Power safety hazard real-time monitoring method and system based on internet of things

Abstract

The application is suitable for the technical field of safety hidden danger monitoring, and particularly relates to a power safety hidden danger real-time monitoring method and system based on the Internet of Things, which comprises the following steps: identifying running equipment in a power system, obtaining equipment numbers of the running equipment, configuring parameter indexes of the running equipment, delimiting fluctuation ranges of the parameter indexes, wherein the parameter indexes at least include rotation speed, temperature and voltage, and calculating importance values of each parameter index; creating a number block corresponding to each parameter index; and driving the running equipment for point inspection through a preset Internet of Things device by selecting a relationship hash function. The application can drive the running equipment for point inspection by selecting a relationship hash function, so as to timely identify early signs of failure, prevent chain reactions, greatly improve the stability of the power system, combine general inspection and point inspection by constructing a monitoring chain, greatly reduce the safety risk of the power system, optimize resource allocation, and further improve the monitoring efficiency of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety hazard monitoring, and particularly relates to a power safety hazard real-time monitoring method and system based on Internet of Things. BACKGROUND

[0002] In the power system, the running state and parameter indexes (such as current, voltage, temperature and vibration, etc.) of power equipment are usually collected and analyzed in real time by using a DCS system, and real-time field data is obtained through a sensor network in combination with Internet of Things technology, so as to realize remote monitoring of key equipment, avoid parameter index deviation, and thus realize timely processing of safety hazards.

[0003] In the data processing process, the DCS system and the Internet of Things equipment compare the collected real-time data with the preset reference standard, and once deviation is detected, the DCS system will automatically trigger an alarm and record abnormal information.

[0004] However, in the above process, only the real-time data and the reference standard are compared, and the correlation between devices is ignored; in actual production, the real-time data may not deviate from the reference standard, but there may be early signs of failure, and since it cannot be identified in time, it is easy to cause potential failure to accumulate and worsen, affecting system stability; therefore, "how to drive the running equipment for safety hazard monitoring based on failure" is a technical problem to be solved by the present application. SUMMARY

[0005] The present application aims to provide a power safety hazard real-time monitoring method and system based on Internet of Things, to solve the problem of "how to drive the running equipment for safety hazard monitoring based on failure" raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0007] A power safety hazard real-time monitoring method based on Internet of Things, the method comprising:

[0008] identifying a running equipment in a power system, obtaining a device number of the running equipment, configuring a parameter index of the running equipment, and delimiting a fluctuation range of the parameter index, wherein the parameter index at least includes: rotation speed, temperature and voltage, and calculating an importance value of each parameter index;

[0009] creating a number block corresponding to the parameter index one by one, collecting real-time data of the parameter index via a preset Internet of Things equipment, and synchronizing the real-time data and the device number to the corresponding number block, sorting the number blocks in descending order of the importance value, and linking to obtain a monitoring chain;

[0010] A correspondence between a sequential hash function and a fluctuation range is established, chain hashing is performed on all number blocks in turn, and the obtained hash values are written into the corresponding number blocks;

[0011] A fault propagation relationship is built using the parameter indicators, a plurality of cascading schemes are generated, evaluation rules and relationship hash functions corresponding to the cascading schemes are constructed, a mapping between the evaluation rules and the relationship hash functions is established, the number blocks in the cascading schemes are chain hashed using the relationship hash functions, and the obtained hash values are written into the corresponding number blocks;

[0012] A trigger time table is constructed, wherein the trigger time table is composed of time items and hash function items, the hash function items are sequential hash functions or relationship hash functions, a target hash function is determined by querying the trigger time table based on the current time, the number block at the front of the monitoring chain is hashed, and a target number block is traversed based on the obtained hash value, real-time data in the target number block is used to query the fluctuation range or the evaluation rules, a monitoring result is determined, and the monitoring result is sent to a preset terminal.

[0013] Further, the steps of identifying the operating equipment in the power system, obtaining the equipment number of the operating equipment, configuring the parameter indicators of the operating equipment, and delimiting the fluctuation range of the parameter indicators include:

[0014] It is judged whether the parameter indicators exceed the fluctuation range, and if they exceed, the corresponding parameter indicators are defined as abnormal items;

[0015] The abnormal items are integrated into the monitoring result.

[0016] Further, the step of calculating the importance value of each parameter indicator includes:

[0017] The importance coefficient of each operating equipment is determined using a pre-constructed fault analysis strategy;

[0018] The priority coefficient of the parameter indicators is configured, the importance coefficient and the priority coefficient are integrated, and the importance value of each parameter indicator is calculated.

[0019] Further, the step of sorting the number blocks in the order from large to small according to the importance values and linking to obtain the monitoring chain includes:

[0020] A root node is embedded into the monitoring chain, and the trigger time table is synchronized into the root node;

[0021] A unified relationship between the root node and all number blocks is established.

[0022] Further, the step of utilizing the relationship hash function to chain hash the number blocks in the chain scheme comprises:

[0023] Defining the number block with the highest order in the monitoring chain as a head block, and sequentially defining a second order block, a third order block,..., and an n order block;

[0024] Hashing the device number in the head block by using the order hash function to obtain a first hash value, and writing the first hash value into the second order block;

[0025] Integrating the first hash value and the device number in the second order block, and hashing by using the order hash function to obtain a second hash value, writing the second hash value into the third order block, and so on.

[0026] Further, the step of utilizing the parameter index to build a fault propagation relationship and generate a plurality of chain schemes comprises:

[0027] Extracting an emergency event of a running device from existing data, traversing the parameter indexes contained in the emergency event to obtain target items;

[0028] Performing causal analysis on the target items to obtain chain schemes.

[0029] Further, the step of utilizing the real-time data in the target number block to query the fluctuation range or evaluation rule comprises:

[0030] Building a communication link between the target number block and the Internet of Things device, and updating the target number block in real time via the communication link;

[0031] Creating a disposal scheme for the emergency event, comparing the monitoring result with the emergency event, and activating the disposal scheme.

[0032] Further, the system comprises:

[0033] A computing module configured to identify a running device in a power system, obtain a device number of the running device, configure a parameter index of the running device, define a fluctuation range of the parameter index, wherein the parameter index at least includes a rotating speed, a temperature, and a voltage, and calculate an importance value of each parameter index;

[0034] A linking module configured to create a number block corresponding to the parameter index one by one, collect real-time data of the parameter index via a preset Internet of Things device, and synchronize the real-time data and the device number to the corresponding number block, sort the number blocks according to the importance value from large to small, and link to obtain a monitoring chain;

[0035] The writing module is configured to establish a correspondence between a sequential hash function and a fluctuation range, sequentially perform chained hashing on all number blocks, and write the obtained hash values into the corresponding number blocks.

[0036] By using the parameter indicators, a fault propagation relationship is built, a plurality of cascading schemes are generated, evaluation rules and relationship hash functions corresponding to the cascading schemes are constructed, a mapping between the evaluation rules and the relationship hash functions is established, the number blocks in the cascading schemes are chained hashed by using the relationship hash functions, and the obtained hash values are written into the corresponding number blocks.

[0037] The sending module is configured to construct a trigger time table, wherein the trigger time table is composed of time items and hash function items, the hash function items are sequential hash functions or relationship hash functions, a target hash function is determined by querying the trigger time table based on a current time, a number block at the front of the monitoring chain is hashed, a target number block is traversed according to an obtained hash value, a monitoring result is determined by querying the fluctuation range or the evaluation rules by using real-time data in the target number block, and the monitoring result is sent to a preset terminal.

[0038] Further, the computing module comprises:

[0039] The defining unit is configured to determine whether the parameter indicators exceed the fluctuation range, and define the corresponding parameter indicators as abnormal items if the parameter indicators exceed the fluctuation range.

[0040] The integrating unit is configured to integrate the abnormal items into the monitoring result.

[0041] The determining unit is configured to determine an importance coefficient of each running device by using a pre-constructed fault analysis strategy.

[0042] The integrating unit is configured to configure a priority coefficient of the parameter indicators, integrate the importance coefficient and the priority coefficient, and calculate an importance value of each parameter indicator.

[0043] Further, the linking module comprises:

[0044] The synchronizing unit is configured to embed a root node into the monitoring chain and synchronize the trigger time table into the root node.

[0045] The planning unit is configured to establish a planning relationship between the root node and all number blocks.

[0046] Compared with the prior art, the present application has the following beneficial effects:

[0047] By determining the fluctuation range, the equipment anomaly can be monitored in time, by calculating the importance value, the key indicators can be monitored, the monitoring resource allocation is optimized, the monitoring efficiency is greatly improved, by selecting the sequential hash function, all real-time data can be checked in sequence according to the importance, the real-time data is avoided to be out of the standard, the data fluctuation deviating from the normal range is quickly identified, by selecting the relationship hash function, the running equipment can be checked, so that the early signs of failure are identified in time, the chain reaction is prevented, the stability of the power system is greatly improved, by constructing the monitoring chain, the general inspection and the point inspection are combined, the safety risk of the power system is greatly reduced, the resource allocation is optimized, and the monitoring efficiency of the power system is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A flowchart of the power safety hidden danger real-time monitoring method based on the Internet of Things is provided for the embodiment of the application.

[0049] Figure 2 A first sub-flowchart of the power safety hidden danger real-time monitoring method based on the Internet of Things is provided for the embodiment of the application.

[0050] Figure 3 A second sub-flowchart of the power safety hidden danger real-time monitoring method based on the Internet of Things is provided for the embodiment of the application.

[0051] Figure 4 A third sub-flowchart of the power safety hidden danger real-time monitoring method based on the Internet of Things is provided for the embodiment of the application.

[0052] Figure 5 A fourth sub-flowchart of the power safety hidden danger real-time monitoring method based on the Internet of Things is provided for the embodiment of the application.

[0053] Figure 6 A composition diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is provided for the embodiment of the application.

[0054] Figure 7 A composition diagram of the calculation module in the power safety hidden danger real-time monitoring system based on the Internet of Things is provided for the embodiment of the application.

[0055] Figure 8 A composition diagram of the link module in the power safety hidden danger real-time monitoring system based on the Internet of Things is provided for the embodiment of the application.

[0056] Figure 9 A composition diagram of the write-in module in the power safety hidden danger real-time monitoring system based on the Internet of Things is provided for the embodiment of the application.

[0057] Figure 10The constituent block diagram of the sending module in the power safety hidden danger real-time monitoring system based on the Internet of Things provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0059] In embodiment 1, Figure 1 The implementation flow of the power safety hidden danger real-time monitoring method based on the Internet of Things provided by the embodiment of the present application is shown, and the following is described in detail as follows:

[0060] S100: identifying the running equipment in the power system, obtaining the equipment number of the running equipment, configuring the parameter index of the running equipment, and determining the fluctuation range of the parameter index, wherein the parameter index at least includes: rotating speed, temperature and voltage, and calculating the importance value of each parameter index.

[0061] In the power system, all running equipment is determined, wherein the running equipment includes power generation equipment, power transformation equipment and power transmission equipment, etc., the equipment number of each running equipment is determined, and the parameter index of each running equipment is configured according to the specific function and operation requirement of the running equipment, wherein the parameter index includes but is not limited to rotating speed, temperature and voltage, etc.; the fluctuation range of each parameter index is determined according to the historical data, technical specification and actual operation environment of the running equipment, etc.; and the importance value of each parameter index is calculated, wherein the greater the importance value of the parameter index, the greater the influence on the power system when the parameter index abnormally fluctuates.

[0062] S200: creating a number block corresponding to the parameter index one by one, collecting real-time data of the parameter index through a preset Internet of Things device, and synchronizing the real-time data and the equipment number to the corresponding number block, sorting the number block according to the order from large to small of the importance value, and linking to obtain a monitoring chain.

[0063] A number block is created for each parameter index, wherein the number block is a data storage and processing unit; real-time data of each parameter index is collected in real time through a preset Internet of Things device, and these real-time data and the corresponding equipment number are synchronously recorded into the corresponding number block, wherein the Internet of Things device includes: a sensor, a data collector, an edge computing device and a control and execution device, etc.

[0064] According to the order of importance value from large to small, all the number blocks are sorted and integrated to generate a monitoring chain; when real-time monitoring is performed, the monitoring effect of the number block with a larger importance value is preferentially guaranteed; for example, when there is a bottleneck in the monitoring resource, the real-time data and the fluctuation range in the number block are compared in turn according to the order of importance value from large to small in the monitoring chain, and when it is found that the real-time data of a certain number block exceeds the fluctuation range, the corresponding number block is determined as an abnormal number block; in other words, the parameter index corresponding to the abnormal number block is out of standard.

[0065] S300: Establish the correspondence between the sequential hash function and the fluctuation range, sequentially chain hash all the number blocks, and write the obtained hash value into the corresponding number block.

[0066] The sequential hash function is selected, and the sequential hash function is determined as the trigger head of the fluctuation range, so that when the sequential hash function is called, the fluctuation range is also extracted at the same time; the sequential hash function is used to chain hash the device numbers in all the number blocks, and the obtained hash value is filled into the corresponding number block.

[0067] Using the parameter index, a fault propagation relationship is built, a plurality of chain schemes are generated, an evaluation rule and a relationship hash function corresponding to the chain scheme are constructed, a mapping between the evaluation rule and the relationship hash function is established, the number block in the chain scheme is chain hashed by using the relationship hash function, and the obtained hash value is written into the corresponding number block.

[0068] According to the possible propagation path and influence range of the device fault, the relationship between the parameter indexes is determined, the fault propagation relationship is obtained, one or more chain schemes are generated for each fault propagation relationship, and an evaluation rule and a relationship hash function are determined for each chain scheme; the number block in the chain scheme is chain hashed by using the relationship hash function, and the obtained hash value is written into the corresponding number block.

[0069] When a general survey is performed, all the number blocks are sequentially hashed by using the sequential hash function, and the real-time data and the fluctuation range are compared; when different chain schemes need to be point inspected, how to determine which parameter indexes need to be monitored, at this time, according to the chain scheme, the corresponding relationship hash function is determined, the device number of the number block ranked first in the monitoring chain is hashed to obtain a hash value, the number block containing the hash value is found out, and the real-time data of the found number block is compared with the evaluation rule; the next number block is found out and hashed, and so on; thereby the number block point inspection is completed, and the monitoring result is obtained.

[0070] For example, the existing power generation speed, generator power, transformer temperature and circuit breaker state four parameter indicators are used to build multiple fault propagation relationships, such as: generator→transformer, generator→transformer→circuit breaker; multiple cascading schemes are generated, for example, generator speed anomaly→generator overload→transformer temperature too high; when monitoring the above cascading scheme, the number of blocks corresponding to the generator speed, generator power and transformer temperature needs to be analyzed specifically.

[0071] Each cascading scheme corresponds to an evaluation rule, and the evaluation rule is generated according to the emergency event. Each evaluation rule corresponds to a sequential hash function; for example, in the parameter indicator of generator speed, the corresponding fluctuation range is 1000RPM-1800RPM, and the evaluation rule can be that if the speed of the generator is lower than 1200RPM and maintains for more than 15 seconds; by combining real-time data such as generator power and transformer temperature, early signs of failure can be discovered in time.

[0072] S400: Construct a trigger time table, wherein the trigger time table is composed of time items and hash function items, the hash function item is a sequential hash function or a relationship hash function, based on the current time, the trigger time table is queried to determine the target hash function, the number block with the highest order in the monitoring chain is hashed, and based on the obtained hash value, the target number block is traversed, the real-time data in the target number block is used to query the fluctuation range or the evaluation rule, the monitoring result is determined, and the monitoring result is sent to a preset terminal.

[0073] The trigger time table is constructed, and the trigger time table is composed of multiple time items and hash function items. Each time item represents a specific time point, and the hash function item includes a sequential hash function or a relationship hash function, wherein the time items corresponding to the sequential hash function are relatively small.

[0074] Based on the current time, the trigger time table is queried to identify the target hash function corresponding to the current time. If the hash function item corresponding to the current time is a sequential hash function, all number blocks in the monitoring chain are processed in order from high to low according to the importance value. The specific monitoring method is: comparing the size of the real-time data in each number block with the fluctuation range; if the hash function item corresponding to the current time is a relationship hash function, only the corresponding number block is compared according to the evaluation rule according to the cascading scheme; according to the comparison result of the fluctuation range or the evaluation rule, the monitoring result is determined, and the monitoring result is sent to a preset terminal, wherein the preset terminal can be a power system maintenance personnel terminal.

[0075] In the embodiment, the monitoring process in S400 is continued to be extended; for example, a plurality of parameter indexes are traversed in the running equipment, and a plurality of number blocks A, B, C, D, … corresponding to the plurality of parameter indexes are established; the sequential hash function and a plurality of relational hash functions are used to hash A to obtain different hash values, which are respectively denoted as, and etc., and the obtained hash values are written into the corresponding number blocks according to the chain scheme; when monitoring is needed, only the corresponding hash function needs to be called, and A is hashed, the number block in which the hash value is located is determined as a target number block, and the real-time data in the target number block is analyzed and processed by using the evaluation rule, and the target number block is continued to be hashed, and so on. In the above manner, a plurality of monitoring channels can be determined by selecting different hash functions, and the number blocks in each monitoring channel are compared and analyzed by using the evaluation rule and the fluctuation range, so that abnormal parameter indexes and failure precursors are determined.

[0076] In embodiment 2, Figure 2 The implementation process of the power safety hidden danger real-time monitoring method based on the Internet of Things is shown, and the steps of identifying the running equipment in the power system, obtaining the equipment number of the running equipment, configuring the parameter indexes of the running equipment, and delimiting the fluctuation range of the parameter indexes are described as follows:

[0077] S101: It is judged whether the parameter index exceeds the fluctuation range, and if yes, the corresponding parameter index is defined as an abnormal item.

[0078] If the parameter index exceeds the fluctuation range, the corresponding parameter index is defined as an abnormal item; all the abnormal items are integrated to generate an additional chain scheme, and the corresponding evaluation rule and relational hash function are configured.

[0079] S102: The abnormal items are integrated into the monitoring result.

[0080] In embodiment 3, Figure 2 The implementation process of the power safety hidden danger real-time monitoring method based on the Internet of Things is shown, and the steps of calculating the importance value of each parameter index are described as follows:

[0081] S103: The importance coefficient of each running equipment is determined by using the pre-constructed failure analysis strategy.

[0082] According to the failure analysis strategy, the importance coefficient of each running equipment is determined, wherein the failure analysis strategy is also a specific analysis method of the failure; when the running equipment fails, the failure cause is identified, and the importance coefficient of each running equipment is determined, wherein if the failure of a power equipment may cause a chain reaction or a serious safety problem, the importance coefficient of the power equipment is relatively large.

[0083] S104: configuring a priority coefficient of each parameter index, integrating the importance coefficient and the priority coefficient, and calculating an importance value of each parameter index.

[0084] In the running device, the priority coefficient of each parameter index is determined, and the greater the priority coefficient, the greater the influence of the parameter index on the stability of the running device.

[0085] In embodiment 4, Figure 3 The implementation process of the power safety hidden danger real-time monitoring method based on the Internet of Things is shown, and the steps of sorting the number blocks in the order from large to small according to the importance values and linking to obtain the monitoring chain are described as follows:

[0086] S201: embedding a root node into the monitoring chain, and synchronizing the trigger time table to the root node.

[0087] The root node is embedded into the monitoring chain, and the corresponding time item is determined by querying the trigger time table using the root node and the current time.

[0088] S202: establishing a unified relationship between the root node and all number blocks.

[0089] All number blocks are managed by the root node, which avoids disorder in the sorting of number blocks.

[0090] In embodiment 5, the step of using the relationship hash function to perform chain hashing on the number blocks in the chain scheme includes:

[0091] The number block sorted first in the monitoring chain is defined as a head block, and the second order block, the third order block,..., and the n-th order block are sequentially defined;

[0092] The device number in the head block is hashed using the order hash function to obtain a first hash value, and the first hash value is written into the second order block.

[0093] The first hash value and the device number in the second order block are integrated, and the order hash function is used for hashing to obtain a second hash value, which is written into the third order block, and the process is repeated.

[0094] The order hash is chain hashing for all number blocks, and the relationship hash is chain hashing for only the number blocks corresponding to the chain scheme; in other words, each number block will be hashed at least once, and the number of hashing has no upper limit, and after hashing, the hash value is stored in the corresponding number block.

[0095] In the monitoring, by selecting different hash functions, the first sorted number block is hashed, the number block corresponding to the hash value is found, and data comparison and continuous hashing are carried out, so that the real-time data is monitored.

[0096] In embodiment 6, Figure 4 The method for monitoring the power safety hidden danger based on the Internet of Things is shown, and the steps of using the parameter index, building a fault propagation relationship, and generating a plurality of cascading schemes are described as follows:

[0097] S301: Extract the emergency event of the running equipment from the existing data, traverse the parameter index contained in the emergency event, and obtain the target item.

[0098] S302: Causally analyze the target item to obtain a cascading scheme.

[0099] From the existing data, extract the accident about the running equipment, and determine the parameter index of the running equipment before and after the accident as the target item, and perform causal analysis to obtain a cascading scheme, wherein the accident is the emergency event, and the target item is the parameter index fluctuating when the emergency event occurs.

[0100] Continue to describe the example in S400, and perform causal analysis on the generator and the transformer, because the abnormal generator speed causes the generator overload, and causes the transformer temperature to be too high, thereby establishing the cascading scheme of generator speed abnormality→generator overload→transformer temperature too high.

[0101] In embodiment 7, Figure 5 The method for monitoring the power safety hidden danger based on the Internet of Things is shown, and the steps of using the real-time data in the target number block to query the fluctuation range or the evaluation rule are described as follows:

[0102] S401: Build a communication link between the target number block and the Internet of Things equipment, and update the target number block in real time through the communication link.

[0103] Through the communication link, the real-time data collected is used to update the target number block in real time.

[0104] S402: Create a disposal scheme of the emergency event, compare the monitoring result with the emergency event, and activate the disposal scheme.

[0105] Determine the disposal scheme of each emergency event, activate the disposal scheme when the monitoring result is the same as the fluctuation of the target item in the emergency event, thereby avoiding the expansion of the emergency event and blocking the development of the accident.

[0106] Figure 6 A component structure block diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is shown, and the power safety hidden danger real-time monitoring system based on the Internet of Things 1 comprises:

[0107] The computing module 11 is configured to identify a running device in a power system, acquire a device number of the running device, configure a parameter index of the running device, and demarcate a fluctuation range of the parameter index, wherein the parameter index at least comprises a rotating speed, a temperature and a voltage, and an importance value of each parameter index is calculated.

[0108] The linking module 12 is configured to create a number block corresponding to the parameter index, collect real-time data of the parameter index via a preset Internet of Things device, synchronize the real-time data and the device number to the corresponding number block, sort the number block according to the order of the importance value from large to small, and link to obtain a monitoring chain.

[0109] The writing module 13 is configured to establish a corresponding relationship between a sequential hash function and the fluctuation range, sequentially perform chain hash on all the number blocks, and write the obtained hash value into the corresponding number block.

[0110] A fault propagation relationship is built by using the parameter index, a plurality of cascading schemes are generated, an evaluation rule and a relationship hash function corresponding to the cascading scheme are constructed, a mapping between the evaluation rule and the relationship hash function is established, the number block in the cascading scheme is chain hashed by using the relationship hash function, and the obtained hash value is written into the corresponding number block.

[0111] The sending module 14 is configured to construct a trigger time table, wherein the trigger time table is composed of a time item and a hash function item, the hash function item is a sequential hash function or a relationship hash function, a target hash function is determined by querying the trigger time table based on a current time, a number block at the front of the monitoring chain is hashed, a target number block is traversed according to the obtained hash value, a real-time data in the target number block is used to query the fluctuation range or the evaluation rule, a monitoring result is determined, and the monitoring result is sent to a preset terminal.

[0112] Figure 7 A component structure block diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is shown, and the computing module 11 comprises:

[0113] The definition unit 111 is configured to determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item.

[0114] The integration unit 112 is configured to integrate the abnormal item into the monitoring result.

[0115] The determining unit 113 is configured to determine the importance coefficient of each operation device by using the pre-constructed fault analysis strategy.

[0116] The integrating unit 114 is configured to configure the priority coefficient of the parameter index, integrate the importance coefficient and the priority coefficient, and calculate the importance value of each parameter index.

[0117] Figure 8 The component structure block diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is shown, and the linking module 12 comprises:

[0118] The synchronizing unit 121 is configured to embed a root node into the monitoring chain and synchronize the trigger time table into the root node.

[0119] The planning unit 122 is configured to establish a planning relationship between the root node and all the number blocks.

[0120] Figure 9 The component structure block diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is shown, and the writing module 13 comprises:

[0121] The obtaining unit 131 is configured to extract an emergency event of an operation device from existing data, traverse parameter indexes contained in the emergency event, and obtain target items.

[0122] The analyzing unit 132 is configured to perform causal analysis on the target items and obtain a fault propagation relationship.

[0123] Figure 10 The component structure block diagram of the power safety hidden danger real-time monitoring system based on the Internet of Things is shown, and the sending module 14 comprises:

[0124] The updating unit 141 is configured to build a communication link between the target number block and the Internet of Things device, and perform real-time updating on the target number block via the communication link.

[0125] The activating unit 142 is configured to create a disposal scheme of the emergency event, compare the monitoring result with the emergency event, and activate the disposal scheme.

[0126] The calculating module 11 is mainly configured to complete the step S100, the linking module 12 is mainly configured to complete the step S200, the writing module 13 is mainly configured to complete the step S300, and the sending module 14 is mainly configured to complete the step S400.

[0127] The definition unit 111 is mainly configured to complete the step S101, the integration unit 112 is mainly configured to complete the step S102, the determination unit 113 is mainly configured to complete the step S103, and the integration unit 114 is mainly configured to complete the step S104.

[0128] The synchronization unit 121 is mainly configured to complete the step S201, and the overall planning unit 122 is mainly configured to complete the step S202.

[0129] The obtaining unit 131 is mainly configured to complete the step S301, and the analysis unit 132 is mainly configured to complete the step S302.

[0130] The updating unit 141 is mainly configured to complete the step S401, and the activation unit 142 is mainly configured to complete the step S402.

[0131] The technical features of the above-described embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above-described embodiments are not described, however, as long as the combinations of the technical features do not exist in contradiction, they should be considered as the scope of the present disclosure.

[0132] The above-described embodiments only express several embodiments of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be pointed out that, for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

[0133] The above-described embodiments are only the preferred embodiments of the present application, and are not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A power safety hazard real-time monitoring method based on the Internet of Things, characterized in that, The method comprises: Identifying operating equipment in a power system, obtaining equipment numbers of the operating equipment, configuring parameter indicators of the operating equipment, and delimiting fluctuation ranges of the parameter indicators, wherein the parameter indicators at least include: rotating speed, temperature, and voltage, and calculating importance values of each parameter indicator; Creating a number block corresponding to each parameter indicator, collecting real-time data of the parameter indicators via a preset Internet of Things device, synchronizing the real-time data and equipment numbers to the corresponding number block, sorting the number blocks in descending order of the importance values, and linking to obtain a monitoring chain; Establishing a correspondence between a sequential hash function and the fluctuation range, sequentially performing chain hashing on all number blocks, and writing the obtained hash values into the corresponding number blocks; Using the parameter indicators to build a fault propagation relationship, generating a plurality of cascading schemes, constructing evaluation rules and relationship hash functions corresponding to the cascading schemes, establishing a mapping between the evaluation rules and the relationship hash functions, using the relationship hash functions to perform chain hashing on the number blocks in the cascading schemes, and writing the obtained hash values into the corresponding number blocks; Constructing a trigger time table, wherein the trigger time table is composed of time items and hash function items, the hash function items are sequential hash functions or relationship hash functions, querying the trigger time table based on the current time to determine a target hash function, hashing the number block at the front of the monitoring chain, and traversing a target number block according to the obtained hash value, using the real-time data in the target number block to query the fluctuation range or the evaluation rule, determining a monitoring result, and sending the monitoring result to a preset terminal. 2.The IoT-based real-time monitoring method for electrical safety hazards according to claim 1, wherein, The step of identifying operating equipment in a power system, obtaining equipment numbers of the operating equipment, configuring parameter indicators of the operating equipment, and delimiting fluctuation ranges of the parameter indicators comprises: Determining whether the parameter indicators exceed the fluctuation range, and defining the corresponding parameter indicators as abnormal items if they exceed the fluctuation range; Integrating the abnormal items into the monitoring result. 3.The IoT-based real-time monitoring method for electrical safety hazards according to claim 2, wherein, The step of calculating the importance values of each parameter indicator comprises: Determining the importance coefficients of each operating equipment using a pre-constructed fault analysis strategy; Configuring priority coefficients of the parameter indicators, integrating the importance coefficients and the priority coefficients, and calculating the importance values of each parameter indicator. 4.The IoT-based real-time monitoring method for electrical safety hazards according to claim 1, wherein, The step of sorting the number blocks in descending order of the importance values and linking to obtain a monitoring chain comprises: Embedding a root node into the monitoring chain and synchronizing the trigger time table into the root node; Establishing a unified relationship between the root node and all number blocks. 5.The IoT-based real-time monitoring of electrical safety hazards method according to claim 1, wherein, The step of using the relationship hash functions to perform chain hashing on the number blocks in the cascading schemes comprises: Defining the number block at the front of the monitoring chain as a head block, and sequentially defining a second sequential block, a third sequential block,..., and an n-th sequential block; Using the sequential hash function to hash the equipment numbers in the head block to obtain a first hash value, and writing the first hash value into the second sequential block; Integrate the first hash value in the second sequential block and the device number, hash by using the sequential hash function to obtain a second hash value, write the second hash value into a third sequential block, and so on. 6.The IoT-based real-time monitoring of electrical safety hazards method according to claim 1, wherein, The step of building a fault propagation relationship by using the parameter indicators and generating a plurality of cascading schemes comprises: Extracting an emergency event of a running device from existing data, traversing parameter indicators contained in the emergency event to obtain target items; Performing causal analysis on the target items to obtain cascading schemes. 7.The IoT-based real-time monitoring method for electrical safety hazards according to claim 6, wherein, The step of querying the fluctuation range or evaluation rule by using real-time data in the target block comprises: Building a communication link between the target block and an Internet of Things device, and updating the target block in real time via the communication link; Creating a disposal scheme for the emergency event, comparing the monitoring result with the emergency event, and activating the disposal scheme.

8. An Internet of Things-based real-time monitoring system for power safety hazards, characterized in that, The system comprises: A computing module configured to identify a running device in a power system, obtain a device number of the running device, configure parameter indicators of the running device, and define a fluctuation range of the parameter indicators, wherein the parameter indicators at least include a rotating speed, a temperature, and a voltage, and an importance value of each parameter indicator is calculated; A linking module configured to create a block corresponding to each parameter indicator, collect real-time data of the parameter indicators via a preset Internet of Things device, synchronize the real-time data and the device number to the corresponding block, sort the blocks according to the importance values from large to small, and link to obtain a monitoring chain; A writing module configured to establish a correspondence between a sequential hash function and a fluctuation range, sequentially perform chain hashing on all blocks, and write the obtained hash values into the corresponding blocks; Build a fault propagation relationship by using the parameter indicators, generate a plurality of cascading schemes, construct an evaluation rule and a relationship hash function corresponding to each cascading scheme, establish a mapping between the evaluation rule and the relationship hash function, perform chain hashing on the blocks in the cascading schemes by using the relationship hash function, and write the obtained hash values into the corresponding blocks; A sending module configured to construct a trigger time table, wherein the trigger time table is composed of a time item and a hash function item, the hash function item is a sequential hash function or a relationship hash function, query the trigger time table based on a current time, determine a target hash function, hash the block sorted first in the monitoring chain, traverse a target block according to the obtained hash value, query the fluctuation range or the evaluation rule by using real-time data in the target block, determine a monitoring result, and send the monitoring result to a preset terminal. 9.The IoT-based real-time monitoring system for electrical safety hazards according to claim 8, wherein, The computing module comprises: A defining unit configured to determine whether the parameter indicators exceed the fluctuation range, and define corresponding parameter indicators as abnormal items if they exceed the fluctuation range; An integrating unit configured to integrate the abnormal items into a monitoring result; A determining unit configured to determine an importance coefficient of each running device by using a pre-constructed fault analysis strategy. An integration unit is configured to configure priority coefficients of the parameter indexes, integrate the importance coefficients and the priority coefficients, and calculate importance values of each parameter index. 10.The IoT-based real-time monitoring system for electrical safety hazards according to claim 8, wherein, The linking module comprises: A synchronization unit is configured to embed a root node into the monitoring chain and synchronize the trigger time table into the root node; A coordination unit is configured to establish a coordination relationship between the root node and all the number blocks.

Images