Monitoring platform configuration generation method, equipment and medium

Abstract

The invention provides a monitoring platform configuration generation method, equipment and a medium. The method comprises the following steps: receiving inquiry information configured by a monitoring platform from a current session to a current round; the inquiry information comprises information of a first monitoring object of a to-be-configured measuring point, a configuration target and measuring point information; generating second configuration data corresponding to the inquiry information according to the inquiry information and first configuration data; wherein the first configuration data is configuration data confirmed by a user in a current session; based on the first configuration data, detecting whether the second configuration data has logic contradictions or not, and obtaining configuration effective information of the second configuration data; and determining the second configuration data and the configuration effective information as the answer of the current round. The configuration difficulty can be reduced, configuration omission and errors are reduced, and the configuration deployment efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of software configuration technology, and in particular to a method, device and medium for generating configurations for a monitoring platform. Background Technology

[0002] Monitoring systems in the energy storage industry are intelligent systems that integrate data acquisition, real-time detection, analysis, and control. In practical applications, it is necessary to connect one or more different types and models of sub-level or parent monitoring devices, depending on the specific engineering environment.

[0003] To improve deployment efficiency and reduce development costs, monitoring systems typically require configuration during deployment to meet the monitoring needs of business scenarios. Since monitoring systems usually consist of multiple monitoring devices, each requiring detailed configuration, and these devices may also need to collaborate, the configuration process is complex. For non-professional or less experienced users, the configuration is difficult, prone to omissions or errors, and impacts deployment efficiency. Summary of the Invention

[0004] This application provides a monitoring platform configuration generation method, device, and medium to reduce configuration difficulty, minimize configuration omissions and errors, and improve configuration deployment efficiency.

[0005] In a first aspect, embodiments of this application provide a method for generating monitoring platform configurations, including: Receive query information from the monitoring platform configuration up to the current round in the current session; the query information includes information about the first monitoring object to be configured, the configuration target, and the measurement point information; Based on the query information and the first configuration data, second configuration data corresponding to the query information is generated; wherein, the first configuration data is the configuration data that the user has confirmed in the current session; Based on the first configuration data, detect whether there is a logical contradiction in the second configuration data, and obtain the configuration validity information of the second configuration data; The second configuration data and the configuration validity information are determined as the answer for the current round.

[0006] In one possible implementation, the step of detecting whether there is a logical contradiction in the second configuration data based on the first configuration data, and obtaining the configuration validity information of the second configuration data, includes: The system detects whether the first configuration data includes third configuration data and fourth configuration data; the third configuration data is configuration data confirmed by the user associated with the measurement point, and the fourth configuration data is configuration data confirmed by the user of the measurement point to be configured corresponding to the query information; the associated measurement point is a measurement point that has an association relationship with the measurement point to be configured. If the first configuration data contains at least one of the third configuration data and the fourth configuration data, then based on the third configuration data and / or the fourth configuration data, the second configuration data is checked for logical contradictions, and the configuration validity information of the second configuration data is obtained.

[0007] In one possible implementation, the step of detecting whether there is a logical contradiction in the second configuration data based on the third configuration data and / or the fourth configuration data, and obtaining the configuration validity information of the second configuration data, includes: The third configuration data and / or the fourth configuration data, as well as the second configuration data, are input into a preset configuration detection model to obtain the configuration validity information output by the configuration detection model; wherein, the configuration detection model is trained based on the historical configuration data, current configuration data and corresponding logical contradiction information of different measurement points in different monitoring platforms, as well as the current configuration data of different measurement points, the configuration data of the associated measurement points of the measurement point and the corresponding logical contradiction information. Wherein, when there is a logical contradiction in the second configuration data, the configuration validity information includes the logical contradiction information of the second configuration data and the configuration validity probability; when there is no logical contradiction in the second configuration data, the configuration validity information includes the configuration validity probability of the second configuration data.

[0008] In one possible implementation, generating second configuration data corresponding to the query information based on the query information and the first configuration data includes: Detect whether the first configuration data contains fourth configuration data; the fourth configuration data is the configuration data confirmed by the user of the test point to be configured corresponding to the query information; If the first configuration data includes the fourth configuration data, then the second configuration data corresponding to the query information is generated based on the query information and the fourth configuration data.

[0009] In one possible implementation, generating the second configuration data corresponding to the query information based on the query information and the fourth configuration data includes: Based on the query information and the fourth configuration data, a query index vector for the query information is generated; Determine the similarity between the template index vector of each preset configuration template in the preset knowledge base and the query index vector; the knowledge base stores multiple preset configuration templates and the template index vector corresponding to each preset configuration template; Select the preset configuration template corresponding to the template index vector whose similarity meets the preset conditions, and determine it as the target configuration template; Based on the target configuration template and the fourth configuration data, the second configuration data corresponding to the query information is generated.

[0010] In one possible implementation, after generating the second configuration data corresponding to the query information based on the query information and the first configuration data, the method further includes: Detect whether the second configuration data contains a first parameter item and a second parameter item; the first parameter item is a parameter item in the second configuration data that has no assigned value, and the second parameter item is a parameter item in the second configuration data that uses a default value; If the second configuration data contains at least one of a first parameter item and a second parameter item, then the configuration recommendation for the second configuration data is determined based on the first parameter item and / or the second parameter item in the second configuration data.

[0011] In one possible implementation, after determining the second configuration data and the configuration validity information as the answer for the current round, the method further includes: Receive confirmation information for the second configuration data in the current session; If the confirmation information includes information confirming the configuration of the second configuration data, then the corresponding monitoring object's measurement points are configured according to the second configuration data.

[0012] In one possible implementation, configuring the measurement points to be configured for the corresponding monitoring object based on the second configuration data includes: Configure the system in a preset test environment based on the second configuration data, and then test the second configuration data. If the second configuration data passes the test, then configure the monitoring points of the corresponding monitoring objects according to the second configuration data.

[0013] Secondly, embodiments of this application provide a monitoring platform configuration generation device, comprising: The receiving module is used to receive query information from the monitoring platform configuration up to the current round in the current session; the query information includes information about the first monitoring object to be configured, the configuration target, and the measurement point information; A generation module is used to generate second configuration data corresponding to the query information based on the query information and the first configuration data; wherein, the first configuration data is configuration data that has been confirmed by the user in the current session; The detection module is used to detect whether there is a logical contradiction in the second configuration data based on the first configuration data, and to obtain the configuration validity information of the second configuration data; The determination module is used to determine the second configuration data and the configuration validity information as the answer for the current round.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0016] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0017] The beneficial effects of the embodiments in this application compared with the prior art are: This application embodiment receives query information from the monitoring platform configuration up to the current round in the current session. This allows for the acquisition of information about the first monitored object to be configured, the configuration target, and the measurement point information, facilitating the subsequent generation of an accurate combined configuration. Based on the received query information and the first configuration data, second configuration data corresponding to the query information can be generated. The first configuration data is the configuration data already confirmed by the user in the current session. Therefore, based on the query information and the first configuration data, the second configuration data for the measurement point to be configured, as required by the user, can be obtained. Furthermore, the first configuration data can be used to detect any logical contradictions between the currently generated second configuration data and the user-confirmed configuration data, thus obtaining the configuration validity information for the second configuration data. Finally, the second configuration data and the configuration validity information are determined as the answer for the current round. This allows the user to obtain the configuration required for the current round, reducing configuration difficulty. The configuration validity information also alerts the user to potential omissions, errors, or logical contradictions in the current configuration, guiding the user to adjust, modify, or apply the configuration. This reduces configuration omissions and errors and improves the efficiency of configuration deployment. This application utilizes an interactive configuration method based on natural language, allowing users to complete system configuration simply by interacting with a natural language model. This eliminates the need for specialized configuration tools, making it easy for users to understand, reducing the difficulty of configuration, reminding users of errors and omissions during the configuration process, and achieving intelligent configuration. This, in turn, reduces the time and manpower costs of on-site configuration of the monitoring system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the implementation of the monitoring platform configuration generation method provided in this application embodiment; Figure 2 This is a flowchart illustrating the monitoring platform configuration generation method provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of the monitoring platform configuration generation device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] The inventors of this application have discovered that deploying a monitoring system requires configuration, but this configuration process is fragmented, difficult, and poorly checked. Specifically, the modeling of monitoring items and alarm rules during configuration is scattered across different configuration files or platforms, and various monitoring devices may need to collaborate, resulting in fragmented configuration information that is difficult to quickly find and utilize. Furthermore, the configuration process requires the use of specialized modeling tools or direct editing of configuration files, necessitating an understanding of complex terminology, leading to high configuration difficulty and operational challenges. In addition, for non-professional or less experienced users, errors and omissions are prone to occur during configuration, leading to configuration failures, and sometimes these errors are only discovered after a period of operation, impacting deployment efficiency and the usability of the monitoring system.

[0022] To reduce configuration difficulty and minimize omissions and errors, this embodiment employs an interactive configuration method using natural language. The system configuration is completed through a question-and-answer session with a natural language model, alerting users to errors and omissions during the configuration process. This eliminates the need for specialized configuration tools, achieving intelligent configuration. Specifically, second configuration data is generated using query information from the monitoring platform up to the current session and the user-confirmed first configuration data to meet the user's configuration needs and reduce configuration difficulty. Simultaneously, the first configuration data is used to detect logical inconsistencies in the generated second configuration data. The generated second configuration data and the detected valid configuration information are then fed back to the user as an answer, alerting them to potential problems in the current configuration and guiding them to confirm and adjust, thereby reducing configuration errors and improving the efficiency of configuration deployment.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0024] See Figure 1 The document illustrates a flowchart of the implementation of the monitoring platform configuration generation method provided in this embodiment of the invention, which is described in detail below: Step 101: Receive the query information of the monitoring platform configuration up to the current round in the current session; the query information includes the information of the first monitoring object to be configured, the configuration target, and the measurement point information.

[0025] Here, the query information represents the user's configuration request for the monitoring platform in the current session and current round, i.e., what kind of test points the user expects to configure. The test points to be configured are the test points to be configured in the current session and current round.

[0026] The query information includes three key aspects of the configuration process: information about the primary monitoring object of the measurement point to be configured, the configuration objective, and the measurement point information. The primary monitoring object is the object monitored by the measurement point to be configured, such as an energy storage converter, energy storage battery, and cooling fan. The configuration objective is the function or purpose to be achieved by the measurement point to be configured, such as safety monitoring or data acquisition optimization to reduce resource consumption. The measurement point information is the basic information of the measurement point to be configured, which may include the measurement point type, quantity, and parameters that need to be adjusted. The measurement point type can include analog and digital measurement points to facilitate subsequent targeted generation of measurement point configurations.

[0027] In this embodiment, considering that the user may have already entered some requirements for the measurement points to be configured before the current round, such as information about the first monitored object and configuration goals, and that in the current round, the user may only have entered the information that needs to be adjusted, such as only entering some measurement point information and not all of the information, all the query information required to configure the measurement points can be obtained through the question and answer information in the current session up to the current round, ensuring that the generated configuration does not deviate from the user's needs and the actual scenario.

[0028] Here, receiving query information from the monitoring platform configuration up to the current round in the current session can be achieved by receiving the current input information from the monitoring platform configuration in the current round of the current session; based on the current input information, determining relevant information related to the measurement point to be configured from the historical input information of previous rounds in the current session; and based on the current input information and the relevant information, determining the query information from the monitoring platform configuration up to the current round in the current session. The relevant information includes information about the first monitoring object of the measurement point to be configured, the configuration target, and the measurement point information.

[0029] For conflicting or contradictory information, the information confirmed by the user or the most recently entered information shall prevail.

[0030] Step 102: Generate second configuration data corresponding to the query information based on the query information and the first configuration data; wherein, the first configuration data is the configuration data that the user has confirmed in the current session.

[0031] Here, the first configuration data consists of all configuration data that has been confirmed by the user in the current session, providing the configuration basis for generating the configuration data for the current round. The second configuration data is the configuration data generated according to the requirements of the query information.

[0032] In this embodiment, by querying information and first configuration data, the configuration data required for the current round can be generated by combining the user's current needs and the configuration data that has been confirmed by the user in the past, thereby reducing the problem of conflicts and contradictions between the configuration and the historical configuration.

[0033] Step 103: Based on the first configuration data, detect whether there is a logical contradiction in the second configuration data, and obtain the configuration validity information of the second configuration data.

[0034] In this embodiment, logical verification is used to detect whether there is a logical contradiction between the generated second configuration data and the first configuration data that has been confirmed by the user in the past, thereby realizing the contradiction detection of the second configuration data and obtaining the corresponding valid configuration information.

[0035] Here, the valid configuration information may include whether there is a logical contradiction in the second configuration data itself, whether there is a logical contradiction between the second configuration data and the first configuration data, and information about the existing logical contradictions, such as the type of logical contradiction.

[0036] For example, if the measurement point to be configured is the temperature monitoring of an energy storage battery, the historical configuration sets the cooling fan of the energy storage battery to start after an energy storage battery temperature alarm, where the alarm temperature is 60℃. However, the second configuration data sets the temperature alarm threshold for energy storage battery temperature monitoring to 65℃, which differs from the user-confirmed cooling fan start temperature of 60℃ in the first configuration data. Therefore, it can be determined that there is a logical contradiction in the configuration of the second configuration data.

[0037] Step 104: Determine the second configuration data and the valid configuration information as the answer for the current round.

[0038] In this embodiment, the generated second configuration data and the detected valid configuration information are integrated into a final answer and fed back to the user. This allows the user to easily and intuitively view the generated configuration and its validity, reminds the user of any problems in the generated second configuration data, assists the user in confirming and adjusting the configuration, lowers the user's configuration threshold, and ensures the accuracy of the configuration.

[0039] Here, by considering user needs (i.e., query information) and historical benchmarks (i.e., the first configuration data), second configuration data is generated, and conflict detection is performed on the generated second configuration data. This entire process can assist users in making accurate decisions. In particular, by generating second configuration data, the fragmented problem of user self-configuration can be avoided; through conflict detection, conflicts with historical configurations and device constraints can be avoided in advance, reducing configuration errors and omissions.

[0040] This application embodiment receives query information from the monitoring platform configuration up to the current round in the current session. This allows for the acquisition of information about the first monitored object to be configured, the configuration target, and the measurement point information, facilitating the subsequent generation of an accurate combined configuration. Based on the received query information and the first configuration data, second configuration data corresponding to the query information can be generated. The first configuration data is the configuration data already confirmed by the user in the current session. Therefore, based on the query information and the first configuration data, the second configuration data for the measurement point to be configured, as required by the user, can be obtained. Furthermore, the first configuration data can be used to detect any logical contradictions between the currently generated second configuration data and the user-confirmed configuration data, thus obtaining the configuration validity information for the second configuration data. Finally, the second configuration data and the configuration validity information are determined as the answer for the current round. This allows the user to obtain the configuration required for the current round, reducing configuration difficulty. The configuration validity information also alerts the user to potential omissions, errors, or logical contradictions in the current configuration, guiding the user to adjust, modify, or apply the configuration. This reduces configuration omissions and errors and improves the efficiency of configuration deployment. This application utilizes an interactive configuration method based on natural language, allowing users to complete system configuration simply by interacting with a natural language model. This eliminates the need for specialized configuration tools, making it easy for users to understand, reducing the difficulty of configuration, reminding users of errors and omissions during the configuration process, and achieving intelligent configuration. This, in turn, reduces the time and manpower costs of on-site configuration of the monitoring system.

[0041] In some embodiments, see Figure 2 The flowchart of the monitoring platform configuration generation method shown above illustrates that, based on the first configuration data, the method checks whether there is a logical contradiction in the second configuration data to obtain the configuration validity information of the second configuration data. This can involve checking whether the first configuration data contains third and fourth configuration data. The third configuration data is the configuration data confirmed by the user of the associated measurement point, and the fourth configuration data is the configuration data confirmed by the user of the measurement point to be configured corresponding to the query information. The associated measurement point is a measurement point that has an association relationship with the measurement point to be configured. If the first configuration data contains at least one of the third and fourth configuration data, then based on the third and / or fourth configuration data, the method checks whether there is a logical contradiction in the second configuration data to obtain the configuration validity information of the second configuration data.

[0042] In this embodiment, considering that a user may configure multiple test points or multiple monitoring platforms in one session, it is necessary to determine the configuration data related to the test point to be configured from the first configuration data of the current session in order to accurately detect logical contradictions, avoid meaningless full comparison, and improve detection accuracy and efficiency.

[0043] Configuration data related to the measurement point to be configured may include configuration data confirmed by the user of the measurement point to be configured, i.e., the fourth configuration data. It may also identify related measurement points that are associated with the measurement point to be configured, including configuration data confirmed by the user of those related measurement points.

[0044] Among them, the associated measurement point and the measurement point to be configured are not the same measurement point, but measurement points with certain logical relationships, such as the same configuration rules for the two types of measurement points or linkage, and mutual dependence between parameters.

[0045] Here, we can first check the first configuration data. If the first configuration data does not contain the third and fourth configuration data, it means that the test point to be configured has not been configured before, and the associated test points to be configured have not been configured either. Therefore, there is no basis for checking the logical contradiction of the test point to be configured. Accordingly, we can skip checking the second configuration data at this time, and we can also remind the user that the second configuration data has not been checked for logical contradiction.

[0046] If the first configuration data contains at least one of the third and fourth configuration data, it indicates that there is a basis for logical contradiction detection of the second configuration data. Therefore, logical contradiction detection of the second configuration data can be performed based on the existing third and / or fourth configuration data.

[0047] For example, if the first configuration data only includes the third configuration data, it means that the measurement point to be configured has not been configured before, but the associated measurement points of the measurement point to be configured have been configured in historical rounds. Then, the second configuration data and the third configuration data of the associated measurement points can be compared to determine whether there are any issues such as conflicting linkage logic or contradictory parameter items.

[0048] When the first configuration data only includes the fourth configuration data, it indicates that the measurement point to be configured has been configured before, but the associated measurement points of the measurement point to be configured have not been configured in previous rounds. In this case, the second configuration data and the fourth configuration data confirmed by the user in previous rounds can be compared to determine if there are any parameter conflicts between them.

[0049] When the first configuration data includes the third and fourth configuration data, it indicates that the test point to be configured has been configured before, and the associated test points to be configured have been configured in historical rounds. Therefore, the second configuration data can be compared with the third configuration data of the associated test points, and the second configuration data can be compared with the fourth configuration data confirmed by the user in historical rounds to identify any potential logical contradictions in the second configuration data.

[0050] Optionally, in this embodiment, detecting whether there is a logical contradiction in the second configuration data based on the third configuration data and / or the fourth configuration data to obtain the configuration validity information of the second configuration data can be achieved by: inputting the third configuration data and / or the fourth configuration data, as well as the second configuration data, into a preset configuration detection model to obtain the configuration validity information output by the configuration detection model; wherein, the configuration detection model is trained based on historical configuration data, current configuration data, and corresponding logical contradiction information of different measurement points in different monitoring platforms, as well as the current configuration data of different measurement points, the configuration data of related measurement points of that measurement point, and corresponding logical contradiction information; wherein, when there is a logical contradiction in the second configuration data, the configuration validity information includes the logical contradiction information and the configuration validity probability of the second configuration data; when there is no logical contradiction in the second configuration data, the configuration validity information includes the configuration validity probability of the second configuration data.

[0051] In this embodiment, a configuration detection model can be used to compare the second configuration data with the third configuration data, as well as the second configuration data with the fourth configuration data, to obtain the configuration validity information of the second configuration data, thereby improving the accuracy of contradiction detection and providing real-time reminders to users of potential configuration problems during the configuration process.

[0052] Here, if both third and fourth configuration data exist, then the second, third, and fourth configuration data are used as inputs to the configuration detection model. If only the third configuration data exists, then the second and third configuration data are used as inputs to the configuration detection model. If only the fourth configuration data exists, then the second and fourth configuration data are used as inputs to the configuration detection model. The second configuration data is considered the object being detected, and the third and fourth configuration data are used as the benchmark for detection.

[0053] The configuration detection model can be trained using a Generative Adversarial Network (GAN). It can be trained using historical configuration data, current configuration data, and corresponding logical contradiction information from different monitoring platforms and various test points, as well as current configuration data from different test points, configuration data from related test points, and corresponding logical contradiction information. Historical configuration data for a test point can be configuration data that a user has previously confirmed or implemented for that test point, or configuration data generated during the configuration process. Current configuration data can be configuration data generated or adjusted by the user for that test point at that time. Configuration data from related test points can be configuration data that a user has confirmed or implemented for those related test points at that time. Corresponding logical contradiction information can be obtained through annotation; the specific annotations can be determined by professionals on the monitoring platform.

[0054] The historical configuration data, current configuration data, configuration data of related test points, and logical contradiction information of the aforementioned different test points can be obtained by collecting actual engineering configurations and artificially creating logical contradictions. Furthermore, the configuration detection model can be trained and updated based on data from actual user usage and data generated during testing in the test environment, thus achieving iterative updates to the configuration detection model.

[0055] Regarding valid configuration information, when logical contradictions exist in the second configuration data, the valid configuration information includes the logical contradiction information present in the second configuration data and the configuration validity probability. The logical contradiction information can include the type of logical contradiction, the associated measurement points with logical contradictions, and the parameter items with logical contradictions. The configuration validity probability is related to the logical contradiction information; the more logical contradictions present in the logical contradiction information, the lower the configuration validity probability; the higher the severity of the logical contradictions, the lower the configuration validity probability.

[0056] When there are no logical contradictions in the second configuration data, the configuration validity information may include the probability of the second configuration data being valid. Additionally, it may include a statement confirming the absence of logical contradictions.

[0057] The effective probability of configuration can be determined by configuring the confidence probability output by the detection model.

[0058] For example, in the historical configuration data of measurement point A, the range of parameter item B is 1-10, while in the current configuration data, the range of parameter item B is 20-50. Since there is a conflict between the two, the valid configuration information can be that there is a logical contradiction in the second configuration data. In the second configuration data, the range of parameter item B of measurement point A is 20-50, which is different from the range of 1-10 in the historical configuration data. The probability of the configuration being valid is 80%.

[0059] In some embodiments, generating second configuration data corresponding to the query information based on the query information and the first configuration data may involve detecting whether the first configuration data contains fourth configuration data; the fourth configuration data is the configuration data confirmed by the user for the test point to be configured corresponding to the query information; if the first configuration data contains the fourth configuration data, then the second configuration data corresponding to the query information is generated based on the query information and the fourth configuration data.

[0060] In this embodiment, when generating the second configuration data, the historically confirmed configuration of the measurement point to be configured, i.e., the fourth configuration data, is reused first. Then, it is supplemented and adjusted in combination with the current user query information to generate complete second configuration data. This ensures that the newly generated configuration is logically consistent with the historical configuration of the same measurement point, and avoids meaningless duplicate configuration or conflict.

[0061] By detecting whether the first configuration data contains the fourth configuration data, it can be determined whether there is a reusable historical configuration for the same measurement point.

[0062] When the first configuration data contains the fourth configuration data, it indicates that there is a reusable historical configuration for the same measurement point. Therefore, the fourth configuration data can be adjusted according to the query information to generate the second configuration data required for the current round.

[0063] For example, if the query message indicates a need to add a new configuration function, then a new parameter item can be added based on the fourth configuration data. If the query message indicates a need to adjust a specific parameter item, then that parameter item can be adjusted based on the fourth configuration data. For instance, if the alarm threshold in the fourth configuration data is 55℃, and the query message indicates a need to adjust the alarm threshold to 50℃, then the alarm threshold in the fourth configuration data can be adjusted from 55℃ to 50℃ to obtain the second configuration data.

[0064] Here, the second configuration data is generated based on the fourth configuration data. This eliminates the need for users to repeatedly input the core parameters of the same measurement point, as the system automatically adopts previously confirmed configurations, lowering the operational threshold for users. Furthermore, configuration iterations for the same measurement point are typically based on historical configurations. Generating the second configuration data based on the fourth configuration data avoids significant discrepancies between the newly generated second configuration data and the fourth configuration data, which could lead to various contradictions and conflicts.

[0065] Optionally, in this embodiment, generating the second configuration data corresponding to the query information based on the query information and the fourth configuration data can be achieved by: first, generating a query index vector for the query information based on the query information and the fourth configuration data; then, determining the similarity between the template index vector and the query index vector of each preset configuration template in the preset knowledge base; the knowledge base stores multiple preset configuration templates and the template index vector corresponding to each preset configuration template; selecting the preset configuration template corresponding to the template index vector whose similarity meets the preset conditions, and determining it as the target configuration template; finally, generating the second configuration data corresponding to the query information based on the target configuration template and the fourth configuration data.

[0066] In this embodiment, the query index vector can be a semantic vector obtained by fusing the user's current query information and the fourth configuration data, which can accurately express the current needs and historical scenarios. The query index vector can be generated using a natural language pre-trained model or by keyword extraction.

[0067] Generating a query index vector through keyword extraction can be achieved by: extracting target keywords from the query information; the target keywords include information about the first monitored object of the measurement point to be configured, the configuration target, and the measurement point information; and generating a query index vector based on the target keywords. Here, the target keywords are the information about the first monitored object of the measurement point to be configured, the configuration target, and the measurement point information extracted from the query information, which can extract information related to the target configuration template. By processing the target keywords, structured data, such as a structured matrix or structured vector, can be formed, thereby obtaining the query index vector for subsequent matching with the template index vector.

[0068] The above query information includes query information up to the current round in the current session. Since the test points to be configured may have been queried in previous rounds, i.e., previous rounds also contained information about the test points to be configured, there may be conflicts between the information in the current round and the information in previous rounds. The most recent information should be taken as the standard.

[0069] Here, the fourth configuration data is the configuration that has been confirmed in the historical rounds. It is a clear scenario parameter that has taken effect and is the logical premise for subsequent configurations. Therefore, when generating the index vector, the fourth configuration data is also considered to ensure that the dynamic vector fits the current real configuration scenario.

[0070] Correspondingly, when extracting target keywords, the target keywords can be extracted from the query information and the fourth configuration data based on the fourth configuration data.

[0071] The query information may conflict across different rounds, and there may also be conflicts between the query information and the fourth configuration data. For example, the query information input in the current round may be incorrect, or the configuration requirements for the test point to be configured may have changed in the current round. Based on the query information and the fourth configuration data, the semantic environment information of the test point to be configured can be determined, and the target keywords can be corrected using this semantic environment information. The semantic environment information can include the word frequency and weight of each keyword. For keywords of the same category, the importance of each keyword can be determined by its word frequency and weight. The keyword with the highest importance can be selected as the target keyword for that category, reducing the impact of errors in the input query information and accurately matching the user's expected preset configuration template.

[0072] For example, after receiving the input information for the current round, the target keywords for the measurement points to be configured can be extracted from the query information up to the current round in the current session. For keywords of the same category, the keywords from the most recent round of input information are used. When there is a conflict between the query information in different rounds and the aforementioned target keywords, or a conflict between the fourth configuration data and the aforementioned target keywords, the semantic environment information of the measurement points to be configured is determined based on the query information and the fourth configuration data. For example, if the user actively modifies a keyword in the current round, the modified keyword has the highest weight, meaning the extracted target keywords have the highest importance, and thus the extracted target keywords are retained without correction. If the user inputs an error in the current round, the keyword with the highest determined importance is used as the corresponding target keyword, thus correcting the target keywords. Finally, using the corrected target keywords, structured data is formed to obtain the query index vector of the query information.

[0073] The knowledge base in this embodiment stores standardized preset configuration templates for different monitoring needs. These templates include required parameters, optional parameters, and parameter constraints. These preset configuration templates can be generated using enterprise data and experience data provided by relevant experts.

[0074] The knowledge base also includes a template index vector corresponding to each preset configuration template. This template index vector indicates the configuration information and scenario information of its corresponding preset configuration template. It can be a fixed semantic vector generated by the same natural language pre-trained model and is in the same semantic space as the query index vector, which facilitates the calculation of similarity.

[0075] By calculating the similarity between the query index vector and the template index vector, we can determine the degree of matching between the preset configuration templates in the knowledge base and the current requirements, thereby quickly filtering out templates that match the current requirements and historical configurations.

[0076] The target configuration template is a preset configuration template selected from the knowledge base that meets preset conditions regarding its similarity to the query index vector. It forms the basic framework for generating the second configuration data. The preset conditions can be either using the preset configuration template corresponding to the template index vector with a similarity greater than a preset similarity as the target configuration template, or using a preset number of preset configuration templates with the highest similarity as the target configuration template.

[0077] Finally, by combining the target configuration template and the fourth configuration data, the fourth configuration data can be adjusted to generate the second configuration data required for the current round.

[0078] Here, the combination of the target configuration template and the fourth configuration data can be achieved using a natural language question-answering model. The target configuration template and the fourth configuration data are input into the natural language question-answering model to obtain the second configuration data output by the model. The natural language question-answering model can be trained using the target configuration template and fourth configuration data corresponding to query information at different measurement points, as well as the second configuration data corresponding to the query information. The training data can be obtained from enterprise data and expert experience data.

[0079] Furthermore, the natural language question answering (NLE) model can be trained and updated based on data from actual user usage and test data generated in the testing environment. Specifically, the data from actual user usage and test data can be cleaned to enrich the training dataset of the NLE model, thereby enabling fine-tuning of the model.

[0080] Furthermore, the process of determining the target configuration template using query information and fourth configuration data can also be implemented using a natural language question-answering model. That is, a natural language question-answering model can generate second configuration data corresponding to the query information based on the query information and fourth configuration data. Accordingly, this natural language question-answering model can be trained using query information and fourth configuration data at different test points, as well as the second configuration data corresponding to the query information.

[0081] Furthermore, if the first configuration data does not contain the fourth configuration data, then the second configuration data corresponding to the query information is generated based on the query information.

[0082] The fact that the first configuration data does not include the fourth configuration data indicates that the measurement point to be configured has not been configured before, and this round is the first time the measurement point to be configured is being configured. Therefore, the second configuration data can be generated directly based on the query information.

[0083] Here, the generation of the second configuration data can refer to the process in the above embodiments, the only difference being that the fourth configuration data is not used. That is, based on the query information, a query index vector of the query information is generated, and then the target configuration template is determined. Finally, the target configuration template is combined to generate the second configuration data corresponding to the query information.

[0084] In some embodiments, such as Figure 2As shown, after generating the second configuration data corresponding to the query information based on the query information and the first configuration data, it is also possible to detect whether the second configuration data contains a first parameter item and a second parameter item; the first parameter item is a parameter item in the second configuration data that has no assigned value, and the second parameter item is a parameter item in the second configuration data that uses a default value; if the second configuration data contains at least one of the first parameter item and the second parameter item, then the configuration suggestion of the second configuration data is determined based on the first parameter item and / or the second parameter item in the second configuration data.

[0085] In this embodiment, configuration suggestions of the prompt type can also be determined for the generated second configuration data to remind the user to supplement or confirm, so as to avoid invalid configuration or failure to meet actual needs due to missing parameter items or improper default values ​​of parameter items.

[0086] The first parameter item is one that has no value assigned in the second configuration data, meaning it is blank and not set. The second parameter item is one that has been assigned a value in the second configuration data, but uses the default value, meaning it is a parameter item that the user has not specifically adjusted.

[0087] Here, by using all the required and optional parameters of the measurement point to be configured, as well as the default value of each parameter, the parameter items that are not assigned values ​​and the parameter items that use the default values ​​in the second configuration data can be accurately identified.

[0088] Then, by using the first and second parameter items in the second configuration data, configuration suggestions can be generated specifically to prompt the user.

[0089] When the second configuration data contains only the first parameter item, configuration suggestions for the second configuration data are determined based on the first parameter item, prompting the user to set or assign a value to the first parameter item. For example, the configuration suggestion could be: Parameter item X has not been set. Furthermore, the configuration suggestion can also include the impact of the first parameter item and the recommended setting range; for example, if parameter item X is not set, it may lead to incomplete monitoring functionality, and it is recommended to set it to 1-10.

[0090] When the second configuration data only contains the second parameter item, the configuration suggestion for the second configuration data is determined based on the second parameter item, reminding the user that the second parameter item is a default value and asking for confirmation whether to adjust it. For example, the configuration suggestion could be: Parameter item X uses the default value; if it meets the requirements, you can confirm directly. Similarly, the configuration suggestion can also include a suggested setting range for the second parameter item for the user to select and adjust.

[0091] When the second configuration data includes both a first parameter item and a second parameter item, a configuration recommendation for the second configuration data is determined based on both items. This recommendation combines the recommendations from the two scenarios above, prompting the user to assign values ​​to the first parameter item and adjust the second parameter item.

[0092] Furthermore, when generating the second configuration data using a natural language question-answering model, configuration suggestions for that second configuration data can also be generated simultaneously. That is, the output of the natural language question-answering model includes both the second configuration data and configuration suggestions. Correspondingly, the training data for the natural language question-answering model will also include configuration suggestions. Training is performed using the target configuration templates and fourth configuration data corresponding to the query information at different test points, as well as the second configuration data and configuration suggestions corresponding to the query information. The configuration suggestions can be generated through expert experience.

[0093] In some embodiments, after determining the second configuration data and configuration validity information as the response for the current round, confirmation information of the second configuration data in the current session can also be received; if the confirmation information contains information confirming the configuration of the second configuration data, then the configuration points of the corresponding monitoring object are configured according to the second configuration data.

[0094] In this embodiment, after providing the user with the second configuration data and configuration validity information, it is necessary to wait for the user's final confirmation. Only after receiving clear confirmation of the configuration information will the second configuration data be applied to the monitoring object's unconfigured measurement points.

[0095] Here, the confirmation information is the feedback information entered by the user after receiving the answer in the current round, that is, after the user receives the second configuration data and the configuration validity information. It may include whether to confirm the configuration data, or to confirm some parameter items in the configuration data.

[0096] If the confirmation message includes information confirming the configuration of the second configuration data, it means that the user requests to configure the measurement point to be configured according to the second configuration data. In this case, the configuration operation can be performed to configure the measurement point to be configured according to the second configuration data.

[0097] The methods described in the above embodiments can be executed on a configuration platform that connects to all services that need to be configured through an interface. When the confirmation information includes information confirming the configuration of the second configuration data, the second configuration data is applied to the monitoring points to be configured on the corresponding monitoring objects through the interface.

[0098] Optionally, in this embodiment, the configuration of the test points to be configured for the corresponding monitoring object is based on the second configuration data. This can be done by configuring the test points in a preset test environment based on the second configuration data and testing the second configuration data. If the second configuration data passes the test, the test points to be configured for the corresponding monitoring object are configured according to the second configuration data.

[0099] In this embodiment, when applying the second configuration data, the feasibility of the second configuration data is first verified in an independent test environment. Only after the test is passed will the test points to be configured be formally configured in the real monitoring environment to avoid direct online deployment that could lead to monitoring system failure or functional inoperability.

[0100] The test environment can be a simulation environment that is completely isolated from the real monitoring environment (or production environment), with the same equipment model, monitoring logic and data transmission channel as the real environment, but will not affect the operation of the actual monitoring system.

[0101] By loading the second configuration data in the test environment, we can verify whether it can run normally, whether it can achieve the expected function, and whether there are any hidden conflicts.

[0102] If the test results meet the preset standards, such as normal function, no errors, response as expected, and no issues affecting the configuration, then the second configuration data test can be considered passed, and the second configuration data can be applied to the test points to be configured in the real monitoring environment.

[0103] If the second configuration data test fails, the configuration of subsequent test points will not proceed. The test logs can be used to pinpoint the reason for the failure and generate a corresponding corrective action. The reason and corrective action should then be fed back to the user, and the steps for receiving confirmation of the second configuration data in the current session and subsequent steps should be re-executed until the second configuration data test passes.

[0104] This application embodiment receives query information from the monitoring platform configuration up to the current round in the current session. This allows for the acquisition of information about the first monitored object to be configured, the configuration target, and the measurement point information, facilitating the subsequent generation of an accurate combined configuration. Based on the received query information and the first configuration data, second configuration data corresponding to the query information can be generated. The first configuration data is the configuration data already confirmed by the user in the current session. Therefore, based on the query information and the first configuration data, the second configuration data for the measurement point to be configured, as required by the user, can be obtained. Furthermore, the first configuration data can be used to detect any logical contradictions between the currently generated second configuration data and the user-confirmed configuration data, thus obtaining the configuration validity information for the second configuration data. Finally, the second configuration data and the configuration validity information are determined as the answer for the current round. This allows the user to obtain the configuration required for the current round, reducing configuration difficulty. The configuration validity information also alerts the user to potential omissions, errors, or logical contradictions in the current configuration, guiding the user to adjust, modify, or apply the configuration. This reduces configuration omissions and errors and improves the efficiency of configuration deployment. This application utilizes an interactive configuration method based on natural language, allowing users to complete system configuration simply by interacting with a natural language model. This eliminates the need for specialized configuration tools, making it easy for users to understand, reducing the difficulty of configuration, reminding users of errors and omissions during the configuration process, and achieving intelligent configuration. This, in turn, reduces the time and manpower costs of on-site configuration of the monitoring system.Specifically, by detecting whether the first configuration data includes the third and fourth configuration data, it can be determined whether the first configuration data contains user-confirmed configuration data of the associated test points of the test point to be configured, as well as user-confirmed configuration data of the test point to be configured, thus establishing the basis for logical contradiction detection. When the first configuration data contains at least one of the third and fourth configuration data, the third and / or fourth configuration data can be used to detect whether there is a logical contradiction in the second configuration data, obtaining the configuration validity information of the second configuration data to provide real-time alerts to users regarding potential configuration issues during the configuration process. The fourth configuration data is the user-confirmed configuration data of the test point to be configured. By using the query information and the fourth configuration data, the second configuration data corresponding to the query information is generated, which can be adjusted based on the user-confirmed configuration data to ensure... The newly generated configuration is logically consistent with the historical configurations of the same measurement point. When generating the second configuration data, the query information and the fourth configuration data are used to generate a query index vector for the query information. The template index vector that meets the preset conditions of similarity with the query index vector is searched in the knowledge base. A preset configuration template that fits the query information and the fourth configuration data can be found. The preset configuration template corresponding to the template index vector is the target configuration template. By combining the target configuration template and the fourth configuration data, the second configuration data corresponding to the query information can be generated. In addition, after generating the second configuration data, configuration suggestions for the second configuration data can be generated based on the parameter items without assigned values ​​and the parameter items with default values. This promptly reminds the user to supplement or confirm, avoiding invalid configurations or configurations that do not meet actual needs due to missing parameter items or inappropriate default values.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0107] Figure 3 A schematic diagram of the monitoring platform configuration generation device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown, and are described in detail below: like Figure 3 As shown, the monitoring platform configuration generation device 30 includes: The receiving module 31 is used to receive the query information of the monitoring platform configuration up to the current round in the current session; the query information includes the information of the first monitoring object of the measurement point to be configured, the configuration target and the measurement point information; The generation module 32 is used to generate second configuration data corresponding to the query information based on the query information and the first configuration data; wherein, the first configuration data is the configuration data that the user has confirmed in the current session; The detection module 33 is used to detect whether there is a logical contradiction in the second configuration data based on the first configuration data, and to obtain the configuration validity information of the second configuration data; The determination module 34 is used to determine the second configuration data and configuration validity information as the answer for the current round.

[0108] In one possible implementation, the detection module 33 is specifically used for: Check whether the first configuration data includes the third and fourth configuration data; the third configuration data is the configuration data confirmed by the user of the associated test point, and the fourth configuration data is the configuration data confirmed by the user of the test point to be configured corresponding to the query information; the associated test point is the test point that has an association relationship with the test point to be configured. If the first configuration data contains at least one of the third and fourth configuration data, then based on the third and / or fourth configuration data, the second configuration data is checked for logical contradictions to obtain the valid configuration information of the second configuration data.

[0109] In one possible implementation, the detection module 33 is specifically used for: The third configuration data and / or the fourth configuration data, as well as the second configuration data, are input into the preset configuration detection model to obtain the configuration validity information output by the configuration detection model. The configuration detection model is trained based on the historical configuration data, current configuration data and corresponding logical contradiction information of different measurement points in different monitoring platforms, as well as the current configuration data of different measurement points, the configuration data of related measurement points of the measurement point and the corresponding logical contradiction information. Specifically, when there is a logical contradiction in the second configuration data, the configuration validity information includes the logical contradiction information of the second configuration data and the configuration validity probability; when there is no logical contradiction in the second configuration data, the configuration validity information includes the configuration validity probability of the second configuration data.

[0110] In one possible implementation, the generation module 32 is specifically used for: Check whether the first configuration data contains the fourth configuration data; the fourth configuration data is the configuration data confirmed by the user of the test point to be configured corresponding to the query information; If the first configuration data contains the fourth configuration data, then the second configuration data corresponding to the query information is generated based on the query information and the fourth configuration data.

[0111] In one possible implementation, the generation module 32 is specifically used for: Based on the query information and the fourth configuration data, generate a query index vector for the query information; Determine the similarity between the template index vector and the query index vector of each preset configuration template in the preset knowledge base; the knowledge base stores multiple preset configuration templates and the template index vector corresponding to each preset configuration template; Select the preset configuration template corresponding to the template index vector whose similarity meets the preset conditions, and determine it as the target configuration template; Based on the target configuration template and the fourth configuration data, the second configuration data corresponding to the query information is generated.

[0112] In one possible implementation, the generation module 32 is further used for: Detect whether the second configuration data contains a first parameter item and a second parameter item; the first parameter item is a parameter item in the second configuration data that has no assigned value, and the second parameter item is a parameter item in the second configuration data that uses the default value; If the second configuration data contains at least one of the first parameter item and the second parameter item, then the configuration recommendation for the second configuration data is determined based on the first parameter item and / or the second parameter item in the second configuration data.

[0113] In one possible implementation, the monitoring platform configuration generation device 30 further includes a configuration module for: Receive confirmation information for the second configuration data in the current session; If the confirmation information includes information confirming the configuration of the second configuration data, then the corresponding monitoring object's measurement points to be configured are configured according to the second configuration data.

[0114] In one possible implementation, the configuration module is specifically used for: Configure the system in a preset test environment based on the second configuration data, and then test the second configuration data. If the second configuration data test passes, then configure the monitoring points of the corresponding monitoring objects according to the second configuration data.

[0115] Figure 4 This is a schematic diagram of the electronic device provided in an embodiment of this application. For example... Figure 4 As shown, the electronic device 40 of this embodiment includes a processor 41 and a memory 42. The memory 42 stores a computer program 43. When the processor 41 executes the computer program 43, it implements the steps in the various method embodiments described above. Alternatively, when the processor 41 executes the computer program 43, it implements the functions of each module / unit in the various device embodiments described above.

[0116] For example, computer program 43 may be divided into one or more modules / units, which are stored in memory 42 and executed by processor 41 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 43 in electronic device 40.

[0117] Electronic device 40 may include, but is not limited to, processor 41 and memory 42. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 40 and does not constitute a limitation on electronic device 40. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0118] The processor 41 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0119] The memory 42 can be an internal storage unit of the electronic device 40, such as a hard disk or RAM of the electronic device 40. The memory 42 can also be an external storage device of the electronic device 40, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 40. Furthermore, the memory 42 can include both internal and external storage units of the electronic device 40. The memory 42 is used to store computer programs and other programs and data required by the electronic device. The memory 42 can also be used to temporarily store data that has been output or will be output.

[0120] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0121] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0122] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0123] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0124] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0125] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for generating a monitoring platform configuration, characterized in that, include: Receive query information from the monitoring platform configuration up to the current round in the current session; The query information includes information about the first monitored object to be configured, the configuration target, and the measurement point information; Based on the query information and the first configuration data, second configuration data corresponding to the query information is generated; wherein, the first configuration data is the configuration data that the user has confirmed in the current session; Based on the first configuration data, detect whether there is a logical contradiction in the second configuration data, and obtain the configuration validity information of the second configuration data; The second configuration data and the configuration validity information are determined as the answer for the current round.

2. The monitoring platform configuration generation method according to claim 1, characterized in that, The step of detecting whether there is a logical contradiction in the second configuration data based on the first configuration data, and obtaining the configuration validity information of the second configuration data, includes: The system detects whether the first configuration data includes third configuration data and fourth configuration data; the third configuration data is configuration data confirmed by the user associated with the measurement point, and the fourth configuration data is configuration data confirmed by the user of the measurement point to be configured corresponding to the query information; the associated measurement point is a measurement point that has an association relationship with the measurement point to be configured. If the first configuration data contains at least one of the third configuration data and the fourth configuration data, then based on the third configuration data and / or the fourth configuration data, the second configuration data is checked for logical contradictions, and the configuration validity information of the second configuration data is obtained.

3. The monitoring platform configuration generation method according to claim 2, characterized in that, The step of detecting whether there is a logical contradiction in the second configuration data based on the third configuration data and / or the fourth configuration data, and obtaining the configuration validity information of the second configuration data, includes: The third configuration data and / or the fourth configuration data, as well as the second configuration data, are input into a preset configuration detection model to obtain the configuration validity information output by the configuration detection model; wherein, the configuration detection model is trained based on the historical configuration data, current configuration data and corresponding logical contradiction information of different measurement points in different monitoring platforms, as well as the current configuration data of different measurement points, the configuration data of the associated measurement points of the measurement point and the corresponding logical contradiction information. Wherein, when there is a logical contradiction in the second configuration data, the configuration validity information includes the logical contradiction information of the second configuration data and the configuration validity probability; when there is no logical contradiction in the second configuration data, the configuration validity information includes the configuration validity probability of the second configuration data.

4. The monitoring platform configuration generation method according to any one of claims 1 to 3, characterized in that, The step of generating second configuration data corresponding to the query information based on the query information and the first configuration data includes: Detect whether the first configuration data contains fourth configuration data; the fourth configuration data is the configuration data confirmed by the user of the test point to be configured corresponding to the query information; If the first configuration data includes the fourth configuration data, then the second configuration data corresponding to the query information is generated based on the query information and the fourth configuration data.

5. The monitoring platform configuration generation method according to claim 4, characterized in that, The step of generating second configuration data corresponding to the query information based on the query information and the fourth configuration data includes: Based on the query information and the fourth configuration data, a query index vector for the query information is generated; Determine the similarity between the template index vector of each preset configuration template in the preset knowledge base and the query index vector; the knowledge base stores multiple preset configuration templates and the template index vector corresponding to each preset configuration template; Select the preset configuration template corresponding to the template index vector whose similarity meets the preset conditions, and determine it as the target configuration template; Based on the target configuration template and the fourth configuration data, the second configuration data corresponding to the query information is generated.

6. The monitoring platform configuration generation method according to any one of claims 1 to 3, characterized in that, After generating the second configuration data corresponding to the query information based on the query information and the first configuration data, the method further includes: Detect whether the second configuration data contains a first parameter item and a second parameter item; the first parameter item is a parameter item in the second configuration data that has no assigned value, and the second parameter item is a parameter item in the second configuration data that uses a default value; If the second configuration data contains at least one of a first parameter item and a second parameter item, then the configuration recommendation for the second configuration data is determined based on the first parameter item and / or the second parameter item in the second configuration data.

7. The monitoring platform configuration generation method according to any one of claims 1 to 3, characterized in that, After determining the second configuration data and the configuration validity information as the answer for the current round, the method further includes: Receive confirmation information for the second configuration data in the current session; If the confirmation information includes information confirming the configuration of the second configuration data, then the corresponding monitoring object's measurement points are configured according to the second configuration data.

8. The monitoring platform configuration generation method according to claim 7, characterized in that, The step of configuring the monitoring points of the corresponding monitoring objects according to the second configuration data includes: Configure the system in a preset test environment based on the second configuration data, and then test the second configuration data. If the second configuration data passes the test, then configure the monitoring points of the corresponding monitoring objects according to the second configuration data.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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