Electric power potential safety hazard real-time monitoring method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of potential safety hazard monitoring, and particularly relates to a real-time monitoring method and system for electric power potential safety hazards based on the Internet of Things, and the method comprises the steps: recognizing operation equipment in an electric power system, obtaining the equipment number of the operation equipment, configuring the parameter index of the operation equipment, and obtaining the parameter index of the operation equipment; the fluctuation range of the parameter indexes is delimited, the parameter indexes at least comprise the rotating speed, the temperature and the voltage, and the important value of each parameter index is calculated; and creating a plurality of blocks in one-to-one correspondence with the parameter indexes through preset Internet of Things equipment. According to the invention, by selecting the relation hash function, the fault can be used as drive to perform point inspection on the operation equipment, so that the early-stage symptom of the fault can be identified in time, chain reaction is prevented, the stability of the power system is greatly improved, general inspection and point inspection can be combined by constructing the monitoring chain, the safety risk of the power system is greatly reduced, resource configuration is optimized, and the reliability of the power system is improved. And the monitoring efficiency of the power system is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of potential safety hazard monitoring, and in particular to a method and system for real-time monitoring of potential safety hazard of electric power based on the Internet of Things. Background Art

[0002] In the power system, the DCS system is usually used to collect and analyze the operating status and parameter indicators (such as current, voltage, temperature and vibration) of power equipment in real time. In combination with the Internet of Things technology, the field data is obtained in real time through the sensor network, thereby realizing remote monitoring of key equipment, avoiding parameter indicator deviations, and achieving timely handling of safety hazards.

[0003] During the data processing process, the DCS system and IoT devices compare the collected real-time data with the preset reference standards. Once a deviation from the standard is detected, the DCS system will automatically trigger an alarm and record the abnormal information.

[0004] However, in the above process, only the real-time data is compared with the reference standard, and the correlation between the equipment is ignored. In actual production, the real-time data may not deviate from the reference standard, but there are early signs of a fault. Due to the inability to identify it in time, potential faults are likely to accumulate and deteriorate, affecting the stability of the system. Therefore, "how to use faults as a driving force to monitor the safety hazards of running equipment" is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring method and system for power safety hazards based on the Internet of Things, so as to solve the problem of "how to monitor safety hazards of running equipment driven by faults" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for real-time monitoring of power safety hazards based on the Internet of Things, the method comprising: Identify the running equipment in the power system, obtain the equipment number of the running equipment, configure the parameter index of the running equipment, define the fluctuation range of the parameter index, wherein the parameter index at least includes: speed, temperature and voltage, and calculate the important value of each parameter index; Create digital blocks corresponding to the parameter indicators one by one, collect real-time data of the parameter indicators through preset Internet of Things devices, synchronize the real-time data and device numbers to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain; Establish the corresponding relationship between the sequential hash function and the fluctuation range, perform chain hashing on all blocks in turn, and write the obtained hash values ​​into the corresponding blocks; Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and the relational hash functions are used to chain hash the blocks in the chain schemes, and the obtained hash values ​​are written into the corresponding blocks; Construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, wherein the hash function item is a sequential hash function or a relational hash function, and based on the current time, query the trigger schedule, determine the target hash function, hash the first number block in the monitoring chain, and traverse the target number block based on the obtained hash value, query the fluctuation range or evaluation rule using the real-time data in the target number block, determine the monitoring result, and send the monitoring result to a preset terminal.

[0007] Furthermore, the steps of identifying the operating equipment in the power system, obtaining the equipment number of the operating equipment, configuring the parameter index of the operating equipment, and defining the fluctuation range of the parameter index include: Determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item; The abnormal items are integrated into the monitoring results.

[0008] Furthermore, the step of calculating the important value of each parameter index includes: Determining the importance factor of each of the operating equipment using a pre-built fault analysis strategy; The priority coefficients of the parameter indicators are configured, the importance coefficients and the priority coefficients are integrated, and the importance value of each parameter indicator is calculated.

[0009] Furthermore, the step of sorting the blocks in descending order of the importance values ​​and linking them to obtain a monitoring chain includes: Embed a root node into the monitoring chain and synchronize the trigger schedule to the root node; A coordinated relationship between the root node and all blocks is established.

[0010] Furthermore, the step of performing chain hashing on the blocks in the chain scheme using the relational hash function includes: The first number of blocks in the monitoring chain are defined as the head block, and the second sequence block, the third sequence block, ..., the nth sequence block are defined in sequence; Using the sequential hash function, hashing the device number in the header block to obtain a first hash value, and writing the first hash value into the second sequential block; The first hash value and the device number in the second sequential block are integrated and hashed using the sequential hash function to obtain a second hash value, and the second hash value is written into the third sequential block, and so on.

[0011] Furthermore, the step of using the parameter indicators to build a fault propagation relationship and generate several chain schemes includes: Extract emergency events of running equipment from existing data, traverse parameter indicators included in the emergency events, and obtain target items; A cause-effect analysis is performed on the target item to obtain a chain solution.

[0012] Furthermore, the step of using the real-time data in the target data block to query the fluctuation range or evaluation rule includes: Establishing a communication link between the target digital block and the IoT device, and updating the target digital block in real time via the communication link; A disposal plan for the emergency event is created, the monitoring result is compared with the emergency event, and the disposal plan is activated.

[0013] Furthermore, the system comprises: A calculation module is used to identify the running equipment in the power system, obtain the equipment number of the running equipment, configure the parameter index of the running equipment, define the fluctuation range of the parameter index, wherein the parameter index at least includes: speed, temperature and voltage, and calculate the important value of each parameter index; A linking module is used to create digital blocks corresponding to the parameter indicators one by one, collect real-time data of the parameter indicators through a preset IoT device, synchronize the real-time data and the device number to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain; The writing module is used to establish the corresponding relationship between the sequential hash function and the fluctuation range, perform chain hashing on all the blocks in sequence, and write the obtained hash values ​​into the corresponding blocks; Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and the relational hash functions are used to chain hash the blocks in the chain schemes, and the obtained hash values ​​are written into the corresponding blocks; A sending module is used to construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, wherein the hash function item is a sequential hash function or a relational hash function, and based on the current time, the trigger schedule is queried to determine a target hash function, the first number block in the monitoring chain is hashed, and based on the obtained hash value, the target number block is traversed, and the real-time data in the target number block is used to query the fluctuation range or evaluation rule, determine the monitoring result, and send the monitoring result to a preset terminal.

[0014] Furthermore, the calculation module includes: A definition unit, used to determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item; An integration unit, used for integrating the abnormal item into the monitoring result; A determination unit, configured to determine the importance coefficient of each of the operating devices using a pre-built fault analysis strategy; The integration unit is used 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.

[0015] Furthermore, the link module includes: A synchronization unit, used for embedding a root node into the monitoring chain and synchronizing the trigger schedule to the root node; The coordination unit is used to establish a coordination relationship between the root node and all the blocks.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By determining the fluctuation range, equipment abnormalities can be monitored in a timely manner. By calculating important values, key indicators can be monitored in a focused manner, and the allocation of monitoring resources can be optimized, greatly improving the monitoring efficiency. By selecting a sequential hash function, all real-time data can be checked in order of importance to avoid real-time data being off-label and quickly identify data fluctuations that deviate from the normal range. By selecting a relational hash function, running equipment can be inspected based on faults, thereby identifying early signs of faults in a timely manner, preventing chain reactions, and greatly improving the stability of the power system. By building a monitoring chain, general inspections and spot inspections can be combined, greatly reducing the safety risks of the power system, optimizing resource allocation, and further improving the monitoring efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for real-time monitoring of power safety hazards based on the Internet of Things provided in an embodiment of the present invention; Figure 2 A first sub-flow chart of a method for real-time monitoring of electric power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Figure 3 A second sub-flow chart of the method for real-time monitoring of power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Figure 4 A third sub-flow chart of the method for real-time monitoring of power safety hazards based on the Internet of Things provided in an embodiment of the present invention; Figure 5 A fourth sub-flow chart of the method for real-time monitoring of power safety hazards based on the Internet of Things provided in an embodiment of the present invention; Figure 6 A block diagram of the composition of a real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of a computing module in a real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of the link module in the real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Fig. 9 A block diagram of the composition of a writing module in a real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention; Fig.10 A block diagram of the composition of a sending module in a real-time monitoring system for power safety hazards based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In Example 1, Figure 1 The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown and described in detail below: S100: Identify the operating equipment in the power system, obtain the equipment number of the operating equipment, configure the parameter indicators of the operating equipment, define the fluctuation range of the parameter indicators, wherein the parameter indicators at least include: rotation speed, temperature and voltage, and calculate the important value of each of the parameter indicators.

[0020] In the power system, all operating equipment are identified, including power generation equipment, substation equipment, and transmission equipment, etc. The equipment number of each operating equipment is determined, and parameter indicators of each operating equipment are configured according to the specific functions and operating requirements of the operating equipment, wherein the parameter indicators include but are not limited to rotation speed, temperature, and voltage, etc.; the fluctuation range of each parameter indicator is determined according to the historical data, technical specifications, and actual operating environment of the operating equipment, etc.; and the importance value of each parameter indicator is calculated, wherein the larger the importance value of the parameter indicator is, the greater the impact on the power system when the parameter indicator fluctuates abnormally.

[0021] S200: Create digital blocks corresponding to the parameter indicators one by one, collect real-time data of the parameter indicators through preset Internet of Things devices, and synchronize the real-time data and device numbers to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain.

[0022] A data block is created for each parameter indicator, where the data block is a data storage and processing unit; real-time data of each parameter indicator is collected in real time through preset IoT devices, and these real-time data are synchronously entered into the corresponding data block with the corresponding device number, where IoT devices include: sensors, data collectors, edge computing devices, and control and execution devices, etc.

[0023] All blocks are sorted in descending order of importance and integrated to generate a monitoring chain. When conducting real-time monitoring, priority is given to ensuring the monitoring effect of blocks with larger importance values. For example, when there is a bottleneck in monitoring resources, the real-time data and fluctuation ranges in the blocks are compared in descending order of importance values ​​in the monitoring chain. When the real-time data of a block is found to exceed the fluctuation range, the corresponding block is identified as an abnormal block. In other words, the parameter indicator corresponding to the abnormal block is out of standard.

[0024] S300: Establish a corresponding relationship between a sequential hash function and a fluctuation range, perform chain hashing on all digital blocks in sequence, and write the obtained hash values ​​into corresponding digital blocks.

[0025] A sequential hash function is selected and determined as a trigger head of the fluctuation range. When the sequential hash function is called, the fluctuation range will also be extracted at the same time. The sequential hash function is used to chain hash the device numbers in all blocks, and the obtained hash values ​​are filled into the corresponding blocks.

[0026] Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and using the relational hash functions, chain hashing is performed on the blocks in the chain scheme, and the obtained hash values ​​are written into the corresponding blocks.

[0027] According to the possible propagation path and impact range of equipment failure, the relationship between parameter indicators is determined to obtain the fault propagation relationship. For each fault propagation relationship, one or more chain schemes are generated, and an evaluation rule and a relational hash function are determined for each chain scheme. Using this relational hash function, chain hash the blocks in the chain scheme, and write the obtained hash value into the corresponding block.

[0028] When conducting a census, use a sequential hash function to hash all the blocks in turn, and compare the real-time data with the fluctuation range; when it is necessary to conduct spot checks on different chain schemes, how to determine which parameter indicators need to be monitored, at this time, according to the chain scheme, determine the corresponding relational hash function, hash the device number of the first block in the monitoring chain, get the hash value, find the block containing the hash value, and compare the real-time data of the found block with the evaluation rule; continue to find the next block and hash it, and so on; thereby completing the spot check of the blocks and obtaining the monitoring results.

[0029] For example, the four parameter indicators of generator speed, generator power, transformer temperature and circuit breaker status are used to build multiple fault propagation relationships, such as: generator → transformer, generator → transformer → circuit breaker; multiple chain schemes are generated, such as abnormal generator speed → generator overload → transformer temperature is too high; when monitoring the above chain schemes, it is necessary to specifically analyze the blocks corresponding to the generator speed, generator power and transformer temperature.

[0030] Each chain scheme corresponds to an evaluation rule, where the evaluation rule is generated according to the emergency event, and 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 maintained for more than 15 seconds; by combining real-time data such as generator power and transformer temperature, early signs of faults can be discovered in time.

[0031] S400: Construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, wherein the hash function item is a sequential hash function or a relational hash function, and based on the current time, query the trigger schedule, determine the target hash function, hash the first number block in the monitoring chain, and traverse the target number block based on the obtained hash value, query the fluctuation range or evaluation rule using the real-time data in the target number block, determine the monitoring result, and send the monitoring result to a preset terminal.

[0032] Construct a trigger time table, which consists of multiple time items and hash function items. Each time item represents a specific time point. The hash function item includes a sequential hash function or a relational hash function, wherein the time items corresponding to the sequential hash function have smaller intervals.

[0033] Based on the current moment, the trigger schedule is queried to identify the target hash function corresponding to the current moment. If the hash function item corresponding to the current moment is a sequential hash function, all blocks in the monitoring chain will be processed in order of importance from large to small. The specific monitoring method is: compare the real-time data in each block with the size of the fluctuation range; if the hash function item corresponding to the current moment is a relational hash function, then according to the chain scheme, only the corresponding blocks are compared according to the evaluation rules; 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 the preset terminal, where the preset terminal can be the terminal of the power system maintenance personnel.

[0034] In this embodiment, the monitoring process in S400 is further expanded; for example, multiple parameter indicators are traversed in the running device, and multiple corresponding number blocks, A, B, C, D,... are established; A is hashed using a sequential hash function and multiple relational hash functions to obtain different hash values, which are recorded as, and, etc., and the obtained hash values ​​are written into the corresponding number blocks according to the chain scheme; when monitoring is required, only the corresponding hash function needs to be called, and A is hashed, and the number block where the hash value is located is determined as the target number block, and the real-time data in the target number block is analyzed and processed using the evaluation rules, and the target number block is continued to be hashed, and so on; in the above manner, by selecting different hash functions, multiple monitoring channels can be determined, and the number blocks in each monitoring channel are compared and analyzed through the evaluation rules and the fluctuation range, so as to determine the abnormal parameter indicators and fault signs therein.

[0035] In Example 2, Figure 2The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of identifying the operating equipment in the power system, obtaining the equipment number of the operating equipment, configuring the parameter index of the operating equipment, and defining the fluctuation range of the parameter index are described in detail as follows: S101: Determine whether the parameter index exceeds the fluctuation range. If so, define the corresponding parameter index as an abnormal item.

[0036] If the parameter index exceeds the fluctuation range, the corresponding parameter index is defined as an abnormal item; all abnormal items are integrated to generate additional chain schemes, and the corresponding evaluation rules and relational hash functions are configured.

[0037] S102: Integrate the abnormal item into the monitoring result.

[0038] In Example 3, Figure 2 The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of calculating the important values ​​of each parameter index are described in detail below: S103: Determine the importance coefficient of each of the operating devices using a pre-built fault analysis strategy.

[0039] According to the fault analysis strategy, the importance coefficient of each operating equipment is determined, where the fault analysis strategy is the specific analysis method of the fault; when an operating equipment fails, the cause of the failure is identified and the importance coefficient of each operating equipment is determined. If the failure of a certain power equipment may cause a chain reaction or serious safety problems, its importance coefficient will be larger.

[0040] S104: configuring the priority coefficient of the parameter indicator, integrating the importance coefficient and the priority coefficient, and calculating the importance value of each parameter indicator.

[0041] In the running equipment, the priority coefficient of each parameter index is determined. The larger the priority coefficient is, the greater the influence of the parameter index on the stability of the running equipment is.

[0042] In Example 4, Figure 3 The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of sorting the blocks in descending order of the importance values ​​and linking them to obtain a monitoring chain are described in detail as follows: S201: embed a root node into the monitoring chain, and synchronize the trigger schedule to the root node.

[0043] The root node is embedded in the monitoring chain, and the root node and the current time are used to query the trigger schedule and determine the corresponding time item.

[0044] S202: Establishing a coordination relationship between the root node and all digital blocks.

[0045] The root node is used to manage all blocks in a coordinated manner to avoid confusion in the order of blocks.

[0046] In Embodiment 5, the step of performing chain hashing on the blocks in the chain scheme using the relational hash function includes: The first number of blocks in the monitoring chain are defined as the head block, and the second sequence block, the third sequence block, ..., the nth sequence block are defined in sequence; Using the sequential hash function, hashing the device number in the header block to obtain a first hash value, and writing the first hash value into the second sequential block; The first hash value and the device number in the second sequential block are integrated and hashed using the sequential hash function to obtain a second hash value, and the second hash value is written into the third sequential block, and so on.

[0047] Sequential hashing is chain hashing of all blocks, and relational hashing is chain hashing only of blocks corresponding to the chain scheme; in other words, each block will be hashed at least once, and there is no upper limit to the number of hashes. After hashing, the hash value will be stored in the corresponding block.

[0048] When monitoring, by selecting different hash functions, hashing the first number blocks in the sorting, finding the number blocks corresponding to the hash values, and performing data comparison and continuing hashing, real-time data monitoring is achieved.

[0049] In Example 6, Figure 4 The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of using the parameter indicators, building a fault propagation relationship, and generating several chain schemes are described in detail as follows: S301: extract emergency events of operating equipment from existing data, traverse parameter indicators included in the emergency events, and obtain target items.

[0050] S302: Perform causal analysis on the target item to obtain a chain solution.

[0051] Accidents related to operating equipment are extracted from existing data, and the parameter indicators of the operating equipment before and after the accident are determined as target items. Cause-effect analysis is performed to obtain a chain reaction plan, in which the accident is an emergency event, and the target item is the parameter indicator that fluctuates when the emergency event occurs.

[0052] The example in S400 is further described in detail, and a cause-effect analysis is performed on the generator and the transformer. Because the abnormal generator speed leads to the generator overload, which causes the transformer temperature to be too high, a chain scheme of abnormal generator speed → generator overload → transformer temperature to be too high is established.

[0053] In Example 7, Figure 5 The implementation process of the real-time monitoring method for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The steps of using the real-time data in the target data block to query the fluctuation range or evaluation rule are described in detail as follows: S401: Establish a communication link between the target digital block and the IoT device, and update the target digital block in real time via the communication link.

[0054] The target data block is updated in real time using the collected real-time data via the communication link.

[0055] S402: Create a disposal plan for the emergency event, compare the monitoring result with the emergency event, and activate the disposal plan.

[0056] Determine the disposal plan for each emergency event. When the monitoring results are the same as the fluctuation of the target item in the emergency event, activate the disposal plan to avoid the expansion of the emergency event and block the development of the accident.

[0057] Figure 6 The present invention shows a block diagram of the structure of a real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention. The real-time monitoring system for power safety hazards based on the Internet of Things 1 includes: The calculation module 11 is used to identify the running equipment in the power system, obtain the equipment number of the running equipment, configure the parameter index of the running equipment, define the fluctuation range of the parameter index, wherein the parameter index at least includes: speed, temperature and voltage, and calculate the important value of each parameter index; The linking module 12 is used to create digital blocks corresponding to the parameter indicators one by one, collect the real-time data of the parameter indicators through the preset Internet of Things devices, and synchronize the real-time data and the device number to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain; The writing module 13 is used to establish a corresponding relationship between the sequential hash function and the fluctuation range, perform chain hashing on all the blocks in sequence, and write the obtained hash values ​​into the corresponding blocks; Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and the relational hash functions are used to chain hash the blocks in the chain schemes, and the obtained hash values ​​are written into the corresponding blocks; The sending module 14 is used to construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, and the hash function item is a sequential hash function or a relational hash function. Based on the current time, the trigger schedule is queried to determine the target hash function, the first number block in the monitoring chain is hashed, and based on the obtained hash value, the target number block is traversed, and the real-time data in the target number block is used to query the fluctuation range or evaluation rule, determine the monitoring result, and send the monitoring result to the preset terminal.

[0058] Figure 7 The structure diagram of the composition of the real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The calculation module 11 includes: A definition unit 111 is used to determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item; An integration unit 112, used to integrate the abnormal item into the monitoring result; A determination unit 113, configured to determine the importance coefficient of each of the operating devices using a pre-built fault analysis strategy; The integration unit 114 is used 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.

[0059] Figure 8 The structure diagram of the composition of the real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The link module 12 includes: A synchronization unit 121, configured to embed a root node into the monitoring chain and synchronize the trigger schedule to the root node; The coordination unit 122 is used to establish a coordination relationship between the root node and all the blocks.

[0060] Fig. 9 The structure diagram of the composition of the real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The writing module 13 includes: The obtaining unit 131 is used to extract the emergency events of the running equipment from the existing data, traverse the parameter indicators included in the emergency events, and obtain the target items; The analyzing unit 132 is used to perform causal analysis on the target item to obtain a fault propagation relationship.

[0061] Fig.10 The structure diagram of the composition of the real-time monitoring system for power safety hazards based on the Internet of Things provided by an embodiment of the present invention is shown. The sending module 14 includes: An updating unit 141 is used to establish a communication link between the target digital block and the IoT device, and to update the target digital block in real time via the communication link; The activation unit 142 is used to create a treatment plan for the emergency event, compare the monitoring result with the emergency event, and activate the treatment plan.

[0062] The calculation module 11 is mainly used to complete step S100, the link module 12 is mainly used to complete step S200, the writing module 13 is mainly used to complete step S300, and the sending module 14 is mainly used to complete step S400; The definition unit 111 is mainly used to complete step S101, the integration unit 112 is mainly used to complete step S102, the determination unit 113 is mainly used to complete step S103, and the integration unit 114 is mainly used to complete step S104; The synchronization unit 121 is mainly used to complete step S201, and the coordination unit 122 is mainly used to complete step S202; The obtaining unit 131 is mainly used to complete step S301, and the analyzing unit 132 is mainly used to complete step S302; The updating unit 141 is mainly used to complete step S401, and the activating unit 142 is mainly used to complete step S402.

[0063] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time monitoring method for power safety hazards based on the Internet of Things, characterized in that: The method comprises: Identify the running equipment in the power system, obtain the equipment number of the running equipment, configure the parameter index of the running equipment, define the fluctuation range of the parameter index, wherein the parameter index at least includes: speed, temperature and voltage, and calculate the important value of each parameter index; Create digital blocks corresponding to the parameter indicators one by one, collect real-time data of the parameter indicators through preset Internet of Things devices, synchronize the real-time data and device numbers to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain; Establish the corresponding relationship between the sequential hash function and the fluctuation range, perform chain hashing on all blocks in turn, and write the obtained hash values ​​into the corresponding blocks; Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and the relational hash functions are used to chain hash the blocks in the chain schemes, and the obtained hash values ​​are written into the corresponding blocks; Construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, wherein the hash function item is a sequential hash function or a relational hash function, and based on the current time, query the trigger schedule, determine the target hash function, hash the first number block in the monitoring chain, and traverse the target number block based on the obtained hash value, query the fluctuation range or evaluation rule using the real-time data in the target number block, determine the monitoring result, and send the monitoring result to a preset terminal.

2. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 1 is characterized in that: The steps of identifying the operating equipment in the power system, obtaining the equipment number of the operating equipment, configuring the parameter index of the operating equipment, and defining the fluctuation range of the parameter index include: Determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item; The abnormal items are integrated into the monitoring results.

3. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 2 is characterized in that: The step of calculating the important value of each parameter index comprises: Determining the importance factor of each of the operating equipment using a pre-built fault analysis strategy; The priority coefficients of the parameter indicators are configured, the importance coefficients and the priority coefficients are integrated, and the importance value of each parameter indicator is calculated.

4. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 1 is characterized in that: The step of sorting the blocks in descending order of the importance values ​​and linking them to obtain a monitoring chain comprises: Embed a root node into the monitoring chain and synchronize the trigger schedule to the root node; A coordinated relationship between the root node and all blocks is established.

5. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 1 is characterized in that: The step of performing chain hashing on the blocks in the chain scheme using the relational hash function comprises: The first blocks in the monitoring chain are defined as the head blocks, and the second sequence block, the third sequence block, ..., the nth sequence block are defined in sequence; Using the sequential hash function, hashing the device number in the header block to obtain a first hash value, and writing the first hash value into the second sequential block; The first hash value and the device number in the second sequential block are integrated and hashed using the sequential hash function to obtain a second hash value, and the second hash value is written into the third sequential block, and so on.

6. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 1 is characterized in that: The step of using the parameter indicators to build a fault propagation relationship and generate a plurality of chain schemes includes: Extract emergency events of running equipment from existing data, traverse parameter indicators included in the emergency events, and obtain target items; A cause-effect analysis is performed on the target item to obtain a chain solution.

7. The method for real-time monitoring of power safety hazards based on the Internet of Things according to claim 6 is characterized in that: The step of using the real-time data in the target data block to query the fluctuation range or evaluation rule includes: Establishing a communication link between the target digital block and the IoT device, and updating the target digital block in real time via the communication link; A disposal plan for the emergency event is created, the monitoring result is compared with the emergency event, and the disposal plan is activated.

8. A real-time monitoring system for power safety hazards based on the Internet of Things, characterized in that: The system comprises: A calculation module is used to identify the running equipment in the power system, obtain the equipment number of the running equipment, configure the parameter index of the running equipment, define the fluctuation range of the parameter index, wherein the parameter index at least includes: speed, temperature and voltage, and calculate the important value of each parameter index; A linking module is used to create digital blocks corresponding to the parameter indicators one by one, collect real-time data of the parameter indicators through a preset IoT device, synchronize the real-time data and the device number to the corresponding digital blocks, sort the digital blocks in descending order of the important values, and link them to obtain a monitoring chain; The writing module is used to establish the corresponding relationship between the sequential hash function and the fluctuation range, perform chain hashing on all the blocks in sequence, and write the obtained hash values ​​into the corresponding blocks; Using the parameter indicators, a fault propagation relationship is established to generate several chain schemes, evaluation rules and relational hash functions corresponding to the chain schemes are constructed, a mapping between the evaluation rules and the relational hash functions is established, and the relational hash functions are used to chain hash the blocks in the chain schemes, and the obtained hash values ​​are written into the corresponding blocks; A sending module is used to construct a trigger schedule, wherein the trigger schedule consists of a time item and a hash function item, wherein the hash function item is a sequential hash function or a relational hash function, and based on the current time, the trigger schedule is queried to determine a target hash function, the first number block in the monitoring chain is hashed, and based on the obtained hash value, the target number block is traversed, and the real-time data in the target number block is used to query the fluctuation range or evaluation rule, determine the monitoring result, and send the monitoring result to a preset terminal.

9. The real-time monitoring system for power safety hazards based on the Internet of Things according to claim 8 is characterized in that: The calculation module comprises: A definition unit, used to determine whether the parameter index exceeds the fluctuation range, and if so, define the corresponding parameter index as an abnormal item; An integration unit, used for integrating the abnormal item into the monitoring result; A determination unit, configured to determine the importance coefficient of each of the operating devices using a pre-built fault analysis strategy; The integration unit is used 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.

10. The real-time monitoring system for power safety hazards based on the Internet of Things according to claim 8 is characterized in that: The link module includes: A synchronization unit, used for embedding a root node into the monitoring chain and synchronizing the trigger schedule to the root node; The coordination unit is used to establish a coordination relationship between the root node and all the blocks.

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