rule data; the first historical state data is any one of the first part of historical state data; the first operation is any operation in the first set of operations.
- The method of claim 3, wherein the determining a first probability based on the first record comprises:respectively obtaining the number of successful records and the number of failed records based on the first record, and determining the first probability based on the number of successful records and the number of failed records.
- The method of claim 1, wherein the determining a probability based on the target state history data and the target operation history data comprises:determining a second probability based on the target historical state data and the target historical operation data, the second probability being a probability of an operation execution conflict between at least two second rule data; the at least two second rule data are rule data for the same device object.
- The method of claim 5, wherein the determining a second probability based on the target historical state data and the target historical operational data comprises:determining a second operation triggering condition parameter, and obtaining a second part of historical state data from the target historical state data based on the second operation triggering condition parameter; the second part of historical state data is state data which meets the second operation triggering condition parameter in the target historical state data; the second operation trigger condition parameter is an operation trigger condition parameter for the same device object included in the at least two pieces of second rule data;determining a second operation set, judging whether a second operation in the second operation set has a mutual exclusion relationship with other operations, and obtaining a first result; the second set of operations comprises operations in the set of operations for the same device object in the at least two second rule data; the second operation is any operation in the second operation set; the other operation is other operation in the second operation set except the second operation;under the condition that the first result is that the second operation in the second operation set does not have a mutual exclusion relationship with other operations, querying the target historical operation data to obtain a second record; the second record is operation data which corresponds to second historical state data and executes the second operation in the target historical operation data; the second historical state data is any historical state data in the second part of historical state data;determining the second probability based on the first result and the second record.
- The method of claim 6, wherein the determining the second probability based on the first result and the second record comprises:under the condition that the first result is that the second operation in the second operation set has a mutual exclusion relationship with other operations, obtaining a first number of the second operations with the mutual exclusion relationship;respectively obtaining the number of successful records and the number of failed records based on the second record;determining the second probability based on the first number, the number of successful records, and the number of failed records.
- The method according to claim 6 or 7, wherein the determining whether the second operation in the second operation set has a mutually exclusive relationship with other operations to obtain a first result comprises:judging whether a second operation in the second operation set and other operations are the operations with the same operation type and different operation parameters to obtain a first result;determining that a second operation in the second operation set has a mutually exclusive relationship with other operations when the first result is that the second operation and the other operations are of the same operation type and have different operation parameters;and determining that the second operation and other operations in the second operation set do not have a mutually exclusive relationship when the first result is that the second operation and other operations in the second operation set are not of the same operation type and have different operation parameters.
- The method of claim 6, wherein the determining a second set of operations comprises:performing union processing on operations in the operation sets aiming at the same equipment object in the at least two pieces of second rule data to obtain a second operation set;the determining a second operation triggering condition parameter includes: and performing logic and processing on the operation triggering condition parameters aiming at the same equipment object in the at least two pieces of second rule data to obtain second operation triggering condition parameters.
- The method of claim 1, wherein the determining a prescribed probability based on the target historical state data and the target historical operational data comprises:determining a third probability based on the target historical state data and the target historical operation data, the third probability being a probability of at least one third rule data loop execution.
- The method of claim 10, wherein the determining a third probability based on the target historical state data and the target historical operational data comprises:determining target data in the target historical state data, and simulating and executing third rule data according to the target data;determining the third probability in dependence on a second amount that the third rule data is executed in simulation and a third amount of state data in the target historical state data.
- The method of claim 11, wherein the determining target data in the target historical state data comprises:traversing the state data in the target historical state data, and traversing the third rule data in the at least one third rule data under the condition that the state data is not completely traversed;under the condition that the third rule data is not traversed completely, judging whether the currently traversed state data meets the condition corresponding to the currently traversed third rule data; wherein the condition corresponding to the third rule data meeting the current traversal includes at least one of: satisfying a third operation trigger condition parameter in the third rule data, satisfying a time parameter in a third action set in the third rule data;under the condition that the currently traversed state data meet the condition corresponding to the currently traversed third rule data, judging whether the currently traversed third rule data have simulated execution records corresponding to the currently traversed state data; under the condition that the currently traversed state data are judged not to meet the condition corresponding to the currently traversed third rule data, re-traversing the state data in the target historical state data;and under the condition that no simulated execution record exists, determining the currently traversed state data as the target data.
- The method of claim 11, wherein said simulating execution of the third rule data according to the target data comprises:simulating and executing a third operation in a third operation set corresponding to the target data in the third rule data, and recording the target data as simulated and executed;determining output status data simulating execution of the third rule data based on the target historical operation data.
- The method of any one of claims 1 to 13, wherein the method further comprises: and determining the probability that the rule data meet the preset conditions according to a deep learning model.
- The method of any one of claims 1 to 14, wherein the method further comprises:and outputting prompt information under the condition that the probability that the rule data meet the preset condition reaches a preset threshold value.
- The method of any of claims 1 to 15, wherein the obtaining target historical state data and target historical operational data comprises:and acquiring historical state data and historical operation data recorded in a cloud platform server, wherein the historical state data is used as the target historical state data, and the historical operation data is used as the target historical operation data.
- The method of any of claims 1 to 15, wherein the obtaining target historical state data and target historical operational data comprises:acquiring historical state data and historical operation data recorded in a cloud platform server, selecting partial state data from the historical state data as target historical state data, and selecting partial operation data from the historical operation data as the target historical operation data;the number and the type of the equipment objects corresponding to the selected part of the state data and the selected part of the operation data are the same as those of the equipment objects contained in the specific range; alternatively, the first and second electrodes may be,the types of the equipment objects corresponding to the selected part of state data and the selected part of operation data are partially the same as the types of the equipment objects contained in the specific range; alternatively, the first and second electrodes may be,the types of the equipment objects corresponding to the selected partial state data and the selected partial operation data are the same as the types of the equipment objects contained in the specific range, and the number of the equipment objects corresponding to the selected partial state data and the selected partial operation data is different from the number of the equipment objects contained in the specific range.
- A rule checking apparatus, the apparatus comprising: an acquisition unit and a first determination unit; wherein the content of the first and second substances,the acquisition unit is configured to acquire target historical state data and target historical operation data; the target historical state data comprises historical data of attributes corresponding to the equipment objects; the target historical operation data comprises historical data of operation corresponding to the equipment object;the first determining unit is configured to determine a probability based on the target historical state data and the target historical operation data, wherein the probability is a probability that rule data meets a preset condition; wherein the rule data includes: an operation trigger condition parameter and an operation set.
- The apparatus of claim 18, wherein the first determination unit comprises a first determination module configured to determine a first probability based on the target historical state data and the target historical operation data, the first probability being a probability that first rule data cannot be executed.
- The apparatus of claim 19, wherein the first determining module is configured to obtain a first portion of historical state data from the target historical state data; the first part of historical state data is state data which meets a first operation triggering condition parameter in the first rule data in the target historical state data; querying the target historical operation data to obtain a first record, and determining the first probability based on the first record; wherein the first record is operation data corresponding to first historical state data in the target historical operation data, and executing a first operation in a first operation set in the first rule data; the first historical state data is any one of the first part of historical state data; the first operation is any operation in the first set of operations.
- The apparatus of claim 20, wherein the first determining module is configured to obtain a number of successful records and a number of failed records based on the first record, respectively, and to determine the first probability based on the number of successful records and the number of failed records.
- The apparatus of claim 18, wherein the determining unit comprises a second determining module configured to determine a second probability that an operation between at least two second rule data performs a conflict based on the target historical state data and the target historical operation data; the at least two second rule data are rule data for the same device object.
- The apparatus of claim 22, wherein the second determination module is configured to determine a second operational trigger condition parameter, obtain a second portion of historical state data from the target historical state data based on the second operational trigger condition parameter; the second part of historical state data is state data which meets the second operation triggering condition parameter in the target historical state data; the second operation trigger condition parameter is an operation trigger condition parameter for the same device object included in the at least two pieces of second rule data; determining a second operation set, judging whether a second operation in the second operation set has a mutual exclusion relationship with other operations, and obtaining a first result; the second set of operations comprises operations in the set of operations for the same device object in the at least two second rule data; the second operation is any operation in the second operation set; the other operation is other operation in the second operation set except the second operation; under the condition that the first result is that the second operation in the second operation set does not have a mutual exclusion relationship with other operations, querying the target historical operation data to obtain a second record; the second record is an operation record which corresponds to second historical state data and executes the second operation in the target historical operation data; the second historical state data is any historical state data in the second part of historical state data; determining the second probability based on the first result and the second record.
- The apparatus of claim 23, wherein the second determining module is configured to obtain a first number of second operations having a mutually exclusive relationship if the first result is that a second operation in the second operation set has a mutually exclusive relationship with other operations; respectively obtaining the number of successful records and the number of failed records based on the second record; determining the second probability based on the first number, the number of successful records, and the number of failed records.
- The apparatus according to claim 23 or 24, wherein the second determining module is configured to determine whether a second operation in the second operation set is an operation with the same operation type and different operation parameters as other operations, and obtain a first result; determining that a second operation in the second operation set has a mutually exclusive relationship with other operations when the first result is that the second operation and the other operations are of the same operation type and have different operation parameters; and determining that the second operation and other operations in the second operation set do not have a mutually exclusive relationship when the first result is that the second operation and other operations in the second operation set are not of the same operation type and have different operation parameters.
- The apparatus according to claim 23, wherein the second determining module is configured to perform union processing on operations in the operation sets for the same device object in the at least two pieces of second rule data to obtain a second operation set; and the second rule data is also configured to logically and process the operation triggering condition parameters aiming at the same equipment object in the at least two pieces of second rule data to obtain second operation triggering condition parameters.
- The apparatus of claim 18, wherein the determination unit further comprises a third determination module configured to determine a third probability based on the target historical state data and the target historical operation data, the third probability being a probability of at least one third rule data loop execution.
- The apparatus of claim 27, wherein the third determination module is configured to determine target data in the target historical state data, simulate execution of the third rule data according to the target data; determining the third probability in dependence on a second amount that the third rule data is executed in simulation and a third amount of state data in the target historical state data.
- The apparatus of claim 28, wherein the third determining module is further configured to traverse the state data in the target historical state data, and traverse a third rule data in the at least one third rule data if the state data is not traversed; under the condition that the third rule data is not traversed completely, judging whether the currently traversed state data meets the condition corresponding to the currently traversed third rule data; wherein the condition corresponding to the third rule data meeting the current traversal includes at least one of: satisfying a third operation trigger condition parameter in the third rule data, satisfying a time parameter in a third action set in the third rule data; under the condition that the currently traversed state data meet the condition corresponding to the currently traversed third rule data, judging whether the currently traversed third rule data have simulated execution records corresponding to the currently traversed state data; under the condition that the currently traversed state data are judged not to meet the condition corresponding to the currently traversed third rule data, re-traversing the state data in the target historical state data; and under the condition that no simulated execution record exists, determining the currently traversed state data as the target data.
- The apparatus of claim 28, wherein the third determining module is configured to simulate execution of a third operation in a third set of operations in the third rule data corresponding to the target data, and record the target data as simulated execution; determining output status data simulating execution of the third rule data based on the target historical operation data.
- The apparatus according to any one of claims 18 to 30, wherein the apparatus further comprises a second determining unit configured to determine a probability that the rule data satisfies a preset condition according to a deep learning model.
- The apparatus according to any one of claims 18 to 31, wherein the apparatus further comprises an output unit configured to output a prompt message in a case where the probability that the rule data determined by the determination unit satisfies a preset condition reaches a preset threshold.
- The apparatus according to any one of claims 18 to 32, wherein the obtaining unit is configured to obtain historical state data and historical operation data recorded in a cloud platform server, and use the historical state data as the target historical state data and the historical operation data as the target historical operation data.
- The apparatus according to any one of claims 18 to 32, wherein the obtaining unit is configured to obtain historical state data and historical operation data recorded in a cloud platform server, select partial state data from the historical state data as the target historical state data, and select partial operation data from the historical operation data as the target historical operation data;the number and the type of the equipment objects corresponding to the selected part of the state data and the selected part of the operation data are the same as those of the equipment objects contained in the specific range; alternatively, the first and second electrodes may be,the types of the equipment objects corresponding to the selected part of state data and the selected part of operation data are partially the same as the types of the equipment objects contained in the specific range; alternatively, the first and second electrodes may be,the types of the equipment objects corresponding to the selected partial state data and the selected partial operation data are the same as the types of the equipment objects contained in the specific range, and the number of the equipment objects corresponding to the selected partial state data and the selected partial operation data is different from the number of the equipment objects contained in the specific range.
- A computer device, comprising: a processor and a memory for storing a computer program, the processor being configured to invoke and execute the computer program stored in the memory to perform the method of any of claims 1 to 17.
- A chip, comprising: a processor for calling and running a computer program from a memory so that a device on which the chip is installed performs the method of any one of claims 1 to 17.
- A computer-readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 1 to 17.
- A computer program product comprising computer program instructions to cause a computer to perform the method of any one of claims 1 to 17.
- A computer program for causing a computer to perform the method of any one of claims 1 to 17.
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| PCT/CN2019/103879 WO2021035750A1 (en) | 2019-08-30 | 2019-08-30 | Rule check method and device, and computer device |
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| CN113574482B CN113574482B (en) | 2023-12-08 |
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| CN113574482B (en) | 2023-12-08 |
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