Inspection parameter optimization method and device based on cloud edge collaboration

Abstract

The invention discloses an inspection parameter optimization method and device based on cloud side cooperation, and relates to the technical field of inspection parameter optimization, and the method comprises the steps: receiving an inspection instruction sent by a cloud, determining an inspection target according to the inspection instruction, determining target terminal equipment according to the inspection target, obtaining a communication code of the target terminal equipment, and transmitting the communication code to the cloud side cooperation. Establishing an exclusive data transmission channel according to the communication code, receiving state information uploaded by the target terminal device from the exclusive data transmission channel, determining an operation state of the target terminal device according to the state information, and when the operation state is an abnormal state, determining an abnormal amplitude of the operation state, and generating an optimization parameter from the abnormal amplitude through a parameter optimization model, and optimizing the operation parameter of the target terminal based on the optimization parameter. Through the above mode, remote inspection of a plurality of terminal devices at different positions can be realized, and parameters of each terminal device can be optimized.

Description

Technical Field

[0001] The present application relates to the technical field of inspection parameter optimization, and in particular to an inspection parameter optimization method and device based on cloud-edge collaboration. Background Art

[0002] In modern industry, the stable operation of distribution network equipment is crucial to the economy and safety of the power system. Traditional manual inspection methods are difficult to meet the requirements of real-time and accuracy, so a more efficient intelligent inspection method is needed. The commonly used method is to use drones to inspect distribution network equipment in a visual way. This method can detect a single distribution network device, but it cannot detect multiple distribution network devices at the same time, and it is impossible to perform unified inspections on distribution network equipment, which is not conducive to the overall management of distribution network equipment.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a patrol parameter optimization method and device based on cloud-edge collaboration, aiming to solve the technical problem in the prior art that it is impossible to coordinate patrol inspections and parameter optimization of multiple distribution network devices.

[0005] To achieve the above objectives, the present application provides a method for optimizing inspection parameters based on cloud-edge collaboration, the method comprising: Receive an inspection instruction sent by the cloud, determine an inspection target according to the inspection instruction, and determine a target terminal device according to the inspection target; Acquire a communication code of the target terminal device, establish a dedicated data transmission channel according to the communication code, and receive status information uploaded by the target terminal device from the dedicated data transmission channel; Determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state; The abnormal amplitude is used to generate optimization parameters through a parameter optimization model, and the operating parameters of the target terminal are optimized based on the optimization parameters.

[0006] In one embodiment, the step of generating optimization parameters from the abnormal amplitude through a parameter optimization model, and optimizing the operating parameters of the target terminal based on the optimization parameters includes: Grouping the abnormal amplitudes, determining the groups of the abnormal amplitudes, and determining the input layer of the parameter optimization model according to the groups; Inputting the abnormal amplitude into the hidden layer through the input layer, and determining the unit weight of each neuron in the hidden layer; Determine an initial optimization parameter according to the abnormal amplitude, and weight the initial optimization parameter based on the unit weight to obtain an optimization parameter; The operating parameters of the target terminal are optimized based on the optimization parameters.

[0007] In one embodiment, the step of determining the initial optimization parameters according to the abnormal amplitude, and weighting the initial optimization parameters based on the unit weights to obtain the optimization parameters comprises: Obtain an abnormal amplitude parameter optimization table, and determine corresponding initial optimization parameters by traversing the abnormal amplitude parameter optimization table based on the abnormal amplitude; Determine an optimization strength according to the abnormal amplitude, and update the unit weight according to the optimization strength and the hidden layer dimension to obtain an optimization weight; The initial optimization parameters are weighted by the optimization weights to obtain optimization parameters.

[0008] In one embodiment, after the step of optimizing the operating parameters of the target terminal based on the optimization parameters, the method further includes: Determining an optimized operating state of the target terminal after the operating parameter optimization; Determine a state gain according to the optimized operating state and the operating state, and determine an optimized abnormal amplitude according to the optimized operating state and the standard operating state; When the optimized abnormal amplitude is a preset abnormal optimized amplitude, determining the state gain type; When the state gain type is a forward gain, the optimization parameter is determined as a reinforcement factor, and a reinforcement discount factor is determined according to the optimization abnormal amplitude, and the reinforcement factor and the reinforcement discount factor are used to determine the next optimization intensity; The optimization parameters are updated according to the next optimization intensity until the operating parameters of the target terminal are within a preset standard threshold.

[0009] In one embodiment, after the step of determining the state gain type when the optimized abnormal amplitude is a preset abnormal optimized amplitude, the method further includes: When the state gain type is a negative gain, the optimization parameter is negated, and the negated optimization parameter is determined as a reinforcement factor; determining a reinforcement discount factor according to the optimized abnormal amplitude and the abnormal amplitude; The comprehensive enhancement factor obtained by combining the enhancement factor and the enhancement discount factor is determined as the next optimization intensity.

[0010] In one embodiment, the step of determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state includes: Splitting the state information to obtain performance parameters of each operating dimension in the target terminal device; Determining standard performance parameters of the operating dimensions respectively; Determining a performance difference of the operating dimension according to the performance parameter and the standard performance parameter; constructing a parameter matrix based on the operating dimension, initializing the parameter matrix, and writing the performance difference into the parameter matrix; Determine a following relationship between elements in the parameter matrix, and determine the operating state of the target terminal device based on a state following function corresponding to the following relationship; When the operating state is an abnormal state, mapping the performance difference to the parameter matrix to obtain an abnormal amplitude matrix; The abnormal amplitude is determined according to the eigenvalue of the abnormal amplitude matrix.

[0011] In one embodiment, the steps of receiving an inspection instruction sent by the cloud, determining an inspection target according to the inspection instruction, and determining a target terminal device according to the inspection target include: Receive the inspection command sent by the cloud, unpack the inspection command, and obtain the command frame; Traversing the instruction frame to determine a key frame of the instruction frame; Taking the key frame as a segmentation point, extracting the inspection task of the instruction frame at the segmentation point; The inspection task is identified to obtain a device code, and a target terminal device is determined according to the device code.

[0012] In one embodiment, the steps of obtaining a communication code of the target terminal device, establishing a dedicated data transmission channel according to the communication code, and receiving status information uploaded by the target terminal device from the dedicated data transmission channel include: Obtaining device information of the target terminal device and reading a communication code of the device information; Generate a temporary communication key pair according to the inspection instruction and the communication code; Sending the temporary communication key pair to the target terminal device, and receiving feedback information from the target terminal device after receiving the temporary communication key pair; An exclusive data transmission channel is established based on the feedback information, and status information processed with a temporary communication key is received based on the exclusive data transmission channel.

[0013] In one embodiment, the step of generating a temporary communication key pair according to the inspection instruction and the communication code includes: Determine an inspection task according to the inspection instruction, and generate verification information according to the inspection task; Interpolate the verification information and the communication code frame by frame to obtain a temporary communication code; Obtaining timestamp information of the inspection instruction, and generating failure timestamp information based on the timestamp information; Combining the expiration timestamp information with the temporary communication code to obtain a temporary encryption handle; Perform an inverse operation on the temporary encryption handle to obtain a temporary decryption handle; A temporary communication key pair is generated according to the temporary encryption handle and the temporary decryption handle.

[0014] In addition, to achieve the above purpose, the present application also proposes a patrol parameter optimization device based on cloud-edge collaboration, and the patrol parameter optimization device based on cloud-edge collaboration includes: An instruction exchange module is used to receive an inspection instruction sent by the cloud, determine an inspection target according to the inspection instruction, and determine a target terminal device according to the inspection target; A channel establishment module, used to obtain a communication code of the target terminal device, establish a dedicated data transmission channel according to the communication code, and receive status information uploaded by the target terminal device from the dedicated data transmission channel; A state determination module, configured to determine the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determine the abnormal amplitude of the operating state; The parameter optimization module is used to generate optimization parameters from the abnormal amplitude through a parameter optimization model, and optimize the operating parameters of the target terminal based on the optimization parameters.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a patrol parameter optimization device based on cloud-edge collaboration, and the patrol parameter optimization device based on cloud-edge collaboration includes: a memory, a processor, and a computer program stored in the memory and runnable on the processor, and the computer program is configured to implement the steps of the patrol parameter optimization method based on cloud-edge collaboration as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the inspection parameter optimization method based on cloud-edge collaboration as described above are implemented.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the inspection parameter optimization method based on cloud-edge collaboration as described above.

[0018] The present application provides a method for optimizing inspection parameters based on cloud-edge collaboration, which receives an inspection instruction sent from the cloud, determines the inspection target according to the inspection instruction, determines the target terminal device according to the inspection target, obtains the communication code of the target terminal device, establishes an exclusive data transmission channel according to the communication code, receives the status information uploaded by the target terminal device from the exclusive data transmission channel, determines the operating status of the target terminal device according to the status information, and when the operating status is an abnormal state, determines the abnormal amplitude of the operating status, generates optimization parameters through a parameter optimization model for the abnormal amplitude, and optimizes the operating parameters of the target terminal based on the optimization parameters. Through the above method, it is possible to remotely inspect multiple terminal devices in different locations and optimize the parameters of each terminal device. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a flow chart of Embodiment 1 of the inspection parameter optimization method based on cloud-edge collaboration of the present application; Figure 2 A schematic diagram of cloud-edge collaboration of a power system according to an embodiment of an inspection parameter optimization method based on cloud-edge collaboration of the present application; Figure 3 A schematic diagram of a parameter optimization process of an embodiment of an inspection parameter optimization method based on cloud-edge collaboration of the present application; Figure 4 A schematic diagram of reinforcement learning for an embodiment of an inspection parameter optimization method based on cloud-edge collaboration of the present application; Figure 5 This is a schematic diagram of the module structure of the inspection parameter optimization device based on cloud-edge collaboration in an embodiment of the present application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the inspection parameter optimization method based on cloud-edge collaboration in an embodiment of the present application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of the embodiment of the present application is: receiving an inspection instruction sent from the cloud, determining an inspection target according to the inspection instruction, and determining a target terminal device according to the inspection target; obtaining a communication code of the target terminal device, establishing an exclusive data transmission channel according to the communication code, and receiving status information uploaded by the target terminal device from the exclusive data transmission channel; determining the operating status of the target terminal device according to the status information, and when the operating status is an abnormal state, determining the abnormal amplitude of the operating status; generating optimization parameters from the abnormal amplitude through a parameter optimization model, and optimizing the operating parameters of the target terminal based on the optimization parameters.

[0026] At present, the traditional manual inspection method is difficult to meet the requirements of real-time and accuracy, so a more efficient intelligent inspection method is needed. The commonly used method is to use drones to inspect distribution network equipment in a visual way. This method can detect a single distribution network device, but it cannot detect multiple distribution network devices at the same time, and it is impossible to perform unified inspections on distribution network equipment, which is not conducive to the overall management of distribution network equipment.

[0027] The present application provides a solution, which receives an inspection instruction sent from the cloud, determines the inspection target according to the inspection instruction, determines the target terminal device according to the inspection target, obtains the communication code of the target terminal device, establishes a dedicated data transmission channel according to the communication code, receives the status information uploaded by the target terminal device from the dedicated data transmission channel, determines the operating state of the target terminal device according to the status information, and when the operating state is an abnormal state, determines the abnormal amplitude of the operating state, generates optimization parameters through a parameter optimization model for the abnormal amplitude, and optimizes the operating parameters of the target terminal based on the optimization parameters. Through the above method, it is possible to remotely inspect multiple terminal devices in different locations and optimize the parameters of each terminal device.

[0028] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions, an inspection parameter optimization device based on cloud-edge collaboration, etc., and this embodiment does not specifically limit this. The following takes the inspection parameter optimization device based on cloud-edge collaboration as an example to illustrate this embodiment and the following embodiments.

[0029] The present application embodiment provides a method for optimizing inspection parameters based on cloud-edge collaboration, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the inspection parameter optimization method based on cloud-edge collaboration in this application.

[0030] In this embodiment, the inspection parameter optimization method based on cloud-edge collaboration includes steps S10 to S40: Step S10, receiving an inspection instruction sent from the cloud, determining an inspection target according to the inspection instruction, and determining a target terminal device according to the inspection target.

[0031] It should be noted that in the power system, in order to facilitate centralized inspection and maintenance of terminal equipment in each power system, cloud-edge collaboration can be used for overall management, in which the cloud can establish a control connection relationship with multiple edge-side devices. Figure 2 , Figure 2 The diagram is a cloud-edge collaboration diagram for the power system. Each edge device can be inspected and regulated, and each edge device can also inspect and regulate multiple terminal devices, thereby enabling the cloud to remotely inspect various terminal devices scattered in different areas.

[0032] In addition, it should be noted that the inspection instructions can be issued by the cloud and edge devices, where the inspection instructions issued by the cloud are received by the edge devices. The inspection instructions issued by the edge devices are received by the terminal devices. The target terminal devices are the terminal devices that need to perform the inspection tasks, and the target terminal devices can feedback their own working parameters according to the inspection instructions.

[0033] It is understandable that when the cloud sends an inspection instruction, the cloud can generate an inspection instruction based on the terminal device that needs to be inspected this time. Since the inspection instruction of the cloud is sent to the edge device and forwarded to the target terminal device by the edge device, it is necessary to obtain the list of target terminal devices linked to each edge device, and use the target terminal device that needs to optimize the inspection parameters this time as the search key value to determine the corresponding edge device. Then, the inspection task of the target terminal device is used as a data packet to generate an inspection instruction sent to the edge device. After the edge device receives the inspection instruction sent by the cloud, it can split the inspection instruction, separate the inspection task of the target terminal device, and generate an inspection instruction from the inspection task, and then send it to the target terminal device.

[0034] In a feasible implementation manner, the steps of receiving an inspection instruction sent by the cloud, determining an inspection target according to the inspection instruction, and determining a target terminal device according to the inspection target include: Receive the inspection command sent by the cloud, unpack the inspection command, and obtain the command frame; Traversing the instruction frame to determine a key frame of the instruction frame; Taking the key frame as a segmentation point, extracting the inspection task of the instruction frame at the segmentation point; The inspection task is identified to obtain a device code, and a target terminal device is determined according to the device code.

[0035] In a specific implementation, after the edge-side device receives the inspection instruction sent from the cloud, it can unpack the inspection instruction to obtain an instruction frame. The instruction frame includes the type of device that needs to be inspected, the time period of the inspection, and other special requirements. After obtaining the instruction frame, the instruction frame can be traversed. Since the beginning and end of each task instruction have an identification mark, when the instruction frame is traversed, when the start mark and the end mark are detected, the data frame corresponding to the start mark and the end mark can be determined as a key frame. The key frame corresponds to the task instruction information in the inspection task, and the instruction frame can be divided into multiple data frames with the key frame as the dividing point. The inspection task of the instruction frame can be determined according to the key frame type of each data frame. At the same time, the target device that performs the current inspection task can be included in the inspection task, and the device code of the target terminal device can be obtained by table lookup, and the target terminal device can be determined by the device code.

[0036] Step S20, obtaining a communication code of the target terminal device, establishing a dedicated data transmission channel according to the communication code, and receiving status information uploaded by the target terminal device from the dedicated data transmission channel.

[0037] It should be noted that the communication code of the target device is similar to the MAC address of the target device and has device uniqueness. When the target device is added to the power system, it can be determined according to the sub-code of the current access edge device. The communication code of the device can be represented by a binary number, and the specific byte length is set by the quasi-access device of the power system. The exclusive data channel is a dedicated channel for inspection data. When the inspection task needs to be performed, the edge side device and the target terminal device are established as the two ends of the data channel. The edge side device and the target terminal device are each other's data sender and data receiver. The status information includes other information such as current information, voltage information, temperature information, power information, etc. of the target terminal device.

[0038] In a specific implementation, the communication code of the target terminal device is determined according to the current access status of the edge device when the target terminal device is connected to the current power system. For example, the communication code of the current edge device is 5C, and its corresponding binary code is 01011100. If the current edge device has been connected to 123 terminal devices, and the current device will become the 124th connected terminal device after joining, then its corresponding binary code is 01111100, and its communication code can be expressed as 0101110001111100. Then, the communication code can be used to establish an exclusive data transmission channel with the target communication terminal, and receive the status information uploaded by the target terminal device from the exclusive data transmission channel.

[0039] In a feasible implementation manner, the steps of obtaining the communication code of the target terminal device, establishing a dedicated data transmission channel according to the communication code, and receiving the status information uploaded by the target terminal device from the dedicated data transmission channel include: Obtaining device information of the target terminal device and reading a communication code of the device information; Generate a temporary communication key pair according to the inspection instruction and the communication code; Sending the temporary communication key pair to the target terminal device, and receiving feedback information from the target terminal device after receiving the temporary communication key pair; An exclusive data transmission channel is established based on the feedback information, and status information processed with a temporary communication key is received based on the exclusive data transmission channel.

[0040] In a specific implementation, when the target inspection instruction is determined according to the inspection instruction, and the target terminal device is determined according to the target inspection instruction, the communication code corresponding to the device information can be read according to the device information of the target terminal device, and then a temporary communication key pair is generated according to the communication code and the inspection instruction. The temporary communication key pair includes a public key and a private key. After the temporary communication key pair is generated, the temporary communication key pair can be sent to the target terminal device. After the target device receives the public key, it will use it to encrypt the status information to be sent. The edge side device detects the data transmission in the exclusive data transmission channel in real time. When receiving the data transmission, it can receive the feedback information of the target terminal device after receiving the temporary communication key pair. After receiving the feedback information of the target terminal device and confirming that both parties can correctly use the temporary communication key pair for encryption and decryption operations, an exclusive data transmission channel can be formally established. This channel ensures that all subsequent data exchanges are encrypted, thereby protecting the confidentiality and integrity of the data. After the exclusive data transmission channel is established, the target terminal device will begin to regularly send its operating status information (such as voltage, current, temperature, etc.). This information will be encrypted using the previously agreed public key before sending. The edge device uses the corresponding private key to decrypt the received information to obtain the original device status data.

[0041] In a feasible implementation manner, the step of generating a temporary communication key pair according to the inspection instruction and the communication code includes: Determine an inspection task according to the inspection instruction, and generate verification information according to the inspection task; Interpolate the verification information and the communication code frame by frame to obtain a temporary communication code; Obtaining timestamp information of the inspection instruction, and generating failure timestamp information based on the timestamp information; Combining the expiration timestamp information with the temporary communication code to obtain a temporary encryption handle; Perform an inverse operation on the temporary encryption handle to obtain a temporary decryption handle; A temporary communication key pair is generated according to the temporary encryption handle and the temporary decryption handle.

[0042] In a specific implementation, the specific inspection task details are clarified according to the received inspection instruction, including the specific parameters to be measured (such as the voltage fluctuation range). Based on these task details, a verification information is generated to ensure the identity and data integrity of the communicating parties in subsequent steps. The verification information can be a hash value or a check code generated based on the inspection task content. The generated verification information is then combined with the communication code of the target device for processing. This embodiment adopts a frame interpolation method to insert relevant information of the communication code between the data frames of the verification information, or vice versa, thereby generating a new combination-temporary communication code. This process adds an additional layer of security so that only devices with the correct communication code can parse the original verification information. The timestamp information in the inspection instruction is obtained, that is, the time when the instruction is issued, and then an expiration timestamp is generated based on this timestamp. The expiration timestamp defines the validity period of this communication. For example, if the validity period is set to 2 hours, the expiration timestamp is the current time plus 2 hours. This is done to limit the life of the temporary key pair and increase security. The generated expiration timestamp information is combined with the temporary communication code to form a temporary encryption handle (TEH). As part of the encryption process, the handle contains information about when to stop accepting communications identified by the handle, further enhancing security measures. For decryption, the above-obtained temporary encryption handle can be inversely operated to generate a temporary decryption handle (TDH) to decrypt the data encrypted by TEH. Finally, a temporary communication key pair is generated using the temporary encryption handle (TEH) and the temporary decryption handle (TDH). This pair of keys will be used for all encrypted communications during this inspection mission. The public key part (usually TEH) will be sent to the target terminal device, while the private key part (TDH) will be retained on the edge device and the cloud to decrypt the data received from the target device.

[0043] Step S30, determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state.

[0044] It should be noted that the operating status refers to the indicator data corresponding to various working parameters of the target terminal device during operation. Each indicator data has a corresponding threshold. When the threshold is exceeded, it can be determined as an abnormal state, and the difference between the abnormal state and the threshold time is the abnormal amplitude.

[0045] In the specific implementation, the operating parameters of the target terminal device, such as current value, voltage value, temperature value, etc., are obtained based on the status information. Each operating parameter has its corresponding threshold value, and each operating parameter is compared with the corresponding threshold value. If any operating parameter is not within its threshold value, the current target terminal device can be considered to be in an abnormal state. Next, the specific abnormal amplitude needs to be calculated to assess the severity of the problem. Taking the current value as an example, the abnormal amplitude corresponding to the current is ,in, ,in, is the maximum current value, is the standard current value of the threshold. Similarly, the calculation method of the voltage abnormal amplitude and the temperature abnormal amplitude is similar to the calculation method of the current abnormal amplitude.

[0046] In a feasible implementation manner, the step of determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state includes: Splitting the state information to obtain performance parameters of each operating dimension in the target terminal device; Determining standard performance parameters of the operating dimensions respectively; Determining a performance difference of the operating dimension according to the performance parameter and the standard performance parameter; constructing a parameter matrix based on the operating dimension, initializing the parameter matrix, and writing the performance difference into the parameter matrix; Determine a following relationship between elements in the parameter matrix, and determine the operating state of the target terminal device based on a state following function corresponding to the following relationship; When the operating state is an abnormal state, mapping the performance difference to the parameter matrix to obtain an abnormal amplitude matrix; The abnormal amplitude is determined according to the eigenvalue of the abnormal amplitude matrix.

[0047] In the specific implementation, first, we need to split the status information received from the target terminal device into performance parameters of different operating dimensions. Assuming that we focus on the three dimensions of current, voltage and temperature, the status information can be split into , , The actual measured current, voltage, and temperature values ​​are as follows. Next, standard performance parameters need to be determined for each operating dimension. These parameters are usually based on the design specifications of the equipment or historical normal operating data. , , , respectively, the rated current, rated operating voltage and maximum safe operating temperature. The performance difference of each operating dimension is calculated based on the performance parameters and the standard performance parameters. The calculation formula is:

[0048]

[0049]

[0050] Then create a parameter matrix , which contains all running dimensions and their performance differences. Assume that the matrix size is , for the three dimensions in this example, we can construct a .

[0051]

[0052] The tracking relationship refers to the degree to which different performance parameters influence each other. For example, excessive temperature may cause increased current or unstable voltage. This relationship can be determined by analyzing historical data of each parameter or using expert knowledge. The overall operating status is evaluated based on the tracking relationship. If any one or more performance differences exceed the preset threshold, the device is considered to be in an abnormal state. When it is determined that the device is in an abnormal state, the performance difference is mapped to the parameter matrix to form an abnormal amplitude matrix .

[0053]

[0054] The absolute value is taken here to uniformly represent the degree of abnormality, regardless of the direction of deviation.

[0055] Finally, the specific abnormal amplitude is determined according to the abnormal amplitude matrix A. This can be done through mathematical operations, such as finding the maximum value, average value or other statistics of the matrix elements to quantify the degree of abnormality. For example, the maximum abnormal amplitude can be expressed as:

[0056] Or calculate the weighted average of the abnormal amplitudes, where the weights can be set based on the impact of each dimension on the device operation:

[0057] in, are the weight coefficients of current, voltage and temperature respectively.

[0058] Step S40: Generate optimization parameters from the abnormal amplitude through a parameter optimization model, and optimize the operating parameters of the target terminal based on the optimization parameters.

[0059] It should be noted that the parameter optimization model is a deep learning reinforcement model, which can continuously adjust the optimization weights of parameter optimization according to the parameter optimization records of the target terminal device, thereby reducing the number of parameter optimization times.

[0060] In specific implementation, in power systems, especially for key equipment such as transformers, using deep learning reinforcement models to optimize operating parameters is an advanced and effective method. In this way, not only can the abnormal state of the equipment and its amplitude be identified, but also the optimization parameters can be further generated to adjust the operating conditions of the equipment, thereby improving efficiency, extending equipment life and reducing the risk of failure. First, after the abnormal amplitude is determined, it can be passed as input to a pre-trained deep learning reinforcement model. This model has been learned through a large amount of historical data, including data under normal operating conditions and data under various abnormal conditions. Deep learning reinforcement models usually consist of two parts: a policy network and a value network. The policy network is used to decide which action to take (such as adjusting current, voltage or temperature settings), while the value network evaluates the goodness of taking a specific action under the current state. For applications in power systems, the model can be trained to minimize energy loss, maximize equipment reliability, or balance the relationship between the two. Among them, the input layer can receive the abnormal amplitude obtained from the target terminal device; the hidden layer: processes the input data through a multi-layer neural network, extracts features and makes decisions; the output layer: generates optimized operating parameter recommendations, such as new set points or adjustment coefficients.

[0061] Based on the output of the model, a set of optimization parameters is generated. These parameters are designed to correct the detected abnormal conditions and optimize the overall performance of the equipment. For example, if the abnormal amplitude shows that the current is too high, the model may recommend reducing the load or adjusting the power of the cooling system; if the voltage is unstable, it may suggest reconfiguring the voltage regulator. The generated optimization parameters are applied to the actual control system of the target terminal equipment. The automatic control system can directly adjust the working state of the equipment according to the model recommendations, or provide them to the operator as a reference for manual adjustment.

[0062] In a feasible implementation manner, the step of generating optimization parameters from the abnormal amplitude through a parameter optimization model, and optimizing the operating parameters of the target terminal based on the optimization parameters includes: Grouping the abnormal amplitudes, determining the groups of the abnormal amplitudes, and determining the input layer of the parameter optimization model according to the groups; Inputting the abnormal amplitude into the hidden layer through the input layer, and determining the unit weight of each neuron in the hidden layer; Determine an initial optimization parameter according to the abnormal amplitude, and weight the initial optimization parameter based on the unit weight to obtain an optimization parameter; The operating parameters of the target terminal are optimized based on the optimization parameters.

[0063] The step of determining the initial optimization parameters according to the abnormal amplitude and weighting the initial optimization parameters based on the unit weights to obtain the optimization parameters comprises: Obtain an abnormal amplitude parameter optimization table, and determine corresponding initial optimization parameters by traversing the abnormal amplitude parameter optimization table based on the abnormal amplitude; Determine an optimization strength according to the abnormal amplitude, and update the unit weight according to the optimization strength and the hidden layer dimension to obtain an optimization weight; The initial optimization parameters are weighted by the optimization weights to obtain optimization parameters.

[0064] In a specific implementation, the abnormal amplitudes are classified according to their nature or impact. The abnormal amplitudes of current, voltage and temperature can be classified into different groups, or grouped according to the degree of deviation from the standard value (such as slight, moderate, severe). This embodiment groups the current, voltage and temperature. The abnormal amplitude of each group is then used as part of the input layer to ensure that the model can receive all relevant abnormal information. The input layer may include three input nodes Then the input can be expressed as , each neuron in the hidden layer has a weight vector associated with it, which determines how the input data is processed and passed to the next layer. During the training process, these weights are constantly adjusted to minimize the model prediction error. Specifically, for a given abnormal amplitude input , the output of the jth neuron in the hidden layer can be expressed as:

[0065] in, is the weight connecting input i and hidden layer neuron j, is the bias term, is the ReLU nonlinear activation function.

[0066] Based on the abnormal amplitude and the current device status, a set of optimization parameters is initially determined. This set of parameters may be the default settings based on the rules, or the successful solutions in previous similar situations. Then, the initial optimization parameters are weighted using the unit weights of each neuron in the hidden layer to obtain the final optimization parameters. For example, if the initial optimization parameters are , then the weighted optimization parameter P can be calculated as follows:

[0067] in, It is a matrix composed of hidden layer unit weights, which reflects the impact of different anomaly dimensions on the optimization decision.

[0068] Finally, the actual operating parameters of the target terminal equipment are adjusted using the obtained optimization parameters P. For example, if the optimization parameters indicate that the operating temperature of the transformer needs to be reduced, the corresponding cooling system power will be increased; if it is recommended to adjust the voltage, the set point of the voltage controller will be adjusted accordingly.

[0069] The step of determining the initial optimization parameters according to the abnormal amplitude and weighting the initial optimization parameters based on the unit weights to obtain the optimization parameters comprises: Obtain an abnormal amplitude parameter optimization table, and determine corresponding initial optimization parameters by traversing the abnormal amplitude parameter optimization table based on the abnormal amplitude; Determine an optimization strength according to the abnormal amplitude, and update the unit weight according to the optimization strength and the hidden layer dimension to obtain an optimization weight; The initial optimization parameters are weighted by the optimization weights to obtain optimization parameters.

[0070] In a feasible implementation manner, after the step of optimizing the operating parameters of the target terminal based on the optimization parameters, the method further includes: Determining an optimized operating state of the target terminal after the operating parameter optimization; Determine a state gain according to the optimized operating state and the operating state, and determine an optimized abnormal amplitude according to the optimized operating state and the standard operating state; When the optimized abnormal amplitude is a preset abnormal optimized amplitude, determining the state gain type; When the state gain type is a forward gain, the optimization parameter is determined as a reinforcement factor, and a reinforcement discount factor is determined according to the optimization abnormal amplitude, and the reinforcement factor and the reinforcement discount factor are used to determine the next optimization intensity; Updating the optimization parameters according to the next optimization intensity until the operating parameters of the target terminal are within a preset standard threshold; When the state gain type is a negative gain, the optimization parameter is negated, and the negated optimization parameter is determined as a reinforcement factor; determining a reinforcement discount factor according to the optimized abnormal amplitude and the abnormal amplitude; The comprehensive enhancement factor obtained by combining the enhancement factor and the enhancement discount factor is determined as the next optimization intensity.

[0071] In the specific implementation, refer to Figure 3 , Figure 3 The following is a schematic diagram of the parameter optimization process. Based on the optimized operating parameters (e.g., adjusted current, voltage, and temperature settings), the optimized operating state of the equipment is determined. This step includes collecting the performance parameters in the new state and comparing them with the previous abnormal state. Next, the state gain before and after optimization is calculated. The state gain can be determined by comparing the performance difference before and after optimization. Assume that the original performance difference is , the performance difference after optimization is , then the state gain It can be expressed as:

[0072] if , indicating that the optimization has brought about an improvement (positive gain); conversely, it may be that the optimization has not met expectations or has led to a deterioration (negative gain). At the same time, it is also necessary to compare the optimized operating state with the standard operating state to determine the abnormal amplitude after optimization. , ,in, is the actual measured value after optimization, These are standard performance parameters.

[0073] When the state gain is positive, it means that the optimization measures are effective. At this time, the currently used optimization parameters are regarded as the reinforcement factor RF, and the reinforcement discount factor DF is determined according to the optimization abnormal amplitude. The reinforcement discount factor is used to adjust the intensity of the next optimization to avoid over-adjustment. The simplified formula can be expressed as:

[0074] Therefore, the next optimization intensity OI is defined as:

[0075] If the state gain is negative, it means that the optimization direction may be wrong. At this time, it is necessary to negate the optimization parameter as a new enhancement factor, and also determine the enhancement discount factor based on the optimization abnormal amplitude. For example, if the original optimization parameter is OP, the new enhancement factor RF′ is:

[0076] The same formula is then used to calculate the reinforcement discount factor DF and the next optimization intensity OI.

[0077] Reference Figure 4 , Figure 4This is a schematic diagram of reinforcement learning. Regardless of whether it is a positive or negative gain situation, the optimization parameters need to be updated according to the calculated next optimization intensity OI, and the above process is repeated until all key performance indicators fall within the preset standard threshold. This process is usually implemented through iterations, and each iteration re-evaluates the state gain and optimization abnormal amplitude based on the latest operating data, thereby dynamically adjusting the optimization strategy.

[0078] This embodiment provides a method for optimizing inspection parameters based on cloud-edge collaboration, which receives an inspection instruction sent by the cloud, determines an inspection target according to the inspection instruction, determines a target terminal device according to the inspection target, obtains a communication code of the target terminal device, establishes an exclusive data transmission channel according to the communication code, receives status information uploaded by the target terminal device from the exclusive data transmission channel, determines the operating status of the target terminal device according to the status information, and when the operating status is an abnormal state, determines the abnormal amplitude of the operating status, generates optimization parameters through a parameter optimization model for the abnormal amplitude, and optimizes the operating parameters of the target terminal based on the optimization parameters. Through the above method, it is possible to remotely inspect multiple terminal devices in different locations and optimize the parameters of each terminal device.

[0079] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the inspection parameter optimization method based on cloud-edge collaboration of the present application. More forms of simple transformations based on this technical concept are all within the scope of protection of the present application.

[0080] This application also provides a patrol parameter optimization device based on cloud-edge collaboration, please refer to Figure 5 , the inspection parameter optimization device based on cloud-edge collaboration includes: The instruction exchange module 10 is used to receive the inspection instruction sent by the cloud, determine the inspection target according to the inspection instruction, and determine the target terminal device according to the inspection target; The channel establishment module 20 is used to obtain the communication code of the target terminal device, establish a dedicated data transmission channel according to the communication code, and receive the status information uploaded by the target terminal device from the dedicated data transmission channel; A state determination module 30, configured to determine the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determine the abnormal amplitude of the operating state; The parameter optimization module 40 is used to generate optimization parameters from the abnormal amplitude through a parameter optimization model, and optimize the operating parameters of the target terminal based on the optimization parameters.

[0081] In a feasible implementation manner, the parameter optimization module 40 is also used to group the abnormal amplitudes, determine the groups of the abnormal amplitudes, and determine the input layer of the parameter optimization model according to the groups; input the abnormal amplitudes to the hidden layer through the input layer, and determine the unit weights of each neuron in the hidden layer; determine initial optimization parameters according to the abnormal amplitudes, weight the initial optimization parameters based on the unit weights to obtain optimization parameters; and optimize the operating parameters of the target terminal based on the optimization parameters.

[0082] In a feasible implementation manner, the parameter optimization module 40 is also used to obtain an abnormal amplitude parameter optimization table, and determine corresponding initial optimization parameters by traversing the abnormal amplitude parameter optimization table based on the abnormal amplitude; determine the optimization strength according to the abnormal amplitude, and update the unit weight according to the optimization strength and the hidden layer dimension to obtain the optimization weight; and weight the initial optimization parameter with the optimization weight to obtain the optimization parameter.

[0083] In a feasible implementation, the parameter optimization module 40 is also used to determine the optimized operating state of the target terminal after the operating parameters are optimized; determine the state gain according to the optimized operating state and the operating state, and determine the optimized abnormal amplitude according to the optimized operating state and the standard operating state; when the optimized abnormal amplitude is a preset abnormal optimization amplitude, determine the state gain type; when the state gain type is a positive gain, determine the optimization parameter as an enhancement factor, and determine the enhancement discount factor according to the optimized abnormal amplitude, and determine the next optimization intensity with the enhancement factor and the enhancement discount factor; update the optimization parameter according to the next optimization intensity until the operating parameter of the target terminal is within the preset standard threshold.

[0084] In a feasible implementation, the parameter optimization module 40 is also used to invert the optimization parameter when the state gain type is a negative gain, and determine the inverted optimization parameter as an enhancement factor; determine an enhancement discount factor based on the optimization abnormal amplitude and the abnormal amplitude; and determine the comprehensive enhancement factor obtained by the enhancement factor and the enhancement discount factor as the next optimization intensity.

[0085] In a feasible implementation, the state determination module 30 is also used to split the state information to obtain performance parameters of each operating dimension in the target terminal device; respectively determine the standard performance parameters of the operating dimension; determine the performance difference of the operating dimension according to the performance parameters and the standard performance parameters; construct a parameter matrix with the operating dimension, initialize the parameter matrix, and write the performance difference into the parameter matrix; determine the following relationship of each element in the parameter matrix, and determine the operating state of the target terminal device based on the state following function corresponding to the following relationship; when the operating state is an abnormal state, map the performance difference to the parameter matrix to obtain an abnormal amplitude matrix; determine the abnormal amplitude according to the eigenvalue of the abnormal amplitude matrix.

[0086] In a feasible implementation, the instruction exchange module 10 is also used to receive inspection instructions sent from the cloud, unpack the inspection instructions to obtain an instruction frame; traverse the instruction frame to determine the key frame of the instruction frame; use the key frame as a segmentation point to extract the inspection task of the instruction frame at the segmentation point; identify the inspection task to obtain a device code, and determine the target terminal device based on the device code.

[0087] In a feasible implementation, the channel establishment module 20 is also used to obtain the device information of the target terminal device and read the communication code of the device information; generate a temporary communication key pair according to the inspection instruction and the communication code; send the temporary communication key pair to the target terminal device, and receive feedback information from the target terminal device after receiving the temporary communication key pair; establish an exclusive data transmission channel based on the feedback information, and receive status information processed by the temporary communication key based on the exclusive data transmission channel.

[0088] In a feasible implementation, the channel establishment module 20 is also used to determine the inspection task according to the inspection instruction, generate verification information according to the inspection task; interpolate the verification information and the communication code frame by frame to obtain a temporary communication code; obtain the timestamp information of the inspection instruction, and generate expiration timestamp information based on the timestamp information; combine the expiration timestamp information with the temporary communication code to obtain a temporary encryption handle; perform inverse operation on the temporary encryption handle to obtain a temporary decryption handle; and generate a temporary communication key pair based on the temporary encryption handle and the temporary decryption handle.

[0089] The inspection parameter optimization device based on cloud-edge collaboration provided by the present application adopts the inspection parameter optimization method based on cloud-edge collaboration in the above-mentioned embodiment, which can solve the technical problem of being unable to coordinate inspections and parameter optimization of multiple distribution network devices. Compared with the prior art, the beneficial effects of the inspection parameter optimization device based on cloud-edge collaboration provided by the present application are the same as the beneficial effects of the inspection parameter optimization method based on cloud-edge collaboration provided by the above-mentioned embodiment, and the other technical features in the inspection parameter optimization device based on cloud-edge collaboration are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0090] The present application provides a patrol parameter optimization device based on cloud-edge collaboration, and the patrol parameter optimization device based on cloud-edge collaboration includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the patrol parameter optimization method based on cloud-edge collaboration in the above-mentioned embodiment one.

[0091] Reference below Figure 6 , which shows a schematic diagram of the structure of a patrol parameter optimization device based on cloud-edge collaboration suitable for implementing the embodiment of the present application. The patrol parameter optimization device based on cloud-edge collaboration in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The inspection parameter optimization device based on cloud-edge collaboration shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0092] like Figure 6As shown, the inspection parameter optimization device based on cloud-edge collaboration may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the inspection parameter optimization device based on cloud-edge collaboration are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the inspection parameter optimization device based on cloud-edge collaboration to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an inspection parameter optimization device based on cloud-edge collaboration with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0093] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0094] The inspection parameter optimization device based on cloud-edge collaboration provided by the present application adopts the inspection parameter optimization method based on cloud-edge collaboration in the above-mentioned embodiment, which can solve the technical problem of inspection parameter optimization based on cloud-edge collaboration. Compared with the prior art, the beneficial effects of the inspection parameter optimization device based on cloud-edge collaboration provided by the present application are the same as the beneficial effects of the inspection parameter optimization method based on cloud-edge collaboration provided by the above-mentioned embodiment, and the other technical features in the inspection parameter optimization device based on cloud-edge collaboration are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0095] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0096] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0097] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the inspection parameter optimization method based on cloud-edge collaboration in the above-mentioned embodiment.

[0098] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0099] The above-mentioned computer-readable storage medium may be included in the inspection parameter optimization device based on cloud-edge collaboration; or it may exist independently without being assembled into the inspection parameter optimization device based on cloud-edge collaboration.

[0100] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the inspection parameter optimization device based on cloud-edge collaboration, the inspection parameter optimization device based on cloud-edge collaboration enables: receiving the inspection instruction sent by the cloud, determining the inspection target according to the inspection instruction, and determining the target terminal device according to the inspection target; obtaining the communication code of the target terminal device, establishing an exclusive data transmission channel according to the communication code, and receiving the status information uploaded by the target terminal device from the exclusive data transmission channel; determining the operating status of the target terminal device according to the status information, and when the operating status is an abnormal state, determining the abnormal amplitude of the operating status; generating optimization parameters from the abnormal amplitude through a parameter optimization model, and optimizing the operating parameters of the target terminal based on the optimization parameters.

[0101] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0102] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0103] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0104] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned inspection parameter optimization method based on cloud-edge collaboration, and can solve the technical problem of inspection parameter optimization based on cloud-edge collaboration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the inspection parameter optimization method based on cloud-edge collaboration provided in the above-mentioned embodiment, and will not be repeated here.

[0105] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned inspection parameter optimization method based on cloud-edge collaboration.

[0106] The computer program product provided in this application can solve the technical problem of patrol parameter optimization based on cloud-edge collaboration. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the patrol parameter optimization method based on cloud-edge collaboration provided in the above embodiment, which will not be repeated here.

[0107] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for optimizing inspection parameters based on cloud-edge collaboration, characterized in that: The inspection parameter optimization method based on cloud-edge collaboration includes: Receive an inspection instruction sent by the cloud, determine an inspection target according to the inspection instruction, and determine a target terminal device according to the inspection target; Acquire a communication code of the target terminal device, establish a dedicated data transmission channel according to the communication code, and receive status information uploaded by the target terminal device from the dedicated data transmission channel; Determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state; The abnormal amplitude is used to generate optimization parameters through a parameter optimization model, and the operating parameters of the target terminal are optimized based on the optimization parameters.

2. The method according to claim 1, characterized in that The step of generating optimization parameters from the abnormal amplitude through a parameter optimization model, and optimizing the operating parameters of the target terminal based on the optimization parameters includes: Grouping the abnormal amplitudes, determining the groups of the abnormal amplitudes, and determining the input layer of the parameter optimization model according to the groups; Inputting the abnormal amplitude into the hidden layer through the input layer, and determining the unit weight of each neuron in the hidden layer; Determine an initial optimization parameter according to the abnormal amplitude, and weight the initial optimization parameter based on the unit weight to obtain an optimization parameter; The operating parameters of the target terminal are optimized based on the optimization parameters.

3. The method according to claim 2, characterized in that The step of determining the initial optimization parameters according to the abnormal amplitude and weighting the initial optimization parameters based on the unit weights to obtain the optimization parameters comprises: Obtain an abnormal amplitude parameter optimization table, and determine corresponding initial optimization parameters by traversing the abnormal amplitude parameter optimization table based on the abnormal amplitude; Determine an optimization strength according to the abnormal amplitude, and update the unit weight according to the optimization strength and the hidden layer dimension to obtain an optimization weight; The initial optimization parameters are weighted by the optimization weights to obtain optimization parameters.

4. The method according to claim 2, characterized in that After the step of optimizing the operating parameters of the target terminal based on the optimization parameters, the method further includes: Determining an optimized operating state of the target terminal after the operating parameter optimization; Determine a state gain according to the optimized operating state and the operating state, and determine an optimized abnormal amplitude according to the optimized operating state and the standard operating state; When the optimized abnormal amplitude is a preset abnormal optimized amplitude, determining the state gain type; When the state gain type is a forward gain, the optimization parameter is determined as a reinforcement factor, and a reinforcement discount factor is determined according to the optimization abnormal amplitude, and the reinforcement factor and the reinforcement discount factor are used to determine the next optimization intensity; The optimization parameters are updated according to the next optimization intensity until the operating parameters of the target terminal are within a preset standard threshold.

5. The method according to claim 4, characterized in that After the step of determining the state gain type when the optimized abnormal amplitude is a preset abnormal optimized amplitude, the method further includes: When the state gain type is a negative gain, the optimization parameter is negated, and the negated optimization parameter is determined as a reinforcement factor; determining a reinforcement discount factor according to the optimized abnormal amplitude and the abnormal amplitude; The comprehensive enhancement factor obtained by combining the enhancement factor and the enhancement discount factor is determined as the next optimization intensity.

6. The method according to claim 1, characterized in that The step of determining the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determining the abnormal amplitude of the operating state comprises: Splitting the state information to obtain performance parameters of each operating dimension in the target terminal device; Determining standard performance parameters of the operating dimensions respectively; Determining a performance difference of the operating dimension according to the performance parameter and the standard performance parameter; constructing a parameter matrix based on the operating dimension, initializing the parameter matrix, and writing the performance difference into the parameter matrix; Determine a following relationship between elements in the parameter matrix, and determine the operating state of the target terminal device based on a state following function corresponding to the following relationship; When the operating state is an abnormal state, mapping the performance difference to the parameter matrix to obtain an abnormal amplitude matrix; The abnormal amplitude is determined according to the eigenvalue of the abnormal amplitude matrix.

7. The method according to claim 1, characterized in that The steps of receiving the inspection instruction sent by the cloud, determining the inspection target according to the inspection instruction, and determining the target terminal device according to the inspection target include: Receive the inspection command sent by the cloud, unpack the inspection command, and obtain the command frame; Traversing the instruction frame to determine a key frame of the instruction frame; Taking the key frame as a segmentation point, extracting the inspection task of the instruction frame at the segmentation point; The inspection task is identified to obtain a device code, and a target terminal device is determined according to the device code.

8. The method according to claim 1, characterized in that The steps of obtaining the communication code of the target terminal device, establishing a dedicated data transmission channel according to the communication code, and receiving the status information uploaded by the target terminal device from the dedicated data transmission channel include: Obtaining device information of the target terminal device and reading a communication code of the device information; Generate a temporary communication key pair according to the inspection instruction and the communication code; Sending the temporary communication key pair to the target terminal device, and receiving feedback information from the target terminal device after receiving the temporary communication key pair; An exclusive data transmission channel is established based on the feedback information, and status information processed with a temporary communication key is received based on the exclusive data transmission channel.

9. The method according to claim 8, characterized in that The step of generating a temporary communication key pair according to the inspection instruction and the communication code comprises: Determine an inspection task according to the inspection instruction, and generate verification information according to the inspection task; Interpolate the verification information and the communication code frame by frame to obtain a temporary communication code; Obtaining timestamp information of the inspection instruction, and generating failure timestamp information based on the timestamp information; Combining the expiration timestamp information with the temporary communication code to obtain a temporary encryption handle; Perform an inverse operation on the temporary encryption handle to obtain a temporary decryption handle; A temporary communication key pair is generated according to the temporary encryption handle and the temporary decryption handle.

10. A patrol parameter optimization device based on cloud-edge collaboration, characterized in that: The inspection parameter optimization device based on cloud-edge collaboration includes: An instruction exchange module is used to receive an inspection instruction sent by the cloud, determine an inspection target according to the inspection instruction, and determine a target terminal device according to the inspection target; A channel establishment module, used to obtain a communication code of the target terminal device, establish a dedicated data transmission channel according to the communication code, and receive status information uploaded by the target terminal device from the dedicated data transmission channel; A state determination module, configured to determine the operating state of the target terminal device according to the state information, and when the operating state is an abnormal state, determine the abnormal amplitude of the operating state; The parameter optimization module is used to generate optimization parameters from the abnormal amplitude through a parameter optimization model, and optimize the operating parameters of the target terminal based on the optimization parameters.

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