An intelligent well lid running state evaluation method, device, equipment and medium

Abstract

The present application belongs to the technical field of intelligent well lid operation and maintenance, and specifically discloses a kind of intelligent well lid operating state evaluation method, device, equipment and medium, comprising: obtaining the operating parameter of intelligent well lid in a preset area, and is divided into several categories;Intelligent well lid in the preset area is obtained, and each intelligent well lid is set to fault loss influence degree, and the intelligent well lid in the preset area is clustered and divided into several clusters;Obtain historical data to obtain the production loss weight of different types of operating parameters;According to historical data, the out-of-limit separation of operating parameters is carried out, and the out-of-limit feedback value is obtained;According to the cluster where the intelligent well lid is located, the production loss weight and the out-of-limit feedback value, a three-dimensional coordinate is constructed;The operating state of the intelligent well lid is evaluated according to the three-dimensional coordinate.The present application obtains the operating parameter of intelligent well lid for processing, obtains clustering result, production loss weight and out-of-limit feedback value, and constructs three-dimensional coordinate to evaluate the loss caused by different operating parameters, which improves the accuracy of evaluation.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manhole cover operation and maintenance technology, specifically relating to an intelligent manhole cover operation status assessment method, device, equipment and medium. Background Technology

[0002] Smart manhole covers can effectively monitor basic information such as cable manhole joints, ambient temperature, harmful gases, and water depth. They can also promptly and accurately detect equipment operating statuses such as manhole cover damage and opening / closing, providing crucial data support for operators to conduct equipment maintenance. Therefore, comprehensive evaluation of regional smart manhole cover systems and avoiding discrepancies in the evaluation of smart manhole cover modules within cluster systems are essential. Currently, the industry lacks research on comprehensive evaluation methods for the operating status of regional smart manhole cover systems. Furthermore, smart manhole cover modules involve multiple parameters, including manhole temperature, humidity, and partial discharge current, which can lead to inaccurate fault type assessments. Existing intelligent fault operation status evaluation methods mostly rely on fault type assessment; when the fault type cannot be accurately determined, the operation status evaluation will also be inaccurate, and potential operational accidents caused by the fault cannot be detected. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, equipment, and medium for evaluating the operational status of intelligent manhole covers. This addresses the technical problem of complex parameters in intelligent manhole covers, which makes it difficult to determine fault types, inaccurately determine operational status based on fault types, and result in high operational risks.

[0004] To achieve the above objectives, the present invention employs the following technical solution:

[0005] In a first aspect, the present invention provides a method for evaluating the operational status of intelligent manhole covers, comprising the following steps:

[0006] Obtain the operating parameters of smart manhole covers within a preset area and classify the operating parameters into several categories;

[0007] Acquire smart manhole covers within a preset area, assign a fault loss impact degree to each smart manhole cover, and cluster the smart manhole covers within the preset area according to the fault loss impact degree to obtain several clusters;

[0008] Acquire historical data of smart manhole covers within a preset area, and obtain production loss weights for different types of operating parameters based on historical data and operating parameters;

[0009] Based on historical data, the operating parameters are separated to obtain the over-limit feedback value;

[0010] A three-dimensional coordinate system is constructed based on the cluster to which the smart manhole cover belongs, the production loss weight, and the over-limit feedback value.

[0011] The operating status of smart manhole covers is evaluated based on three-dimensional coordinates.

[0012] A further improvement of the present invention is that the step of acquiring historical data of smart manhole covers within a preset area and obtaining production loss weights for different types of operating parameters based on the historical data and operating parameters specifically includes:

[0013] Generate sample data based on historical data;

[0014] The runtime parameters are categorized by type;

[0015] The production loss weight for each type of operating parameter is calculated using the entropy weight method based on the sample data.

[0016] A further improvement of the present invention is that the step of separating operating parameters based on historical data to obtain limit feedback values ​​specifically includes:

[0017] A kernel density estimation and analysis model was established based on historical data and operating parameters;

[0018] The kernel density variation curve is generated based on the kernel density estimation analysis model. The maximum value of each type of operating parameter is obtained based on the kernel density variation curve, and the maximum value of each type of operating parameter is used as the typical value of each type of operating parameter.

[0019] Set a threshold limit for each type of operating parameter;

[0020] The over-limit feedback value for each type of operating parameter is calculated based on the threshold limit and typical value of each type of operating parameter.

[0021] A further improvement of the present invention is that the kernel density estimation analysis model is:

[0022]

[0023] In the formula, x is the running parameter, f w Let x be the probability density function of the operating parameters of smart well type i. i Let be the historical data of the i-th type of running parameters, n be the number of samples, w be the bandwidth, and K be the kernel function.

[0024] A further improvement of this invention is that the kernel function K is expressed using a Gaussian function:

[0025]

[0026] A further improvement of the present invention is that the step of calculating the over-limit feedback value of each type of operating parameter based on the threshold limit and typical value of each type of operating parameter is performed using the following formula:

[0027]

[0028] In the formula, Q j This represents the out-of-limit feedback value of the j-type runtime parameter. x is the threshold limit for the j-th type of operating parameter. tp，j This represents the typical value of the j-th type of runtime parameter.

[0029] A further improvement of the present invention is that, in the step of constructing three-dimensional coordinates based on the cluster where the smart manhole cover is located, the production loss weight, and the over-limit feedback value, the three coordinate axes of the three-dimensional coordinates are respectively the cluster where the smart manhole cover is located; the over-limit feedback value; and the product of the over-limit feedback value and the production loss weight.

[0030] Secondly, the present invention provides an intelligent manhole cover operation status assessment device, comprising:

[0031] Division module: Used to obtain the operating parameters of smart manhole covers within a preset area and divide the operating parameters into several categories;

[0032] Clustering module: Used to obtain smart manhole covers within a preset area, set a fault loss impact degree for each smart manhole cover, and cluster the smart manhole covers within the preset area according to the fault loss impact degree to obtain several clusters;

[0033] Production loss weight generation module: used to obtain historical data of smart manhole covers within a preset area, and to obtain production loss weights for different types of operating parameters based on historical data and operating parameters;

[0034] Over-limit feedback value calculation module: used to separate over-limits of operating parameters based on historical data and obtain over-limit feedback values;

[0035] Coordinate establishment module: used to construct three-dimensional coordinates based on the cluster where the smart manhole cover is located, production loss weight, and limit exceedance feedback value;

[0036] Evaluation module: Used to evaluate the operating status of smart manhole covers based on three-dimensional coordinates.

[0037] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for evaluating the operating status of an intelligent manhole cover.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for evaluating the operating status of an intelligent manhole cover.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects:

[0040] 1. This invention obtains and processes the operating parameters of the smart manhole cover to get clustering results, production loss weights, and limit-breaking feedback values. Based on the clustering results, production loss weights, and limit-breaking feedback values, a three-dimensional coordinate system is constructed. By comprehensively judging the operating status of the manhole cover by integrating multiple data, the loss caused by different operating parameters is evaluated, thus improving the accuracy of the evaluation.

[0041] 2. This invention conducts a comprehensive evaluation, comparative ranking analysis of multiple smart manhole cover modules in a regional smart manhole cover cluster system, avoiding the differences between modules caused by evaluating only each smart manhole cover module, and improving the accuracy of cluster system evaluation.

[0042] 3. This invention performs cluster analysis on regional smart manhole cover clusters based on importance, and combines real-time monitoring data from various operations. Using kernel density estimation, it statistically analyzes the distribution patterns of parameters such as temperature, humidity, and partial discharge current inside the manholes, conducts high-frequency fault type analysis of smart manhole covers, and constructs an autonomous early warning mechanism. This effectively overcomes the reliance on subjective judgment in traditional assessments, while also considering historical operating parameters, further improving the reliability of the assessment and providing support for a comprehensive assessment of smart manhole cover operation and surrounding environmental information.

[0043] 4. This invention constructs a three-dimensional coordinate system based on the importance level, over-limit parameters, and failure loss assessment of smart manhole covers. During the evaluation of smart manhole covers in a region, the three-dimensional parameters can be evaluated sequentially, allowing the backend main station to more comprehensively and objectively analyze the importance of the manhole cover module and the potential impact of failures. This is more conducive to maintenance personnel and decision-makers taking targeted maintenance measures, especially in situations where resources are scarce, resulting in higher operating efficiency. Attached Figure Description

[0044] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0045] In the attached diagram:

[0046] Figure 1 This is a flowchart of a smart manhole cover operation status evaluation method according to the present invention;

[0047] Figure 2 This is a structural block diagram of an intelligent manhole cover operation status assessment device according to the present invention;

[0048] Figure 3 This is a schematic diagram of the intelligent manhole cover module in the intelligent manhole cover operation status evaluation method of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0050] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0051] Example 1

[0052] A method for assessing the operational status of smart manhole covers, such as Figure 1 As shown, it includes the following steps:

[0053] S1. Obtain the operating parameters of the smart manhole covers within the preset area and classify the operating parameters into several categories;

[0054] Specifically, such as Figure 3 The image shows a conventional smart manhole cover. The conventional smart manhole cover includes an operating module, which includes a communication module, a liquid level sensing module, a lock motor module, a temperature and humidity sensing module, a gas sensing module, and a micro switch module. The operating module of a smart manhole cover may include not only the above-mentioned components, but may also include other modules, depending on the actual situation.

[0055] The operating parameters are the parameters for each operating module. For example, the temperature and humidity data of the smart manhole cover can be obtained from the temperature and humidity sensing module.

[0056] When classifying runtime parameters, they are categorized into different classes based on different runtime modules;

[0057] S2. Obtain the smart manhole covers within the preset area, set the failure loss impact degree for each smart manhole cover, and cluster the smart manhole covers within the preset area according to the failure loss impact degree to obtain several clusters;

[0058] Specifically, in S2, in the step of setting the failure loss impact of each smart manhole cover, the failure loss impact is manually set according to the geographical importance of the smart manhole cover. For example, if the first manhole cover is set in an area with high traffic, then the failure loss impact of the first manhole cover will be set to be larger accordingly. The specific setting is based on the actual situation.

[0059] The k-means clustering algorithm is used for clustering. All smart manhole covers within the preset area are taken as the sample group, and each smart manhole cover is taken as a sample point to be assigned. Clustering is performed according to the impact of failure loss. Assume that n manhole cover modules are taken as the given data sample X, and N points X = {X1, X2, ..., X...}N Each point has an attribute that determines the degree of impact of failure losses.

[0060] The goal of the K-means algorithm is to group N points into several predefined clusters based on their mutual similarity. In this invention, the points are divided into three categories, and each point belongs to one and only one of the clusters with the smallest distance to the cluster center.

[0061] First, three cluster centers {V1, V2, V3} need to be initialized. The distance from each point to the cluster center is then calculated using the following formula:

[0062]

[0063] In the formula, X i V represents the i-th object. j Let X represent the j-th cluster center. ik V represents the type of the k-th characteristic parameter of the i-th object. jk This represents the type of the k-th feature parameter of the j-th cluster center. The distance between the points and the cluster centers is traversed in a "many-to-many" pattern, and the points are assigned to the clusters closest to the cluster centers, resulting in three clusters {S1, S2, S3} that consider the impact of fault loss, representing three levels of smart manhole covers: very important, generally important, and ordinary.

[0064] S3. Obtain historical data of smart manhole covers within the preset area, and obtain the production loss weights of different types of operating parameters based on the historical data and operating parameters;

[0065] Specifically, S3 includes the following steps:

[0066] S31. Generate sample data based on historical data;

[0067] S32. Divide the running parameters by type;

[0068] S33. Calculate the production loss weight for each type of operating parameter using the entropy weight method based on the sample data.

[0069] Specifically, other weighting methods can also be used to calculate the production loss weight in step S3;

[0070] Specifically, in S3, production loss weights are set for each type of operating parameter based on historical data. The production loss weights are represented by μ, {μ1, μ2, ..., μ...} m}, where the weights of each type of data satisfy the relational expression

[0071] μ1+μ2+…+μ m =1;

[0072] Here, it is mainly considered that the intelligent manhole cover corresponds to multiple types of parameters, but the loss assessment caused by the overlimit of different parameter types is different. Therefore, the production loss caused by the overlimit of the intelligent manhole cover parameters is evaluated in the mode of weighting different types of parameters.

[0073] S4. Separate the overlimit of the operating parameters according to historical data to obtain the overlimit feedback value;

[0074] Specifically, S4 includes the following steps:

[0075] S41. Establish a kernel density estimation analysis model based on historical data and operating parameters:

[0076]

[0077] In the formula, x is the operating parameter, f w is the probability density function of the i - type operating parameter of the intelligent well, x i is the historical data of the i - type operating parameter, n is the number of samples, w is the bandwidth, and K is the kernel function;

[0078] The kernel function K is expressed by a Gaussian function:

[0079] <�

[0080] S42. Generate a kernel density change curve according to the kernel density estimation analysis model, obtain the maximum value of each type of operating parameter according to the kernel density change curve, and use the maximum value of each type of operating parameter as the typical value of each type of operating parameter;

[0081] x tp = argmax f w (x);

[0082] In the formula, x tp is the typical value of this type of operating parameter;

[0083] Generate a set of variable typical values according to the typical values of each type of operating parameter, {x tp |x tp,j , 0 < j ≤ m}, where m is the number of variables;

[0084] S43. Set a threshold limit for each type of operating parameter;

[0085] Specifically, the threshold limit is set according to experience;

[0086] S44. Calculate the overlimit feedback value of each type of operating parameter according to the threshold limit and typical value of each type of operating parameter;

[0087]

[0088] In the formula, Q jThis represents the out-of-limit feedback value of the j-type runtime parameter. x is the threshold limit for the j-th type of operating parameter. tp，j These are typical values ​​for the j-th type of runtime parameters;

[0089] Q j The smaller the value, the better the variable. The greater the likelihood of exceeding the limit, the more frequently the corresponding fault types can be identified, allowing for targeted prevention measures. Q can also be set. j The system can automatically issue warnings about critical values ​​before measured values ​​exceed limits, reminding maintenance personnel to eliminate potential hazards before an accident occurs.

[0090] S5. Construct three-dimensional coordinates based on the cluster where the smart manhole cover is located, the production loss weight, and the over-limit feedback value;

[0091] Specifically, in S5, the three-dimensional coordinates represent that each smart manhole cover module corresponds to a feature parameter sequence L, including m feature parameters:

[0092] L = {L1, L2, ..., L} m};

[0093] Each feature parameter in the sequence can be represented by three-dimensional coordinates:

[0094] L j =L(S) i Q j μ j Q j ), where 1≤j≤m, 1≤i≤3;

[0095] These coordinates represent the importance level, over-limit parameters, and failure loss assessment of the smart manhole cover, respectively. Therefore, they can also be used as the basis for assessing the operating status of the manhole cover according to the above three types of characteristics.

[0096] S6. Evaluate the operating status of the smart manhole cover based on three-dimensional coordinates.

[0097] Specifically, S6 includes the following steps:

[0098] The importance of a smart manhole cover is determined by the cluster in which it belongs.

[0099] The probability of a certain type of operating parameter exceeding the limit is determined based on the over-limit feedback value. If the over-limit probability is greater than or equal to 50%, it is determined that the operating parameter needs to be monitored. If the over-limit probability is less than 50%, it is determined that no attention is needed.

[0100] The severity of the loss caused by a failure due to a certain type of operating parameter is determined by the product of the feedback value of the out-of-limit operation of a certain type of operating parameter and the weight of the production loss.

[0101] For example, a certain smart manhole cover belongs to the S1 group, with a small Q value and μ.j Q j Medium. Therefore, the evaluation results for this smart manhole cover are as follows:

[0102] 1) First, smart manhole covers belong to the S1 group, which is of high importance and therefore requires special attention;

[0103] 2) Secondly, the Q value of the temperature parameter of the smart manhole cover is relatively small, which means that it is very likely to exceed the limit. Therefore, the temperature parameter needs to be closely monitored.

[0104] 3) Again, the μ of smart manhole covers j Q j Medium means that the failure loss assessment is medium, so a medium level of concern is sufficient in terms of loss.

[0105] Example 2

[0106] A smart manhole cover operation status assessment device, such as Figure 2 As shown, it includes:

[0107] Division module: Used to obtain the operating parameters of smart manhole covers within a preset area and divide the operating parameters into several categories;

[0108] Specifically, the operating parameters are the parameters of each operating module in the smart manhole cover;

[0109] Specifically, when classifying operating parameters, they are classified according to the type of operating module;

[0110] Clustering module: Used to obtain smart manhole covers within a preset area, set a fault loss impact degree for each smart manhole cover, and cluster the smart manhole covers within the preset area according to the fault loss impact degree to obtain several clusters;

[0111] Specifically, in the clustering module, when setting the failure loss impact degree for each smart manhole cover, the failure loss impact degree is determined based on the geographical importance of the smart manhole cover.

[0112] All smart manhole covers within a preset area are taken as the sample group, and each smart manhole cover is taken as a sample point to be assigned. Clustering is performed according to the impact of failure loss. Assume that n manhole cover modules are taken as the given data sample X, and N points X = {X1, X2, ..., X...} N Each point has an attribute that determines the degree of impact of failure losses.

[0113] The goal of the K-means algorithm is to group N points into several predefined clusters based on their mutual similarity. In this invention, the points are divided into three categories, and each point belongs to one and only one of the clusters with the smallest distance to the cluster center.

[0114] First, three cluster centers {V1, V2, V3} need to be initialized. The distance from each point to the cluster center is then calculated using the following formula:

[0115]

[0116] In the formula, X i V represents the i-th object. j Let X represent the j-th cluster center. ik V represents the type of the k-th characteristic parameter of the i-th object. jk This represents the type of the k-th feature parameter of the j-th cluster center. The distance between the points and the cluster centers is traversed in a "many-to-many" pattern, and the points are assigned to the clusters closest to the cluster centers, resulting in three clusters {S1, S2, S3} that consider the impact of fault loss, representing three levels of smart manhole covers: very important, generally important, and ordinary.

[0117] Production loss weight generation module: used to obtain historical data of smart manhole covers within a preset area, and to obtain production loss weights for different types of operating parameters based on historical data and operating parameters;

[0118] Specifically, the production loss weight generation module includes:

[0119] Generate sample data based on historical data;

[0120] The runtime parameters are categorized by type;

[0121] The production loss weight for each type of operating parameter is calculated using the entropy weight method based on the sample data.

[0122] Specifically, the production loss weight generation module sets the production loss weight for each type of operating parameter based on historical data. The production loss weight is represented by μ, {μ1, μ2…μ}. m}, where the weights of each type of data satisfy the relational expression

[0123] μ1+μ2+…+μ m =1;

[0124] Over-limit feedback value calculation module: used to separate over-limits of operating parameters based on historical data and obtain over-limit feedback values;

[0125] Specifically, the over-limit feedback value calculation module includes:

[0126] A kernel density estimation and analysis model was established based on historical data and operating parameters:

[0127]

[0128] In the formula, x is the running parameter, f wis the probability density function of the operation parameters of the intelligent well of type i, and x i is the historical data of the operation parameters of type i, n is the number of samples, w is the bandwidth, and K is the kernel function;

[0129] The kernel function K is expressed using a Gaussian function:

[0130]

[0131] Generate a kernel density change curve according to the kernel density estimation analysis model, and obtain the maximum value of each type of operation parameter based on the kernel density change curve;

[0132] Generate a kernel density change curve according to the kernel density estimation analysis model, obtain the maximum value of each type of operation parameter based on the kernel density change curve, and use the maximum value of each type of operation parameter as the typical value of each type of operation parameter;

[0133] x tp = argmax f w (x);

[0134] In the formula, x tp is the typical value of this type of operation parameter;

[0135] Generate a set of variable typical values according to the typical values of each type of operation parameter, {x tp |x tp,j , 0 < j ≤ m}, where m is the number of variables;

[0136] Set a threshold limit for each type of operation parameter;

[0137] Specifically, the threshold limit is set according to experience;

[0138] Calculate the over-limit feedback value of each type of operation parameter according to the threshold limit and the typical value of each type of operation parameter;

[0139]

[0140] In the formula, Q j represents the over-limit feedback value of the j-th type of operation parameter, is the threshold limit of the j-th type of operation parameter, and x tp，j is the typical value of the j-th type of operation parameter;

[0141] Q j The smaller it is, the greater the possibility that the variable exceeds the limit, so as to screen out the corresponding high-frequency fault types and strengthen prevention targeted. It is also possible to set the size of the Q j critical value to give an autonomous warning before the measured value is about to exceed the limit, reminding the operation and maintenance personnel to eliminate potential hazards before an accident occurs.

[0142] Coordinate establishment module: used to construct three-dimensional coordinates based on the cluster where the smart manhole cover is located, production loss weight, and limit exceedance feedback value;

[0143] Specifically, in the coordinate establishment module, the three-dimensional coordinates represent that each smart manhole cover module corresponds to a feature parameter sequence L, including m feature parameters:

[0144] L ＝ {L1,L2…L m};

[0145] Each feature parameter in the sequence can be represented by three-dimensional coordinates:

[0146] L j =L(S) i Q j μ j Q j ), where 1≤j≤m, 1≤i≤3;

[0147] These coordinates represent the importance level, over-limit parameters, and failure loss assessment of the smart manhole cover, respectively. Therefore, they can also be used as the basis for assessing the operating status of the manhole cover according to the above three types of characteristics.

[0148] Evaluation module: Evaluates the operating status of smart manhole covers based on three-dimensional coordinates.

[0149] Example 3

[0150] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0151] Obtain the operating parameters of smart manhole covers within a preset area and classify the operating parameters into several categories;

[0152] Get the smart manhole covers in the preset area, set the failure loss impact degree for each smart manhole cover, and cluster the smart manhole covers in the preset area according to the failure loss impact degree, dividing the smart manhole covers in the preset area into several clusters.

[0153] Acquire historical data and, based on the historical data and operating parameters, obtain the production loss weights for different types of operating parameters;

[0154] Based on historical data, the operating parameters are separated to obtain the over-limit feedback value;

[0155] A three-dimensional coordinate system is constructed based on the cluster to which the smart manhole cover belongs, the production loss weight, and the over-limit feedback value.

[0156] The operating status of smart manhole covers is evaluated based on three-dimensional coordinates.

[0157] Example 4

[0158] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for evaluating the operational status of a smart manhole cover, comprising the following steps:

[0159] Obtain the operating parameters of smart manhole covers within a preset area and classify the operating parameters into several categories;

[0160] Get the smart manhole covers in the preset area, set the failure loss impact degree for each smart manhole cover, and cluster the smart manhole covers in the preset area according to the failure loss impact degree, dividing the smart manhole covers in the preset area into several clusters.

[0161] Acquire historical data and, based on the historical data and operating parameters, obtain the production loss weights for different types of operating parameters;

[0162] Based on historical data, the operating parameters are separated to obtain the over-limit feedback value;

[0163] A three-dimensional coordinate system is constructed based on the cluster to which the smart manhole cover belongs, the production loss weight, and the over-limit feedback value.

[0164] The operating status of smart manhole covers is evaluated based on three-dimensional coordinates.

[0165] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or equivalent to the scope of this invention are included in this invention.

[0166] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the operational status of intelligent manhole covers, characterized in that, Includes the following steps: Obtain the operating parameters of smart manhole covers within a preset area and classify the operating parameters into several categories; Acquire smart manhole covers within a preset area, assign a fault loss impact degree to each smart manhole cover, and cluster the smart manhole covers within the preset area according to the fault loss impact degree to obtain several clusters; Acquire historical data of smart manhole covers within a preset area, and obtain production loss weights for different types of operating parameters based on historical data and operating parameters; Based on historical data, the operating parameters are separated to obtain the over-limit feedback value; A three-dimensional coordinate system is constructed based on the cluster to which the smart manhole cover belongs, the production loss weight, and the over-limit feedback value. The operating status of the smart manhole cover is evaluated based on three-dimensional coordinates; The step of separating operating parameters based on historical data to obtain over-limit feedback values ​​specifically includes: establishing a kernel density estimation analysis model based on historical data and operating parameters; generating a kernel density change curve based on the kernel density estimation analysis model; obtaining the maximum value of each type of operating parameter based on the kernel density change curve; and using the maximum value of each type of operating parameter as the typical value of each type of operating parameter; and setting a threshold limit for each type of operating parameter. Calculate the over-limit feedback value for each type of operating parameter based on the threshold limit and typical value of each type of operating parameter; The step of calculating the over-limit feedback value of each type of operating parameter based on the threshold limit and typical value of each type of operating parameter is performed using the following formula: ； In the formula, Q j This represents the out-of-limit feedback value of the j-type runtime parameter. x is the threshold limit for the j-th type of operating parameter. tp，j This represents the typical value of the j-th type of runtime parameter.

2. The method for evaluating the operating status of an intelligent manhole cover according to claim 1, characterized in that, The step of acquiring historical data of smart manhole covers within a preset area and obtaining production loss weights for different types of operating parameters based on the historical data and operating parameters specifically includes: Generate sample data based on historical data; The runtime parameters are categorized by type; The production loss weight for each type of operating parameter is calculated using the entropy weight method based on the sample data.

3. The method for evaluating the operating status of an intelligent manhole cover according to claim 1, characterized in that, The kernel density estimation analysis model is as follows: In the formula, For running parameters, Let be the probability density function of the operating parameters of smart well type i. Historical data for type i runtime parameters. For the sample size, For bandwidth, This is the kernel function.

4. The method for evaluating the operating status of an intelligent manhole cover according to claim 3, characterized in that, The kernel function K is expressed using a Gaussian function: 。 5. The method for evaluating the operating status of an intelligent manhole cover according to claim 1, characterized in that, In the step of constructing three-dimensional coordinates based on the cluster where the smart manhole cover is located, the production loss weight, and the over-limit feedback value, the three coordinate axes of the three-dimensional coordinates are the cluster where the smart manhole cover is located; the over-limit feedback value; and the product of the over-limit feedback value and the production loss weight.

6. A smart manhole cover operation status assessment device, used to implement the smart manhole cover operation status assessment method according to any one of claims 1 to 5, characterized in that, include: Division module: Used to obtain the operating parameters of smart manhole covers within a preset area and divide the operating parameters into several categories; Clustering module: Used to obtain smart manhole covers within a preset area, set a fault loss impact degree for each smart manhole cover, and cluster the smart manhole covers within the preset area according to the fault loss impact degree to obtain several clusters; Production loss weight generation module: used to obtain historical data of smart manhole covers within a preset area, and to obtain production loss weights for different types of operating parameters based on historical data and operating parameters; Over-limit feedback value calculation module: used to separate over-limits of operating parameters based on historical data and obtain over-limit feedback values; Coordinate establishment module: used to construct three-dimensional coordinates based on the cluster where the smart manhole cover is located, production loss weight, and limit exceedance feedback value; Evaluation module: Used to evaluate the operating status of smart manhole covers based on three-dimensional coordinates.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent manhole cover operation status evaluation method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent manhole cover operation status evaluation method according to any one of claims 1 to 5.

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