Instruction execution method and device, air conditioning system, equipment and storage medium

Abstract

The invention relates to an instruction execution method and device, an air conditioning system, equipment and a storage medium. The method comprises the following steps: intercepting instruction data sent to target equipment; identifying an instruction type of the instruction data by using a static dictionary and user preference data; under the condition that the instruction type of the instruction data is a suspicious instruction, correcting the instruction data by utilizing a dynamic dictionary and the user preference data; determining the confidence coefficient of the corrected instruction data according to the equipment state data corresponding to the target equipment and the external environment data; and under the condition that the confidence coefficient of the corrected instruction data is greater than a preset confidence coefficient threshold value, controlling the target equipment to execute the corrected instruction data. According to the embodiment of the invention, through the synergistic effect of interception, identification, correction and evaluation, the technical problem that the traditional static rule cannot identify the abnormal instruction of which the control parameter is in a reasonable range is effectively solved.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and in particular to an instruction execution method, apparatus, air conditioning system, device and storage medium. Background Technology

[0002] With the explosive growth of data volume and the increasing complexity of business needs, the efficiency of intelligent equipment control has become a critical factor. Traditional intelligent control methods often rely on static rules to identify abnormal commands. This involves pre-setting static rules that include control commands and their reasonable parameter ranges. If the control parameters corresponding to a command exceed this reasonable range, the command is considered abnormal and discarded. However, static rules are ill-suited to dynamically changing data environments and business needs. For example, in intelligent air conditioning systems, with the widespread adoption of microservices, containerization, and serverless architectures, business interfaces have increased, and data traffic and interaction patterns have become more complex. In this environment, static rule-based control methods cannot identify abnormal commands with control parameters within reasonable ranges. For instance, a user might habitually set the air conditioner temperature to 26℃ in summer. If, due to a mistake, the temperature is set to 16℃, this control parameter is within the normal operating range of the air conditioner and would not be intercepted by static rules. However, execution of this setting could lead to a poor user experience or abnormal energy consumption. Summary of the Invention

[0003] This application provides an instruction execution method, apparatus, air conditioning system, device, and storage medium to solve the problem that static rule-based control methods cannot identify abnormal instructions when control parameters are within a reasonable range.

[0004] To address the aforementioned technical problems, the technical solution of this application is provided through the following embodiments: This application provides an instruction execution method, comprising: intercepting instruction data sent to a target device; identifying the instruction type of the instruction data using a static dictionary and user preference data; correcting the instruction data using a dynamic dictionary and the user preference data if the instruction type of the instruction data is a suspicious instruction; determining the confidence level of the corrected instruction data based on device status data and external environment data corresponding to the target device; and controlling the target device to execute the corrected instruction data if the confidence level of the corrected instruction data is greater than a preset confidence level threshold.

[0005] The step of identifying the instruction type of the instruction data using a static dictionary and user preference data includes: reading the control instructions and control parameters in the instruction data; querying the static dictionary for a reasonable range of parameters corresponding to the control instructions; if the control parameters are not within the reasonable range, determining that the instruction type of the instruction data is an abnormal instruction; if the control parameters are within the reasonable range, determining whether the instruction data conforms to the user preference data; if the instruction data conforms to the user preference data, determining that the instruction type of the instruction data is a normal instruction; and if the number of instructions does not conform to the user preference data, determining that the instruction type of the instruction data is a suspicious instruction.

[0006] The method further includes: if the instruction type of the instruction data is an abnormal instruction, prohibiting the target device from executing the instruction data; and if the instruction type of the instruction data is a normal instruction, controlling the target device to execute the instruction data.

[0007] The step of modifying the instruction data using a dynamic dictionary and the user preference data includes: modifying the instruction parameters in the instruction data so that the modified instruction data conforms to the user preference data and does not belong to the blacklist instruction data in the dynamic dictionary.

[0008] The step of determining the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device includes: inputting the device status data, the external environment data, and the corrected instruction data into a pre-trained digital twin model, so as to use the digital twin model to determine the confidence level of the corrected instruction data based on the device status data, the external environment data, and the corrected instruction data.

[0009] The method further includes, after controlling the target device to execute the modified instruction data, collecting device operation data and operation behavior data of the target device after executing the modified instruction data; updating the user preference data according to the instruction data when the device operation data meets the preset safe operation conditions and the operation behavior data is null; and updating the instruction data to the blacklist instruction data of the dynamic dictionary when the device operation data does not meet the safe operation conditions or the operation behavior data is not null.

[0010] This application also provides an instruction execution device, comprising: an interception module for intercepting instruction data sent to a target device; an identification module for identifying the instruction type of the instruction data using a static dictionary and user preference data; a correction module for correcting the instruction data using a dynamic dictionary and the user preference data when the instruction type of the instruction data is a suspicious instruction; a determination module for determining the confidence level of the corrected instruction data based on device status data and external environment data corresponding to the target device; and an execution module for controlling the target device to execute the corrected instruction data when the confidence level of the corrected instruction data is greater than a preset confidence level threshold.

[0011] This application also provides an air conditioning system, wherein the air conditioning system applies the instruction execution method described in any of the above claims.

[0012] This application also provides an instruction execution device, including: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus, wherein the processor is configured to execute an instruction execution program stored in the memory to implement the instruction execution method described in any of the above claims.

[0013] This application also provides a computer-readable storage medium storing computer-executable instructions, which are executed to implement the instruction execution method described in any of the preceding claims.

[0014] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application can intercept instruction data sent to the target device; identify the instruction type of the instruction data using a static dictionary and user preference data; correct the instruction data using a dynamic dictionary and the user preference data when the instruction type of the instruction data is a suspicious instruction; determine the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device; and control the target device to execute the corrected instruction data when the confidence level of the corrected instruction data is greater than a preset confidence level threshold. This application effectively solves the technical problem that traditional static rules cannot identify abnormal instructions with control parameters within a reasonable range through the synergistic effect of interception, identification, correction, and evaluation. This application accurately identifies suspicious instructions using a static dictionary and user preference data, then corrects them using a dynamic dictionary to avoid risks and meet personalized needs, and finally evaluates the confidence level of the instruction by combining device status data and external environment data, ensuring the security and reliability of the instruction. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 This is a flowchart of an instruction execution method according to an embodiment of this application; Figure 2 This is a flowchart of the instruction type identification steps according to an embodiment of this application; Figure 3 This is a flowchart of the steps for modifying instruction data according to an embodiment of this application; Figure 4 This is a flowchart of data update steps according to an embodiment of the present application; Figure 5 This is a structural diagram of an instruction execution apparatus according to an embodiment of this application; Figure 6 This is a structural diagram of an instruction execution device according to an embodiment of the present application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] This application provides an instruction execution method. For example... Figure 1 The diagram shown is a flowchart of an instruction execution method according to an embodiment of this application.

[0022] Step S110: Intercept instruction data sent to the target device.

[0023] Interception refers to intercepting instruction data before it is sent to the target device.

[0024] The target equipment refers to the controlled equipment or equipment system. Types of target equipment include, but are not limited to, air conditioning systems.

[0025] Command data refers to the control commands and control parameters used to control the target device. This application embodiment ensures that all instruction data is monitored and captured by intercepting instruction data, thereby preventing the risks that may arise from direct instruction execution.

[0026] Step S120: Using a static dictionary and user preference data, identify the instruction type of the instruction data.

[0027] A static dictionary refers to pre-defined static rules. A static dictionary includes at least one static rule. Static rules include control commands and their corresponding reasonable parameter ranges. The reasonable parameter range refers to the reasonable control range of the control parameters.

[0028] User preference data refers to data on users' routine behaviors.

[0029] Command type refers to the classification of control data. Command types include, but are not limited to: normal commands, abnormal commands, and suspicious commands. Normal commands are control data whose control parameters are within a reasonable range and conform to user preference data. Abnormal commands are control data whose control parameters are outside a reasonable range. Suspicious commands are control data whose control parameters are within a reasonable range but deviate from user preference data.

[0030] Step S130: If the instruction type of the instruction data is a suspicious instruction, the instruction data is corrected using a dynamic dictionary and the user preference data.

[0031] A dynamic dictionary refers to identification rules that can be dynamically updated. Identification rules are used to determine whether a suspicious instruction is an abnormal instruction. Identification rules can be blacklisted instruction data. Blacklisted instruction data refers to instruction data that is prohibited from execution.

[0032] Modifying instruction data refers to modifying the control parameters in the instruction data so that the modified control parameters conform to the user's preferred data and are not included in the blacklist instruction data.

[0033] The embodiments of this application can intelligently optimize suspicious instructions instead of directly rejecting them, thus balancing the security of instruction data and user experience.

[0034] Step S140: Determine the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device.

[0035] Equipment status data refers to the current operating status data of the target equipment. For example, equipment status data includes data such as fan speed.

[0036] External environment data refers to the current external data of the environment in which the target device is located. For example, external environment data includes data such as temperature, humidity, and weather.

[0037] Confidence level refers to the degree of safe operation of the target device after executing the corrected instruction data under the current device status data and external environment data. The higher the confidence level, the safer the target device is in executing the corrected instruction data; the lower the confidence level, the less safe the target device is in executing the corrected instruction data.

[0038] In order to prevent equipment failure, inefficiency and poor user experience that may be caused by executing modified instruction data, this application embodiment assesses the degree of risk that the modified instruction data may cause before making the target device execute the modified instruction data, so as to avoid user inconvenience or equipment risk caused by directly executing the modified instruction data.

[0039] Step S150: If the confidence level of the corrected instruction data is greater than a preset confidence level threshold, control the target device to execute the corrected instruction data.

[0040] The confidence threshold is a standard value used to measure whether the corrected instruction data should be executed.

[0041] If the confidence level of the corrected instruction data is greater than a preset confidence threshold, the corrected instruction data is sent to the target device, so that the target device executes the control instructions in the instruction data according to the control parameters in the instruction data.

[0042] In this embodiment, instruction data sent to the target device is intercepted; the instruction type of the instruction data is identified using a static dictionary and user preference data; if the instruction type of the instruction data is a suspicious instruction, the instruction data is corrected using a dynamic dictionary and the user preference data; the confidence level of the corrected instruction data is determined based on the device status data and external environment data corresponding to the target device; if the confidence level of the corrected instruction data is greater than a preset confidence level threshold, the target device is controlled to execute the corrected instruction data. This embodiment effectively solves the technical problem that traditional static rules cannot identify abnormal instructions with control parameters within a reasonable range through the synergistic effect of interception, identification, correction, and evaluation. This embodiment accurately identifies suspicious instructions using a static dictionary and user preference data, then corrects them using a dynamic dictionary to avoid risks and meet personalized needs, and finally evaluates the confidence level of the instruction by combining device status data and external environment data, ensuring the security and reliability of the instruction.

[0043] To make the embodiments of this application clearer, the instruction execution method of the embodiments of this application will be further described below.

[0044] In this embodiment of the application, instruction data sent to the target device is intercepted.

[0045] The sources of instruction data include, but are not limited to: the mobile terminal corresponding to the target device, and the remote control. The types of mobile terminals include, but are not limited to: mobile phones and tablet computers.

[0046] Furthermore, it can capture all instruction data entering the target device's main controller in real time, and parse out the control instructions (such as SET_TEMP), control parameters (such as value: 100), and the source of the instruction data (such as src:App). Here, App refers to the application installed on the mobile terminal.

[0047] In this embodiment of the application, after intercepting the instruction data sent to the target device, the instruction type of the instruction data can be identified using a static dictionary and user preference data.

[0048] Figure 2 This is a flowchart of the instruction type identification steps according to an embodiment of the present application.

[0049] Step S210: Read the control instructions and control parameters from the instruction data.

[0050] The control commands in the instruction data are used to control the target device to perform operational actions.

[0051] The control parameters in the instruction data are used to indicate the parameter values ​​used by the target device when performing the operation.

[0052] Step S220: Query the static dictionary for a reasonable range of parameters corresponding to the control command.

[0053] A static dictionary includes at least one static rule.

[0054] Static rules include: control commands and their corresponding reasonable parameter ranges. For example: temperature control commands and their corresponding reasonable parameter ranges from 0℃ to 35℃.

[0055] The reasonable range of parameters refers to the parameter boundaries for the safe operation of equipment. Control parameters in the instruction data that exceed the corresponding reasonable range will be judged as violations, i.e., abnormal instructions.

[0056] Step S230: Determine whether the control parameter is within a reasonable range; if yes, proceed to step S250; if no, proceed to step S240.

[0057] Step S240: If the control parameters are not within a reasonable range, determine that the instruction type of the instruction data is an abnormal instruction.

[0058] If the instruction type of the instruction data is an abnormal instruction, the target device is prohibited from executing the instruction data. Furthermore, if the instruction type of the instruction data is an abnormal instruction, the instruction data is discarded, and sending the instruction data to the target device is prohibited.

[0059] Step S250: If the control parameters are within a reasonable range, determine whether the instruction data matches the user preference data; if yes, proceed to step S260; if no, proceed to step S270.

[0060] When the control parameters are within a reasonable range, it is also necessary to exclude instruction data that does not conform to user preferences to avoid directly executing instruction data, which could cause target device malfunction or poor user experience.

[0061] The system reads the preference data corresponding to the current time period from the user preference data. The current time period can be a time range centered on the current time. The duration of the time range can be a preset value. For example, the duration range can be 1 hour. The preference data corresponding to the current time period includes: control commands executed in the same historical period and their control parameter ranges. If the control command in the intercepted command data is among the control commands executed in the same historical period, and the control parameters in the intercepted command data are within the control parameter range corresponding to that historically executed control command, then the intercepted command data is determined to conform to the user preference data; otherwise, it is determined that the intercepted command data does not conform to the user preference data.

[0062] Furthermore, the range of control parameters can be a continuous numerical range or a discrete numerical range.

[0063] Step S260: If the instruction data matches the user preference data, determine that the instruction type of the instruction data is a normal instruction.

[0064] If the instruction type of the instruction data is a normal instruction, the target device is controlled to execute the instruction data.

[0065] Step S270: If the number of instructions does not match the user preference data, determine that the instruction type of the instruction data is a suspicious instruction.

[0066] For example: The intercepted command data is to control the air conditioner temperature to 16℃; the reasonable range of the temperature control command and its corresponding parameters is 0℃ to 35℃; the user preference data is 24℃ to 26℃; it can be determined that 16℃ is within the reasonable range of parameters, but deviates from the user preference data, so the intercepted command data is a suspicious command.

[0067] If the instruction type of the instruction data is a suspicious instruction, the target device is selectively controlled to execute the instruction data. Further, the target device is controlled to execute instruction data with a confidence level greater than a confidence threshold; the target device is prohibited from executing instruction data with a confidence level less than or equal to the confidence threshold. The confidence level of the instruction data will be described later and will not be elaborated upon here.

[0068] This application embodiment introduces user preference data as a secondary judgment criterion to construct a two-layer filtering mechanism composed of static specifications and dynamic preferences. This mechanism can accurately identify and separate suspicious instructions that traditional static rules cannot identify, namely, operations where the control parameters are within a reasonable range but the control behavior is abnormal. This ensures the basic security of the target device (intercepting abnormal instructions) and guarantees a smooth experience (allowing normal instructions), while providing accurate input for subsequent targeted intelligent correction and risk assessment processes. This fundamentally solves the technical defects of fixed static rules being too rigid and unable to adapt to personalized scenarios.

[0069] In this embodiment of the application, after identifying the instruction type of the instruction data, the instruction data can be corrected using a dynamic dictionary and the user preference data if the instruction type of the instruction data is a suspicious instruction.

[0070] A dynamic dictionary refers to identification rules that can be dynamically updated. Identification rules are used to determine whether a suspicious instruction is an abnormal instruction. Identification rules can be blacklisted instruction data. Blacklisted instruction data refers to instruction data that is prohibited from execution.

[0071] Specifically, the instruction parameters in the instruction data are modified so that the modified instruction data conforms to the user preference data and does not belong to the blacklist instruction data in the dynamic dictionary.

[0072] Figure 3 This is a flowchart of the steps for modifying instruction data according to an embodiment of this application.

[0073] Step S310: In the user preference data, obtain the historical control commands executed at the same time and their control parameter ranges that match the command data intercepted this time.

[0074] The control command executed in the same historical period is the same as the control command in the command data intercepted this time, and the control parameters in the command data intercepted this time are within the range of control parameters corresponding to the control command executed in the same historical period.

[0075] Step S320: Generate candidate control parameters that match the user's preference data based on the control parameter range of the same historical period.

[0076] The number of candidate control parameters is at least one.

[0077] Furthermore, the candidate control parameter is a preset number of values ​​that are closest to the control parameters in the command data intercepted in this case, within the range of control parameters.

[0078] For example: if the control parameter range is 22℃ to 26℃, and the control parameter in the intercepted command data is 35℃, and the preset number is 3, then the candidate control parameters can be 26℃, 25℃, and 24℃.

[0079] Step S330: In the dynamic dictionary, query the blacklist instruction data that matches the control instruction in the instruction data intercepted this time.

[0080] The control commands in the intercepted command data are the same as those in the matching blacklist command data.

[0081] Step S340: Select candidate control parameters that are different from the control parameters in the blacklist instruction data and use them as correction values ​​for the control parameters in the instruction data to be intercepted this time.

[0082] The control commands and their control parameter correction values ​​in the intercepted command data are identified as the corrected command data.

[0083] This application embodiment achieves accurate, secure, and personalized correction of suspicious commands by combining users' historical preferences with dynamic security rules (dynamic dictionary): First, candidate control parameters that are close to user habits are generated based on historical preference data, ensuring the personalization of the correction results and user comfort; then, parameters with known risks are filtered out through the blacklist mechanism of the dynamic dictionary to prevent target devices from malfunctioning or deteriorating in performance.

[0084] In this embodiment of the application, after correcting the instruction data, the confidence level of the corrected instruction data can be determined based on the device status data corresponding to the target device and the external environment data.

[0085] Specifically, the device status data, the external environment data, and the corrected instruction data can be input into a pre-trained digital twin model so that the confidence level of the corrected instruction data can be determined using the digital twin model based on the device status data, the external environment data, and the corrected instruction data.

[0086] Digital twin models are used to predict the safe operating level of a target device after executing corrected command data, given the current device status data and external environment data. For example, a digital twin model can predict the compressor current, exhaust temperature, and energy consumption of the target device in the next 30 seconds to 5 minutes. Based on these predictions, it can be determined whether the target device is operating safely and the degree of safe operation can be given.

[0087] Equipment status data includes, but is not limited to, sensor data and status parameters. Specifically, sensor data detected by various sensors within the target device can be acquired via the device's internal bus. Sensor data includes, but is not limited to, compressor current and outlet air temperature. Status parameters include, but are not limited to, fan speed and compressor operating frequency.

[0088] External environmental data includes, but is not limited to: ambient temperature, ambient humidity, and weather data. Specifically, ambient temperature and humidity can be detected by sensors placed outside the target device, and weather data for the target device's location can be obtained from the internet.

[0089] For example: The intercepted command data is to adjust the air conditioner temperature to 16℃; the reasonable range of the temperature control command and its corresponding parameters is 0℃ to 35℃; the user preference data is 24℃ to 26℃; thus, it can be determined that 16℃ is within the reasonable range of parameters, but deviates from the user preference data, so the intercepted command data is a suspicious command; the current device status data of the target device (air conditioning system), the external environment data, and the intercepted command data are input into the digital twin model. The digital twin model can simulate the operating state of the target device after executing the command data based on the current device status data and the external environment data. For example, if it is predicted that setting the target device to 16℃ when the current ambient temperature is 25℃ will cause the compressor to run at high load for a long time, the digital twin model will output a lower safe operating level value.

[0090] In this embodiment of the application, after determining the confidence level of the corrected instruction data, the target device can be controlled to execute the corrected instruction data if the confidence level of the corrected instruction data is greater than a preset confidence level threshold.

[0091] Furthermore, the modified instruction data can be sent to the target device, so that the target device executes the control instructions in the modified instruction data according to the control parameters in the modified instruction data.

[0092] In this embodiment of the application, after determining the confidence level of the modified instruction data, the target device may be prohibited from executing the modified instruction data if the confidence level of the modified instruction data is less than or equal to the confidence level threshold.

[0093] Furthermore, the transmission of the modified instruction data to the target device can be prohibited, thereby preventing the target device from executing the control instructions in the modified instruction data according to the control parameters in the modified instruction data.

[0094] In this embodiment of the application, after the target device is controlled to execute the modified instruction data, device operation data and operation behavior data of the target device after executing the modified instruction data can be collected; based on the device operation data and operation behavior data, user preference data and dynamic dictionary are updated.

[0095] Figure 4 This is a flowchart of data update steps according to an embodiment of the present application.

[0096] Step S410: Collect device operation data and operation behavior data of the target device after executing the modified instruction data. Equipment operation data refers to the physical state parameters and operational performance parameters of the target device's hardware and system after executing modified instruction data. Equipment operation data directly reflects the impact of the modified instruction data on the target device.

[0097] Operational behavior data refers to the amount of intervention instruction data received by the target device after executing the modified instruction data.

[0098] Intervention command data refers to the operational data used to change the control parameters in the revised command data. Specifically, the intervention commands in the intervention command data are the same as the control commands in the revised command data, but the intervention parameters in the intervention command data differ from the control parameters in the revised command data.

[0099] Step S420: Determine whether the equipment operation data meets the preset safe operation conditions and whether the operation behavior data is empty; if yes, proceed to step S430; if no, proceed to step S440.

[0100] Step S430: If the device operation data meets the preset safe operation conditions and the operation behavior data is empty, update the user preference data according to the instruction data.

[0101] Safe operating conditions refer to the range of operating parameters of the target equipment under safe operating conditions.

[0102] Furthermore, the equipment operation data includes at least one operating parameter (physical state parameter and operating performance parameter). Each operating parameter is compared with its corresponding operating parameter range. If all operating parameters are within their respective operating parameter ranges, it indicates that the equipment operation data meets the preset safe operating conditions; otherwise, it indicates that the equipment operation data does not meet the preset safe operating conditions.

[0103] Furthermore, abnormal alarm data of the target device can be read; if abnormal alarm data exists in the target device, it means that the device operation data meets the preset safe operation conditions; otherwise, it means that the device operation data does not meet the preset safe operation conditions.

[0104] A null value in the operation behavior data indicates that the target device did not receive any intervention command data after executing the modified command data. In other words, the user was satisfied with the modified command data after it was executed and did not further adjust the function corresponding to that command data.

[0105] Step S440: If the device operation data does not meet the safe operation conditions or the operation behavior data is not empty, update the instruction data to the blacklist instruction data in the dynamic dictionary.

[0106] A non-empty operation behavior data value indicates that the target device received intervention command data after executing the modified command data. In other words, the user was dissatisfied with the modified command data after it was executed and continued to control the function corresponding to that command data.

[0107] The instruction data is updated to the blacklist instruction data in the dynamic dictionary, that is, the control instructions and their corresponding control parameters in the intercepted instruction data are stored as a blacklist instruction data.

[0108] This application combines the objective state of the device after the execution of the modified control command with the subjective feedback of the user to verify the modification decision of the command data. Based on the verification result, the user preference data and dynamic dictionary are updated. If the verification is successful, the user preference data is strengthened to improve the level of personalized service. If the verification fails, the blacklist command data is dynamically updated to prevent the recurrence of similar risks. This constructs an efficient self-learning and updating mechanism, driving the intelligent decision-making capability of this application embodiment to be continuously optimized in the continuous iteration, realizing a spiral improvement in security and user experience.

[0109] Of course, after controlling the target device to execute normal instruction data, this application embodiment can also collect device operation data and operation behavior data of the target device after executing the normal instruction data; and update user preference data and dynamic dictionary based on device operation data and operation behavior data. Here, normal instruction data refers to instruction data with the instruction type of normal instruction.

[0110] This application also provides an instruction execution device. For example... Figure 5 The diagram shown is a structural diagram of an instruction execution apparatus according to an embodiment of this application.

[0111] The instruction execution device includes: The interception module 510 is used to intercept instruction data sent to the target device. The identification module 520 is used to identify the instruction type of the instruction data using a static dictionary and user preference data.

[0112] The correction module 530 is used to correct the instruction data using a dynamic dictionary and the user preference data when the instruction type of the instruction data is a suspicious instruction.

[0113] The determination module 540 is used to determine the confidence level of the corrected instruction data based on the device status data corresponding to the target device and the external environment data.

[0114] The execution module 550 is used to control the target device to execute the modified instruction data when the confidence level of the modified instruction data is greater than a preset confidence level threshold.

[0115] The functions of the apparatus described in this application embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in the description of this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0116] This application also provides an air conditioning system. This air conditioning system can utilize the instruction execution method described above.

[0117] This application also provides an instruction execution device, such as... Figure 6 The diagram shown is a structural diagram of an instruction execution device according to an embodiment of this application.

[0118] The instruction execution device includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, communication interface 620, and memory 630 communicate with each other via the communication bus 640.

[0119] The memory 630 is used to store computer programs.

[0120] In one embodiment of this application, when the processor 610 executes a program stored in the memory 630, it implements the instruction execution method provided in any of the foregoing method embodiments, including: intercepting instruction data sent to a target device; identifying the instruction type of the instruction data using a static dictionary and user preference data; correcting the instruction data using a dynamic dictionary and the user preference data if the instruction type of the instruction data is a suspicious instruction; determining the confidence level of the corrected instruction data based on device status data and external environment data corresponding to the target device; and controlling the target device to execute the corrected instruction data if the confidence level of the corrected instruction data is greater than a preset confidence level threshold.

[0121] The step of identifying the instruction type of the instruction data using a static dictionary and user preference data includes: reading the control instructions and control parameters in the instruction data; querying the static dictionary for a reasonable range of parameters corresponding to the control instructions; if the control parameters are not within the reasonable range, determining that the instruction type of the instruction data is an abnormal instruction; if the control parameters are within the reasonable range, determining whether the instruction data conforms to the user preference data; if the instruction data conforms to the user preference data, determining that the instruction type of the instruction data is a normal instruction; and if the number of instructions does not conform to the user preference data, determining that the instruction type of the instruction data is a suspicious instruction.

[0122] The method further includes: if the instruction type of the instruction data is an abnormal instruction, prohibiting the target device from executing the instruction data; and if the instruction type of the instruction data is a normal instruction, controlling the target device to execute the instruction data.

[0123] The step of modifying the instruction data using a dynamic dictionary and the user preference data includes: modifying the instruction parameters in the instruction data so that the modified instruction data conforms to the user preference data and does not belong to the blacklist instruction data in the dynamic dictionary.

[0124] The step of determining the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device includes: inputting the device status data, the external environment data, and the corrected instruction data into a pre-trained digital twin model, so as to use the digital twin model to determine the confidence level of the corrected instruction data based on the device status data, the external environment data, and the corrected instruction data.

[0125] The method further includes, after controlling the target device to execute the modified instruction data, collecting device operation data and operation behavior data of the target device after executing the modified instruction data; updating the user preference data according to the instruction data when the device operation data meets the preset safe operation conditions and the operation behavior data is null; and updating the instruction data to the blacklist instruction data of the dynamic dictionary when the device operation data does not meet the safe operation conditions or the operation behavior data is not null. This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the instruction execution method provided in any of the foregoing method embodiments. Since the instruction execution method has already been described in detail above, any omissions in the description of this embodiment can be found in the relevant descriptions in the foregoing embodiments, and will not be repeated here.

[0126] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0128] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0129] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for executing instructions, characterized in that, include: Intercept command data sent to the target device; The instruction type of the instruction data is identified using a static dictionary and user preference data; If the instruction type in the instruction data is a suspicious instruction, the instruction data is corrected using a dynamic dictionary and the user preference data. The confidence level of the corrected instruction data is determined based on the device status data and external environment data corresponding to the target device. If the confidence level of the corrected instruction data is greater than a preset confidence level threshold, the target device is controlled to execute the corrected instruction data.

2. The method according to claim 1, characterized in that, The step of identifying the instruction type of the instruction data using a static dictionary and user preference data includes: Read the control commands and control parameters from the instruction data; Search the static dictionary for a reasonable range of parameters corresponding to the control command; If the control parameters are not within a reasonable range, the instruction type of the instruction data is determined to be an abnormal instruction. If the control parameters are within a reasonable range, determine whether the instruction data matches the user preference data; If the instruction data matches the user preference data, the instruction type of the instruction data is determined to be a normal instruction; If the number of instructions does not match the user preference data, the instruction type of the instruction data is determined to be a suspicious instruction.

3. The method according to claim 2, characterized in that, The method further includes: If the instruction type of the instruction data is an abnormal instruction, the target device is prohibited from executing the instruction data; If the instruction type of the instruction data is a normal instruction, the target device is controlled to execute the instruction data.

4. The method according to claim 1, characterized in that, The step of modifying the instruction data using a dynamic dictionary and the user preference data includes: The instruction parameters in the instruction data are corrected so that the corrected instruction data conforms to the user preference data and does not belong to the blacklist instruction data in the dynamic dictionary.

5. The method according to claim 1, characterized in that, The step of determining the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device includes: The device status data, the external environment data, and the corrected instruction data are input into a pre-trained digital twin model so that the confidence level of the corrected instruction data can be determined using the digital twin model based on the device status data, the external environment data, and the corrected instruction data.

6. The method according to claim 1, characterized in that, After controlling the target device to execute the modified instruction data, the method further includes: Collect device operation data and operational behavior data of the target device after executing the modified instruction data; If the device operation data meets the preset safe operation conditions and the operation behavior data is empty, the user preference data is updated according to the instruction data; If the device operation data does not meet the safe operation conditions or the operation behavior data is not empty, the instruction data will be updated to the blacklist instruction data in the dynamic dictionary.

7. An instruction execution device, characterized in that, include: The interception module is used to intercept instruction data sent to the target device; The identification module is used to identify the instruction type of the instruction data using a static dictionary and user preference data; The correction module is used to correct the instruction data using a dynamic dictionary and the user preference data when the instruction type of the instruction data is a suspicious instruction. The determination module is used to determine the confidence level of the corrected instruction data based on the device status data and external environment data corresponding to the target device; An execution module is configured to control the target device to execute the modified instruction data when the confidence level of the modified instruction data is greater than a preset confidence level threshold.

8. An air conditioning system, characterized in that, The air conditioning system uses the instruction execution method according to any one of claims 1-6.

9. An instruction execution device, characterized in that, include: At least one communication interface; At least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; At least one memory connected to the at least one bus, wherein the processor is configured to execute an instruction execution program stored in the memory to implement the instruction execution method of any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are executed to implement the instruction execution method according to any one of claims 1-6.

Images