A method, device, computer equipment and medium for retrieving hidden danger data information

Abstract

The embodiment of the present invention discloses a method, device, equipment and medium for retrieving hidden danger data information. The method includes: determining the equipment operation status data; obtaining the weight value associated with each equipment operation status data, and calculating the equipment health index with each equipment operation status data; determining the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determining the equipment operation status hidden danger data in the equipment operation status data; retrieving the hidden danger data through a constructed graph database, and retrieving the hidden danger data through a constructed graph database to obtain the hidden danger data information associated with the hidden danger data. The embodiment of the present invention solves the problem that it is difficult to locate hidden dangers and faults in high-voltage power equipment, realizes the ability to effectively monitor high-voltage power equipment, improves the accuracy of hidden danger and fault location, reduces labor costs and saves time costs.

Description

Technical Field

[0001] The embodiments of the present invention relate to computer data processing technology, and more particularly to a hidden danger data information retrieval method, device, computer equipment and medium. Background Art

[0002] Safety supervision of high-voltage power customers is an important part of improving the safe operation level of the power grid. Transformers, high-voltage switchgear, drop-out fuses and other electrical equipment of high-voltage power customers are also the focus of power safety inspections. Timely elimination of equipment safety hazards plays an important role in ensuring the safe operation of the power grid. With the development of Internet of Things technology, the status monitoring system of high-voltage customer power equipment is becoming more and more popular.

[0003] During the invention process, the inventor discovered the defects of the prior art: based on the monitoring parameters, the health index can be calculated to reflect the real-time health status of the equipment. However, due to the complexity and diversity of power equipment, it is difficult to establish a clear relationship between key monitoring parameters and other parameters of the equipment, which makes it difficult for business personnel to perceive hidden dangers and locate faults. The knowledge of equipment failures and safety hazards is scattered in various procedures, standards and document records, lacking a unified management and query method, making it difficult to give full play to its value. Summary of the invention

[0004] The embodiments of the present invention provide a hidden danger data information retrieval method, device, computer equipment and medium to achieve effective monitoring of high-voltage power equipment and improve the accuracy of hidden danger and fault location.

[0005] In a first aspect, an embodiment of the present invention provides a hidden danger data information retrieval method, which includes:

[0006] Determine equipment operating status data;

[0007] Obtaining a weight value associated with each of the equipment operation status data, and calculating a device health index with each of the equipment operation status data;

[0008] Determine the current device health status level according to the device health index;

[0009] If the current equipment health status level is downgraded, determining equipment operation status hidden danger data in the equipment operation status data;

[0010] The hidden danger data is retrieved through the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data.

[0011] In a second aspect, an embodiment of the present invention further provides a hidden danger data information retrieval device, the hidden danger data information retrieval device comprising:

[0012] A device operation status data determination module, used to determine device operation status data;

[0013] The device health index calculation module is used to obtain the weight value associated with each device operation status data, and calculate the device health index with each device operation status data;

[0014] A device health status level determination module, used to determine the current device health status level according to the device health index;

[0015] An equipment operation status hidden danger data determination module, configured to determine equipment operation status hidden danger data in the equipment operation status data if the current equipment health status level is downgraded;

[0016] The hidden danger data information determination module is used to retrieve the hidden danger data from the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data.

[0017] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the hidden danger data information retrieval method as described in any embodiment of the present invention is implemented.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the hidden danger data information retrieval method as described in any embodiment of the present invention is implemented.

[0019] The technical solution provided by the embodiment of the present invention determines the equipment operation status data; obtains the weight value associated with each equipment operation status data, and calculates the equipment health index with each equipment operation status data; determines the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determines the equipment operation status hidden danger data in the equipment operation status data; retrieves the hidden danger data through the constructed map database to obtain the hidden danger data information associated with the hidden danger data. The embodiment of the present invention solves the problem of difficulty in locating hidden dangers and faults in high-voltage power equipment, realizes the ability to effectively monitor high-voltage power equipment, improves the accuracy of hidden danger and fault location, reduces labor costs and saves time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a hidden danger data information retrieval method provided in the first embodiment of the present invention;

[0021] Figure 2 A flowchart of another hidden danger data information retrieval method provided in the second embodiment of the present invention;

[0022] Figure 3 It is a structural schematic diagram of a hidden danger data information retrieval device provided by Embodiment 3 of the present invention;

[0023] Figure 4 It is a structural diagram of a computer device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0025] Embodiment 1

[0026] Figure 1 A flowchart of a hidden danger data information retrieval method provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation of locating hidden dangers and faults of high-voltage power equipment. The method of this embodiment can be executed by a hidden danger data information retrieval device, which can be implemented by software and / or hardware, and the device can be configured in a server or terminal device.

[0027] Accordingly, the method specifically comprises the following steps:

[0028] S110: Determine equipment operation status data.

[0029] The device operation status data may be a parameter describing the current device operation status, and data may be collected from the device under test.

[0030] It is understandable that a device operation status data set can be collected at the current time, wherein the device operation status data is obtained by standardizing multiple pieces of original device operation status data in the device operation status data set. The device operation status data can describe the data of parameters such as temperature, voltage, and power of the current device, and data collection operations can be performed from various sensors on the device to be tested. That is, temperature data can be collected from the temperature sensor, voltage data can be collected from the voltage sensor, and power data can be collected from the power sensor.

[0031] Optionally, the determining of the equipment operation status data includes: obtaining the data type of the original equipment status data of each equipment operation in the equipment operation status data set, wherein the data type includes the minimum optimal type, the intermediate optimal type and the maximum optimal type; obtaining the historical maximum equipment operation status data, the historical minimum equipment operation status data and the historical optimal equipment operation status data associated with the original equipment operation status data of each equipment; and performing standardization processing on the original equipment operation status data of each equipment according to the following formula to obtain the operation status data of each equipment;

[0032] Among them, x i It is the original running status data of the equipment. This is the largest equipment operation status data in history. It is the historical minimum equipment operation status data. It is the historical optimal equipment operating status data.

[0033] Among them, the device original state data can be data collected from the sensor in the device under test, which is data that has not been standardized. The data type can be a type that describes the device original state data. Different device original state data can have different data attributes. For example, when the device original state data is temperature, the smaller the temperature of the device, the better. Therefore, the data type of the device original state data is the minimum optimal type. The minimum optimal type can be a data type that indicates that the smaller the device original state data corresponding to the device under test is, the better the state of the device under test is. The intermediate optimal type can be a data type that indicates that the state of the device under test is better when the device original state data corresponding to the device under test is in an intermediate state (not in a very large or very small state). The maximum optimal type can be a data type that indicates that the larger the device original state data corresponding to the device under test is, the better the state of the device under test is.

[0034] It can be understood that the historical maximum device operating status data may be the value of the historical maximum data existing in the device operating status data. The historical minimum device operating status data may be the value of the historical minimum data existing in the device operating status data. The historical optimal device operating status data may be the value of the historical best data existing in the device operating status data.

[0035] In this embodiment, the data type of the original operation status data is first determined, and then the original operation status data is standardized according to the corresponding formula above, so as to obtain the device operation status data.

[0036] The advantage of this setting is that by standardizing the original operating status data, the obtained equipment operating status data is more accurate, so that it is possible to better judge whether there are hidden dangers in the equipment.

[0037] S120: Obtain a weight value associated with each device operation status data, and calculate a device health index with each device operation status data.

[0038] The weight value may be a value that measures the weight ratio of each device's operating status data. The device health index may be an index that describes the health status of the device to be tested. It is understood that the larger the device health index, the healthier the device. The corresponding device health status level may also be calculated based on the device health index.

[0039] Optionally, the acquiring a weight value associated with each of the device operation status data and calculating a device health index with each of the device operation status data includes: determining the device health index HI according to the following formula; Among them, h i is the equipment operation status data, β i is the weight value, and n is the number of corresponding equipment operation status data in the equipment operation status data set.

[0040] For example, it is assumed that the equipment operation status data is H = {h1, h2, h3, ..., h i ,…,h n}, the corresponding weight value can be obtained as β = {β1, β2, β3, …, β i ,…,β n}, the device health index can be calculated accordingly, that is,

[0041] It can be understood that since the equipment operation status data is the data obtained by standardizing the original equipment operation status data, and the size range of the equipment operation status data is between 0 and 1. Since the size range of the weight value is also between 0 and 1, the calculated The value of is also between 0 and 1, and thus the device health index is between 0 and 5.

[0042] 130. Determine a current device health status level according to the device health index.

[0043] The current equipment health status level may be a level measurement standard to describe the current equipment health level. The current equipment health status level may include a health level, a sub-health level, a general defect level, a serious defect level, and a dangerous defect level.

[0044] It is understandable that the corresponding current device health status level can be found according to each calculated device health index, so as to determine whether the current device has hidden dangers according to the current device health status level.

[0045] Optionally, determining the current equipment health status level based on the equipment health index includes: when the equipment health index is greater than or equal to a first value and less than or equal to a second value, the current equipment health status level is a healthy level; when the equipment health index is greater than or equal to a third value and less than the first value, the current equipment health status level is a sub-healthy level; when the equipment health index is greater than or equal to a fourth value and less than the third value, the current equipment health status level is a general defect level; when the equipment health index is greater than or equal to a fifth value and less than the fourth value, the current equipment health status level is a severe defect level; when the equipment health index is greater than or equal to a sixth value and less than the fifth value, the current equipment health status level is a dangerous defect level.

[0046] The first value may be a first threshold for measuring the health index of the device. The second value may be a second threshold for measuring the health index of the device. The third value may be a third threshold for measuring the health index of the device. The fourth value may be a fourth threshold for measuring the health index of the device. The fifth value may be a fifth threshold for measuring the health index of the device. The sixth value may be a sixth threshold for measuring the health index of the device.

[0047] It can be understood that if the size of the current device health index is between the first value and the second value, the current device health status level is a healthy level; if the size of the current device health index is between the second value and the third value, the current device health status level is a sub-healthy level; if the size of the current device health index is between the third value and the fourth value, the current device health status level is a general defect level; if the size of the current device health index is between the fourth value and the fifth value, the current device health status level is a serious defect level; if the size of the current device health index is between the fifth value and the sixth value, the current device health status level is a dangerous defect level.

[0048] Exemplarily, as mentioned above, the size of the equipment health index is between 0 and 5. Therefore, it can be further determined that if the current equipment health index is greater than or equal to 4 and less than or equal to 5, the current equipment health status level is a healthy level; if the current equipment health index is greater than or equal to 3 and less than 4, the current equipment health status level is a sub-healthy level; if the current equipment health index is greater than or equal to 2 and less than 3, the current equipment health status level is a general defect level; if the current equipment health index is greater than or equal to 1 and less than 2, the current equipment health status level is a serious defect level; if the current equipment health index is greater than or equal to 0 and less than 1, the current equipment health status level is a dangerous defect level.

[0049] The advantage of this setting is that it can more clearly and directly judge and determine the level of the equipment health index, so that it can more accurately judge whether the current equipment health status level has been degraded, which can further save time costs and better accurately locate the fault of the equipment under test.

[0050] S140: If the current equipment health status level is downgraded, determine equipment operation status hidden danger data in the equipment operation status data.

[0051] The equipment operation status hidden danger data may be data that may contain hidden dangers in the equipment operation status data, that is, equipment operation status data that may cause the current equipment health status level corresponding to the equipment under test to be degraded.

[0052] It is understandable that if the current equipment health status level is downgraded, it means that compared with the current equipment health status level in the previous cycle, the current equipment health status level is relatively low, that is, there may be problems with the current equipment under test, and fault location and maintenance are required to avoid causing greater equipment damage, thereby causing losses in manpower, material and financial resources.

[0053] Optionally, if the current equipment health status level is downgraded, determining equipment operation status hidden danger data in the equipment operation status data, including: obtaining a reference equipment operation status data set and a reference equipment health status level of the current cycle of the equipment under test; wherein the reference equipment operation status data set contains multiple reference equipment operation status data; judging whether the current equipment health status level is downgraded based on the reference equipment health status level of the equipment under test, and if so, determining the equipment operation status hidden danger data in the equipment operation status data.

[0054] The reference device operation status data set may be a data set containing multiple reference device operation status data. Specifically, the reference device operation status data set may be a data set of the device operation status of the previous cycle stored in the current device under test. Of course, the reference device operation status data set of the device under test also needs to be updated in real time to ensure comparison with the most recent cycle.

[0055] It is understandable that the reference device health status level can be a level describing the device health status of the previous cycle. Specifically, the device health status level of the previous cycle stored in the current device under test is obtained. Of course, the reference device health status level of the device under test also needs to be updated in real time to ensure comparison with the most recent cycle.

[0056] Specifically, the reference device operation status data may be device operation status data describing the previous cycle. It is understandable that the reference device operation status data is obtained from a reference device operation status data set, and each reference device operation status data set contains multiple reference device operation status data.

[0057] In this embodiment, the current device health status level is compared with the reference device health status level to determine whether the current device health status level has been degraded, that is, the health status of the current device under test has changed compared to the previous one. Therefore, it is necessary to determine the device operation status hidden danger data in the case of degradation.

[0058] Optionally, determining the equipment operation status hidden danger data in the equipment operation status data includes: according to formula z i =β i (h i ′-h i ), and respectively calculate the equipment health status degradation contribution z corresponding to each of the equipment operation status data i , where h i ' is the reference equipment operation status data; the contribution of each equipment health status degradation is sorted from large to small, and the equipment operation status data corresponding to the first N equipment health status degradation contributions are determined to be the equipment operation status hidden danger data.

[0059] Among them, the contribution of equipment health status degradation can be the degree of influence of equipment operating status data on equipment health status degradation. When the target equipment operating status data changes greatly, it may have a greater impact on the equipment health status, that is, it may be equipment operating status hidden danger data.

[0060] Exemplarily, assume that there are three pieces of equipment operating status data, with a temperature of a, a power of b, and a voltage of c. Because the current equipment health status level is downgraded, the previously acquired reference equipment operating status data have a temperature of a1, a power of b1, and a voltage of c1. And the weight corresponding to the temperature is β1, the weight corresponding to the power is β2, and the weight corresponding to the voltage is β3. It can be further calculated that the contribution of the temperature to the equipment health status degradation is z1=β1(a1-a); the contribution of the power to the equipment health status degradation is z2=β2(b2-b); and the contribution of the voltage to the equipment health status degradation is z3=β3(c3-c). Assuming that z1>z2>z3, the equipment operating status data corresponding to the first two equipment health status degradation contributions are selected as the equipment operating status hidden danger data, that is, the equipment operating status data corresponding to the temperature and power are selected.

[0061] The advantage of this setting is that by calculating the contribution of equipment health status degradation, it is possible to more clearly reflect which equipment operating status data causes the degradation of the equipment health status level, thereby more accurately determining the equipment operating status hidden danger data and better locating and processing equipment failures.

[0062] S150, searching the hidden danger data in the constructed atlas database to obtain hidden danger data information associated with the hidden danger data.

[0063] Among them, the atlas database can be a database storing the atlases of various electric high-voltage equipment. It can be understood that storing in the form of atlas can more accurately determine the subordinate relationship of various electric high-voltage equipment, thereby being more organized and faster to retrieve. The hidden danger data information can be the relevant data information about various electric high-voltage equipment fed back by the atlas database through searching for hidden danger data.

[0064] It can be understood that when temperature-related hidden danger data is input into the atlas database, the atlas database will generate relevant conditional statements for the hidden danger data for retrieval. The hidden danger data information can be obtained through the retrieval and can be fed back to relevant staff.

[0065] The technical solution provided by the embodiment of the present invention determines the equipment operation status data; obtains the weight value associated with each equipment operation status data, and calculates the equipment health index with each equipment operation status data; determines the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determines the equipment operation status hidden danger data in the equipment operation status data; retrieves the hidden danger data through the constructed map database to obtain the hidden danger data information associated with the hidden danger data. The embodiment of the present invention solves the problem of difficulty in locating hidden dangers and faults in high-voltage power equipment, realizes the ability to effectively monitor high-voltage power equipment, improves the accuracy of hidden danger and fault location, reduces labor costs and saves time costs.

[0066] Embodiment 2

[0067] Figure 2 A flowchart of another method for retrieving hidden danger data information provided in Embodiment 2 of the present invention. This embodiment is optimized based on the above embodiments. In this embodiment, before the hidden danger data is retrieved through the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data, it also includes the construction of the atlas database.

[0068] Accordingly, the method specifically comprises the following steps:

[0069] S210: Determine equipment operation status data.

[0070] S220: Obtain a weight value associated with each device operation status data, and calculate a device health index with each device operation status data.

[0071] S230: Determine the current device health status level according to the device health index.

[0072] S240: If the current equipment health status level is downgraded, determine equipment operation status hidden danger data in the equipment operation status data.

[0073] S250: Acquire device information data text, and preprocess the device information data text to obtain preprocessed device information data text.

[0074] The device information data text may be data information describing each electric high voltage device in a text form. The pre-processed device information data text may be a pre-processed device information data text, which does not contain redundant data information such as punctuation marks.

[0075] S260: Input the pre-processed device information data text into a pre-trained named entity recognition model to obtain a named entity recognition data text.

[0076] The named entity recognition data text includes device information, a reference device operating status data set and a reference device health status level.

[0077] It can be understood that the named entity recognition model can perform equipment modeling on the pre-processed equipment information data text, and can name different information in the pre-processed equipment information data text, so that the pre-processed equipment information data text can better describe various high-voltage power equipment.

[0078] Specifically, the named entity recognition data text may be an information text describing a device, which may specifically include device information, a reference device operating status data set, and a reference device health status level.

[0079] S270. Extract the relationship between the device information, the reference device operating status data set and the reference device health status level through a triple relationship template to construct a graph database.

[0080] The triple relationship template may be a template for establishing an association relationship between three types of parameters, specifically establishing a relationship between the device information, the reference device operating status data set, and the reference device health status level.

[0081] For example, first, the data source is sorted out, including: information on laws, regulations, standards, and procedures related to equipment operation and maintenance, health management in the power industry, as well as previous equipment maintenance, fault records, and other multi-source unstructured text information, as the data source for building the atlas database.

[0082] Furthermore, the knowledge ontology expression model of power equipment safety hazards is designed in a top-down manner, and Protégé is used to build the graph database model layer. The text preprocessing operation is performed to remove punctuation marks from multi-source texts related to equipment hazards based on regular expressions, and then the jieba tool is used for word segmentation, and then the word segmentation results are tagged with part of speech (BIO tagging strategy can be used).

[0083] Accordingly, the Bert-BiLSTM-CRF model (also known as the named entity recognition model) can be used for named entity recognition. First, select some preprocessed texts as training samples to train the Bert-BiLSTM-CRF model, and use the trained model to perform named entity recognition on all preprocessed texts.

[0084] Finally, based on the template-based relationship extraction, the triple relationship templates of high-voltage power equipment information, monitoring parameters, and safety hazards are predefined, and on this basis, the semi-supervised Bootstapping method is used to extract the relationship between entities. Thus, the ontology model built by Protégé is mapped to the Neo4j graph database, and then the extracted triple relationship is imported into the Neo4j graph database to complete the construction of the graph database.

[0085] S280, searching the hidden danger data in the constructed atlas database to obtain hidden danger data information associated with the hidden danger data.

[0086] The technical solution provided by the embodiment of the present invention determines the equipment operation status data; obtains the weight value associated with each equipment operation status data, and calculates the equipment health index with each equipment operation status data; determines the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determines the equipment operation status hidden danger data in the equipment operation status data; obtains equipment information data text, preprocesses the equipment information data text, and obtains preprocessed equipment information data text; inputs the preprocessed equipment information data text into a pre-trained named entity recognition model to obtain a named entity recognition data text; wherein the named entity recognition data text includes equipment information, a reference equipment operation status data set and a reference equipment health status level; extracts the relationship between the equipment information, the reference equipment operation status data set and the reference equipment health status level through a triple relationship template, and constructs a graph database; retrieves the hidden danger data through the constructed graph database to obtain the hidden danger data information associated with the hidden danger data. In this way, it is possible to better integrate the electronic data of high-voltage power equipment to obtain a map database, facilitate hidden danger data retrieval, and thus obtain more accurate hidden danger data information, so as to improve the accuracy of hidden danger and fault location, reduce labor costs and save time costs.

[0087] Embodiment 3

[0088] Figure 3 1 is a schematic diagram of the structure of a hidden danger data information retrieval device provided in the third embodiment of the present invention. The hidden danger data information retrieval device provided in this embodiment can be implemented by software and / or hardware and can be configured in a terminal device or a server. It is used to implement a hidden danger data information retrieval method in the embodiment of the present invention. Figure 3 As shown, the device may specifically include: an equipment operation status data determination module 310, an equipment health index calculation module 320, an equipment health status level determination module 330, an equipment operation status hidden danger data determination module 340 and a hidden danger data information determination module 350.

[0089] Wherein, the device operation status data determination module 310 is used to determine the device operation status data;

[0090] The device health index calculation module 320 is used to obtain the weight value associated with each device operation status data, and calculate the device health index with each device operation status data;

[0091] The device health status level determination module 330 is used to determine the current device health status level according to the device health index;

[0092] An equipment operation status hidden danger data determination module 340 is used to determine equipment operation status hidden danger data in the equipment operation status data if the current equipment health status level is downgraded;

[0093] The hidden danger data information determination module 350 is used to retrieve the hidden danger data from the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data.

[0094] The technical solution provided by the embodiment of the present invention determines the equipment operation status data; obtains the weight value associated with each equipment operation status data, and calculates the equipment health index with each equipment operation status data; determines the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determines the equipment operation status hidden danger data in the equipment operation status data; retrieves the hidden danger data through the constructed map database to obtain the hidden danger data information associated with the hidden danger data. The embodiment of the present invention solves the problem of difficulty in locating hidden dangers and faults in high-voltage power equipment, realizes the ability to effectively monitor high-voltage power equipment, improves the accuracy of hidden danger and fault location, reduces labor costs and saves time costs.

[0095] Based on the above embodiments, the device health index calculation module 320 can be specifically used to: determine the device health index HI according to the following formula; Among them, h i is the equipment operation status data, β i is the weight value, and n is the number of corresponding equipment operation status data in the equipment operation status data set.

[0096] On the basis of the above embodiments, the equipment health status level determination module 330 can be specifically used for: when the equipment health index is greater than or equal to the first value and less than or equal to the second value, the current equipment health status level is a healthy level; when the equipment health index is greater than or equal to the third value and less than the first value, the current equipment health status level is a sub-healthy level; when the equipment health index is greater than or equal to the fourth value and less than the third value, the current equipment health status level is a general defect level; when the equipment health index is greater than or equal to the fifth value and less than the fourth value, the current equipment health status level is a serious defect level; when the equipment health index is greater than or equal to the sixth value and less than the fifth value, the current equipment health status level is a dangerous defect level.

[0097] Based on the above embodiments, the equipment operation status hidden danger data determination module 340 can be specifically used to: obtain a reference equipment operation status data set and a reference equipment health status level of the current cycle of the equipment under test; wherein the reference equipment operation status data set contains multiple reference equipment operation status data; based on the reference equipment health status level of the equipment under test, determine whether the current equipment health status level is downgraded, and if so, determine the equipment operation status hidden danger data in the equipment operation status data.

[0098] Based on the above embodiments, the equipment operation status hidden danger data determination module 340 can be specifically used to: i =β i (h′ i -h i ), and respectively calculate the equipment health status degradation contribution z corresponding to each of the equipment operation status data i , where h′ i For reference, the equipment operation status data is sorted from large to small for the contribution of each equipment health status degradation, and the equipment operation status data corresponding to the first N equipment health status degradation contributions are selected as the equipment operation status hidden danger data.

[0099] On the basis of the above embodiments, the device operation status data determination module 310 can be specifically used to: obtain the data type of the device original status data of each device operation in the device operation status data set, wherein the data type includes the minimum optimal type, the intermediate optimal type and the maximum optimal type;

[0100] Obtain the historical maximum device operating status data, the historical minimum device operating status data, and the historical optimal device operating status data associated with the original operating status data of each device;

[0101] The original operation status data of each device is standardized according to the following formula to obtain the operation status data of each device;

[0102]

[0103] Among them, x i It is the original running status data of the equipment. This is the largest equipment operation status data in history. It is the historical minimum equipment operation status data. It is the historical optimal equipment operating status data.

[0104] On the basis of the above embodiments, it also includes a graph database construction module, which can be specifically used for: before retrieving the hidden danger data through the constructed graph database to obtain the hidden danger data information associated with the hidden danger data, obtaining the equipment information data text, preprocessing the equipment information data text, and obtaining the preprocessed equipment information data text; inputting the preprocessed equipment information data text into a pre-trained named entity recognition model to obtain the named entity recognition data text; wherein the named entity recognition data text includes equipment information, a reference equipment operating status data set and a reference equipment health status level; extracting the relationship between the equipment information, the reference equipment operating status data set and the reference equipment health status level through a triple relationship template to construct a graph database.

[0105] The hidden danger data information retrieval device can execute the hidden danger data information retrieval method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0106] Embodiment 4

[0107] Figure 4 Schematic diagram of the structure of a computer device provided by Embodiment 4 of the present invention. Figure 4 As shown, the device includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of processors 410 in the device can be one or more. Figure 4 A processor 410 is taken as an example; the processor 410, memory 420, input device 430 and output device 440 in the device can be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0108] The memory 420, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the hidden danger data information retrieval method in the embodiment of the present invention (for example, the equipment operation status data determination module 310, the equipment health index calculation module 320, the equipment health status level determination module 330, the equipment operation status hidden danger data determination module 340 and the hidden danger data information determination module 350). The processor 410 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 420, that is, implements the above-mentioned hidden danger data information retrieval method, which includes:

[0109] Determine the equipment operation status data; obtain the weight value associated with each of the equipment operation status data, and calculate the equipment health index with each of the equipment operation status data; determine the current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determine the equipment operation status hidden danger data in the equipment operation status data; retrieve the hidden danger data through the constructed map database to obtain the hidden danger data information associated with the hidden danger data.

[0110] The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 420 may further include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0111] The input device 430 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 440 may include a display device such as a display screen.

[0112] Embodiment 5

[0113] Embodiment 5 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions are used to execute a hidden danger data information retrieval method when executed by a computer processor, the method comprising: determining equipment operation status data; obtaining weight values ​​associated with each of the equipment operation status data, and calculating an equipment health index with each of the equipment operation status data; determining a current equipment health status level according to the equipment health index; if the current equipment health status level is downgraded, determining equipment operation status hidden danger data in the equipment operation status data; searching the hidden danger data through a constructed graph database to obtain hidden danger data information associated with the hidden danger data.

[0114] Of course, the computer-readable storage medium provided in the embodiment of the present invention has computer-readable instructions that are not limited to the method operations described above, and can also execute related operations in the hidden danger data information retrieval method provided in any embodiment of the present invention.

[0115] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk or an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0116] It is worth noting that in the embodiment of the above-mentioned hidden danger data information retrieval device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0117] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A hidden danger data information retrieval method, characterized in that: include: Determine equipment operating status data; Obtaining a weight value associated with each of the equipment operation status data, and calculating a device health index with each of the equipment operation status data; Determine the current device health status level according to the device health index; If the current equipment health status level is downgraded, determining equipment operation status hidden danger data in the equipment operation status data; The hidden danger data is retrieved from the constructed atlas database to obtain hidden danger data information associated with the hidden danger data; The obtaining of the weight value associated with each of the equipment operation status data and calculating the equipment health index with each of the equipment operation status data includes: Determine the equipment health index HI according to the following formula; Among them, h i is the equipment operation status data, β i is the weight value, and n is the number of corresponding equipment operation status data in the equipment operation status data set; Wherein, the determining of the equipment operation status data includes: Acquire the data type of the original state data of each device in the device operation state data set, wherein the data type includes the minimum optimal type, the intermediate optimal type and the maximum optimal type; Obtain the historical maximum device operating status data, the historical minimum device operating status data, and the historical optimal device operating status data associated with the original operating status data of each device; The original operation status data of each device is standardized according to the following formula to obtain the operation status data of each device; Among them, x i It is the original running status data of the equipment. This is the largest equipment operation status data in history. It is the historical minimum equipment operation status data. It is the historical optimal equipment operating status data.

2. The method according to claim 1, characterized in that Determining the current device health status level according to the device health index includes: When the device health index is greater than or equal to the first value and less than or equal to the second value, the current device health status level is the health level; When the device health index is greater than or equal to a third value and less than the first value, the current device health status level is a sub-health level; When the equipment health index is greater than or equal to the fourth value and less than the third value, the current equipment health status level is a general defect level; When the device health index is greater than or equal to the fifth value and less than the fourth value, the current device health status level is a severe defect level; When the equipment health index is greater than or equal to the sixth value and less than the fifth value, the current equipment health status level is a dangerous defect level.

3. The method according to claim 2, characterized in that If the current equipment health status level is downgraded, determining equipment operation status hidden danger data in the equipment operation status data includes: Obtaining a reference device operating status data set and a reference device health status level of the current cycle of the device under test; wherein the reference device operating status data set includes multiple reference device operating status data; According to the reference device health status level of the device to be tested, it is determined whether the current device health status level is degraded, and if so, the device operation status hidden danger data is determined in the device operation status data.

4. The method according to claim 3, characterized in that Determining the equipment operation status hidden danger data in the equipment operation status data includes: According to the formula z i =β i (h i ′-h i ), and respectively calculate the equipment health status degradation contribution z corresponding to each of the equipment operation status data i , where h i ' is the operating status data of the reference equipment; The contribution degrees of health status degradation of the devices are sorted from large to small, and the device operation status data corresponding to the first N device health status degradation contributions are selected as the device operation status hidden danger data.

5. The method according to claim 1, characterized in that Before the hidden danger data is retrieved from the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data, the method further includes: Acquire device information data text, and preprocess the device information data text to obtain preprocessed device information data text; Inputting the pre-processed device information data text into a pre-trained named entity recognition model to obtain a named entity recognition data text; wherein the named entity recognition data text includes device information, a reference device operating status data set, and a reference device health status level; The equipment information, the reference equipment operation status data set and the reference equipment health status level are subjected to relationship extraction through a triple relationship template to construct a graph database.

6. A hidden danger data information retrieval device, characterized in that: include: The equipment operation status data determination module is used to determine the equipment operation status data; The device health index calculation module is used to obtain the weight value associated with each device operation status data, and calculate the device health index with each device operation status data; A device health status level determination module, used to determine the current device health status level according to the device health index; An equipment operation status hidden danger data determination module, configured to determine equipment operation status hidden danger data in the equipment operation status data if the current equipment health status level is downgraded; A hidden danger data information determination module is used to retrieve the hidden danger data from the constructed atlas database to obtain the hidden danger data information associated with the hidden danger data; The equipment health index calculation module is used to: determine the equipment health index HI according to the following formula; Among them, h i is the equipment operation status data, β i is the weight value, and n is the number of corresponding equipment operation status data in the equipment operation status data set; Among them, the equipment operation status data determination module is used to: obtain the data type of the equipment original status data of each equipment operation in the equipment operation status data set, wherein the data type includes the minimum optimal type, the intermediate optimal type and the maximum optimal type; obtain the historical maximum equipment operation status data, the historical minimum equipment operation status data, and the historical optimal equipment operation status data associated with each equipment original operation status data; perform standardization processing on the original operation status data of each equipment according to the following formula to obtain the operation status data of each equipment; Among them, x i It is the original running status data of the equipment. This is the largest equipment operation status data in history. It is the historical minimum equipment operation status data. It is the historical optimal equipment operating status data.

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, the hidden danger data information retrieval method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the hidden danger data information retrieval method as described in any one of claims 1 to 5 is implemented.

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