Monitoring method for electromechanical equipment in highway tunnel

Abstract

The invention provides a highway tunnel electromechanical equipment monitoring method, which relates to the field of digital monitoring, and comprises the following steps: acquiring node local data based on a node local sensor and node local equipment, the node local data comprising node position information; preprocessing local data of the nodes, including signal filtering and feature extraction, to obtain preprocessed data; based on the node position information, performing spatial fusion on the preprocessed data to obtain fused node data; and generating a local node fusion data packet based on the fused node data, receiving node fusion data packets of other nodes, fusing the local node fusion data packet and the node fusion data packets of other nodes, eliminating repeated data, generating a new local node fusion data packet, and forwarding the new local node fusion data packet. According to the invention, the problem of monitoring difficulty caused by unstable signal transmission in the tunnel is solved, the maintenance and rescue difficulty is reduced, and the safety of the expressway tunnel is improved.

Description

Technical Field

[0001] The invention relates to the field of digital monitoring, and in particular to a method for monitoring electromechanical equipment in a highway tunnel. Background Art

[0002] The monitoring of electromechanical equipment in the tunnel (for example, ventilation and environmental monitoring equipment, lighting equipment, fire-fighting equipment, and drainage systems) requires wired equipment. When encountering geological disasters, the signal will be interrupted. If wireless equipment is used, the signal in the tunnel is not good, which will cause signal transmission obstruction or poor signal. Even if the signal is transmitted through a wireless communication repeater, when a geological disaster occurs, if the wireless communication repeater is damaged, the signal in the tunnel will be lost, and the monitoring of the electromechanical equipment will be lost, increasing the difficulty of maintenance or rescue. Summary of the invention

[0003] The present invention provides a method for monitoring electromechanical equipment in a highway tunnel, comprising the following steps: Based on the node local sensor and the node local device, obtaining the node local data, wherein the node local data includes the node location information; Preprocessing the local data of the node, including signal filtering and feature extraction, to obtain preprocessed data; Based on the node position information, spatially fuse the preprocessed data to obtain fused node data; A local node fusion data packet is generated based on the fused node data, node fusion data packets of other nodes are received, the local node fusion data packet is merged with the node fusion data packets of other nodes, a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded.

[0004] In some embodiments, the spatial fusion comprises the following steps: Based on the node location information, the node data of the same type of devices in the same space are fused to obtain node device fusion data, which includes temperature data, gas concentration data, smoke concentration data, water level data and device status data.

[0005] In some embodiments, the temperature data and the gas concentration data are obtained based on the following algorithm: The present invention provides a method for monitoring electromechanical equipment in a highway tunnel, comprising the following steps: Based on the node local sensor and the node local device, obtaining the node local data, wherein the node local data includes the node location information; Preprocessing the local data of the node, including signal filtering and feature extraction, to obtain preprocessed data; Based on the node position information, spatially fuse the preprocessed data to obtain fused node data; A local node fusion data packet is generated based on the fused node data, node fusion data packets of other nodes are received, the local node fusion data packet is merged with the node fusion data packets of other nodes, a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded.

[0006] In some embodiments, the spatial fusion comprises the following steps: Based on the node location information, the node data of the same type of devices in the same space are fused to obtain node device fusion data, which includes temperature data, gas concentration data, smoke concentration data, water level data and device status data.

[0007] In some embodiments, the temperature data and the gas concentration data are obtained based on the following algorithm: ; in, represents the reading of the i-th sensor; n represents the total number of sensors; Represents the temperature data after fusion; Indicates the gas concentration data after fusion; The smoke concentration data and water level data are obtained based on the following algorithm: , in, represents the reading of the i-th smoke sensor, Indicates the minimum smoke density data after fusion; Indicates the minimum water level data after fusion; The device status data is obtained based on the following method: counting the status of each device in the same space, and then determining the fusion status based on the counting result; The counting algorithm is: , s∈{normal, fault}; in, Indicates the number of devices with status s in the same space; k indicates the kth device; represents the actual state of the kth device, which can be "normal" or "faulty"; s represents the target state, which can be "normal" or "faulty"; n represents the total number of devices in the same space, which is used to traverse all devices; is an indicative function used to determine whether the device state is equal to s; Among them, the indicative function is defined as: ; The algorithm for determining the fusion state is: , in, Indicates the device status of the same type of devices in the same space after fusion; Indicates the use The largest state s, find the state with the most occurrences.

[0008] In some embodiments, the spatial fusion further comprises the following steps: Based on the node location information, the node device fusion data is fused to obtain fused node data, and the fused node data includes a spatial state.

[0009] In some embodiments, based on the node location information, the node device fusion data is fused to obtain fused node data, and the fused node data includes a spatial state, including: Based on the node location information, the node device fusion data is standardized, and the data is mapped to the [0,1] interval to obtain standardized indicator data; The standardized indicator data are weighted and summed according to the weight corresponding to each indicator data to obtain a comprehensive score of the space where the node is located; According to the value of the comprehensive score, the space state is divided into different levels to obtain the space state.

[0010] In some embodiments, the acquiring of local data includes: in response to detecting that a UPS power supply is started, acquiring node local data for a first preset time period based on a first preset time interval; The spatial fusion further comprises the following steps: In response to detecting that the UPS power supply is started, based on the node device fusion data, it is determined whether an abnormality occurs in the current space. If an abnormality occurs, the node device fusion data is fused based on the node position information to obtain fused node data, and the fused node data includes an abnormal status code.

[0011] In some embodiments, the abnormal status code includes five groups of status codes and node location information, including temperature abnormality code, gas concentration abnormality code, smoke abnormality code, water level abnormality code and device status code, and the five groups of status codes are obtained based on preset mapping rules, and the preset mapping rules are: ,

[0012] Among them, L is the level after mapping, to is a preset threshold value for the reading of the local sensor of each of the nodes, is the actual reading of the local sensor of each node.

[0013] In some embodiments, generating a local node fusion data packet based on the fused node data, receiving a node fusion data packet from other nodes, fusion of the local node fusion data packet with the node fusion data packets from other nodes, generating a new local node fusion data packet after removing duplicate data, and forwarding the new local node fusion data packet includes: Generate a local node fusion data packet based on the fused node data, and receive node fusion data packets from other nodes; In response to detecting that the UPS power supply is started, determining whether the local node is abnormal based on the local node fusion data packet; If there is an abnormality, the local node fusion data packet is merged with the node fusion data packets of other nodes, and a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded; If there is no abnormality, the node fusion data packets received from other nodes are directly forwarded.

[0014] In some embodiments, in response to detecting that a UPS power supply is started, the forwarding method is broadcasting; In response to not detecting that the UPS power supply is started, the forwarding mode is multi-hop unicast.

[0015] Beneficial effects: The present invention integrates the equipment data in the same space by utilizing the node location information, thus reducing the reliance on centralized communication infrastructure, and maintaining monitoring of electromechanical equipment even when disasters occur, thus solving the problem of monitoring difficulties caused by unstable signal transmission in tunnels, reducing the difficulty of maintenance and rescue, and improving the safe operation level of highway tunnels. At the same time, when the UPS power supply is detected to be enabled and the equipment enters the low-power mode, the intermittent monitoring is adopted to extend the battery power supply time and ensure the continuous transmission of key data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 is a flow chart of a method for monitoring electromechanical equipment in a highway tunnel according to some embodiments of the present invention; Figure 2 is a flow chart of spatial fusion of some embodiments of the present invention; Figure 3It is a flowchart of data packet generation and forwarding in some embodiments of the present invention. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0018] like Figure 1-Figure 3 As shown, the present invention provides a method for monitoring electromechanical equipment in a highway tunnel, comprising the following steps: S1. Acquire node local data based on node local sensors and node local devices, where the node local data includes node location information; S2. Preprocessing the local data of the node, including signal filtering and feature extraction, to obtain preprocessed data; S3. Based on the node position information, spatially fuse the preprocessed data to obtain fused node data; S4. Generate a local node fusion data packet based on the fused node data, receive node fusion data packets from other nodes, fuse the local node fusion data packet with the node fusion data packets from other nodes, generate a new local node fusion data packet after removing duplicate data, and forward the new local node fusion data packet.

[0019] In some embodiments, step S2 comprises the following steps: S21. Filter the node local data to remove noise and interference.

[0020] In order to improve the accuracy of sensor data, the original signal must be filtered to remove noise and interference. In some embodiments, different filtering methods can be used for different types of data. For example, a low-pass filter, such as a moving average filter, is used for temperature, gas concentration, smoke concentration, and water level data to remove high-frequency noise. The moving average filter formula is: , in, Represents the value of the filtered signal at the nth sampling point; Represents the value of the original signal at the nkth sampling point; N represents the length of the moving average window, that is, the number of samples involved in the average calculation; Represents the sum index, from 0 to N-1.

[0021] For voltage and current data, due to the possible transient interference, a median filter is used to remove the spike noise. The median filter formula is: , in, Represents the value of the signal at the sampling point after filtering; Median represents the median operation, that is, taking the middle value of a set of data; Represents the sample value of the original signal in the window, the window size is 2k+1; k represents half the length of the window.

[0022] S22. Perform feature extraction on the node local data after noise reduction to obtain preprocessed data.

[0023] Similarly, in step S22, different methods may be used to extract features for different types of data.

[0024] For example, for temperature data and gas concentration data, their average values ​​can be extracted. The calculation formula for the average value is: , in, represents the average value (mean) of the filtered data; N represents the number of filtered data points collected over a period of time; Represents the value of the i-th filtered data point; Represents the sum of the values ​​from the 1st to the Nth data point.

[0025] For smoke density data and water level data, their maximum and minimum values ​​can be extracted.

[0026] For voltage and current data, its effective value (RMS) and crest factor can be extracted.

[0027] The effective value (RMS) calculation formula is: , Among them, RMS (Root Mean Square) represents the root mean square value of the filtered data; N represents the total number of sampling points of the data; Represents the value of the i-th filtered data point; represents the sum of the squares of all data points.

[0028] The formula for calculating the crest factor is: , Among them, CF (Crest Factor) represents the peak factor; Indicates the peak value of the filtered data, that is, the maximum absolute value of the data.

[0029] In some embodiments, in response to detecting that the UPS power supply is enabled, sensors, wireless communication relays, computing modules, wireless communication transmitters, etc., all enter a low power consumption mode. In the low power consumption mode, all modules do not adopt a continuous monitoring method, but an intermittent monitoring method, for example, monitoring and calculation are performed for 5 seconds every minute.

[0030] When the UPS power supply is enabled, it means that the conventional power supply of the electromechanical equipment in the highway tunnel has been disconnected, and the electromechanical equipment (node ​​local equipment), sensors (node ​​local sensors), and wireless communication relays, computing modules, and wireless communication transmitters that are set together with or inside the electromechanical equipment are all in an abnormal state. In this abnormal state, due to the limited power storage of the UPS power supply, it is necessary to save the power consumed by each device as much as possible without affecting the transmission of monitoring data.

[0031] In some embodiments, the acquiring of node local data includes: in response to detecting that a UPS power supply is started, acquiring node local data of a first preset time period based on a first preset time interval.

[0032] By adopting the intermittent monitoring method, the energy consumption of the equipment can be effectively reduced and the power supply time of the UPS power supply can be extended. In an emergency, limited power resources can be reasonably utilized and the necessary monitoring and data transmission services can be continuously provided.

[0033] In some embodiments, step S3 comprises the following steps: S31. Based on the node location information, the node data of the same type of devices in the same space are integrated to obtain node device integrated data, wherein the node device integrated data includes temperature data, gas concentration data, smoke concentration data, water level data and device status data.

[0034] Node location information is the location of the node of the device in the tunnel. In order to reduce the amount of data, the tunnel can be divided into the same space with a boundary of 20 meters, and then the space can be divided into the left space and the right space. Then two data (position in length and left / right) are used to represent the coordinates of the device in the space. Devices in the same space are represented by the same coordinates. If there are multiple identical devices in the same space, an algorithm is used to merge the data of multiple identical devices in the same space into one data during data fusion.

[0035] For example, using two parameters, four numbers represent the coordinates of the device, of which the first three represent the segment index and the last one represents the side index: The position in the tunnel is indexed by segments of 20 meters (taking a 10-kilometer tunnel as an example, the first segment is represented by 001, the second segment is represented by 002, and the 500th segment is represented by 500); the left and right sides of the tunnel are represented by 0 and 1, the left side is represented by 0, and the right side is represented by 1. For example, if the device is located on the left side of the 2442th meter in the tunnel, then the coordinates of the device are 1230, that is, the device is located on the left side of the 123rd segment in the tunnel.

[0036] By fusing the data of the same type of equipment in the same space, data redundancy can be effectively reduced, the load of data transmission and processing can be reduced, and the operating efficiency and real-time performance of the system can be improved. At the same time, using node location information for spatial division and data fusion can accurately reflect the status of equipment at various locations in the tunnel and enhance the accuracy and reliability of monitoring data. This method helps to detect equipment anomalies in a timely manner and improve the safety monitoring level of highway tunnels.

[0037] In some embodiments, when spatial coordinate fusion is performed on data of the same type of devices, different fusion strategies are adopted for data of different types of devices.

[0038] In some embodiments, the temperature data and the gas concentration data are obtained based on the following algorithm: , in, represents the reading of the i-th sensor; n represents the total number of sensors; Represents the temperature data after fusion; Indicates the gas concentration data after fusion; The smoke concentration data and water level data are obtained based on the following algorithm: , in, represents the reading of the i-th smoke sensor, Indicates the minimum water level data after fusion; Indicates the minimum water level data after fusion; The device status data is obtained based on the following method: counting the status of each device in the same space, and then determining the fusion status based on the counting result; The counting algorithm is: , s∈{normal, fault}; in, Indicates the number of devices with status s in the same space; k indicates the kth device; represents the actual state of the kth device, which can be "normal" or "faulty"; s represents the target state, which can be "normal" or "faulty"; n represents the total number of devices in the same space, which is used to traverse all devices; is an indicative function (indicator function), which is used to determine whether the device state is equal to s; Among them, the indicative function is defined as: , The fusion state determination algorithm is: , in, Indicates the device status of the same type of devices in the same space after fusion; Indicates the use The largest state s is found, and the state with the most occurrences is found. s represents the target state, and its value is either "normal" or "faulty".

[0039] Further integration of data from different types of equipment in the same space can comprehensively reflect the overall status of the space and provide more comprehensive monitoring information. This integration method helps to improve the accuracy of fault detection and early warning. By comprehensively analyzing multiple parameters, potential safety hazards can be discovered earlier, providing strong support for the safe operation and management of the tunnel.

[0040] In some embodiments, after the data of the same type of devices in the same space are merged, the data of different types of devices in the same space are merged.

[0041] In some embodiments, step S3 further includes the following steps: S32. Based on the node location information, the node device fusion data is fused to obtain fused node data, where the fused node data includes a spatial state.

[0042] In some embodiments, step S32 includes: S321. Based on the node location information, the node device fusion data is standardized, and the data is mapped to the [0, 1] interval to obtain standardized indicator data.

[0043] For positive indicators, the larger the value, the safer it is, such as oxygen concentration data. Therefore, for such data, the mapping formula is: ; For reverse indicators, the larger the value, the more dangerous it is, such as temperature data, smoke concentration data, water level data, and voltage and current data. Therefore, for such data, the mapping formula is: ; in, is the actual value of the lth indicator; and are the minimum reference value and maximum reference value of the i-th indicator respectively; is the value of the lth index after standardization, ranging from [0,1].

[0044] S322: performing weighted summation on the standardized indicator data according to the weight corresponding to each indicator data to obtain a comprehensive score of the space where the node is located.

[0045] The calculation formula for weighted sum is: , in, Indicates the result of the summation, l indicates the index number; n indicates that the total number of traversed indicators is n; represents the weight of the lth indicator; is the value of the lth indicator after standardization.

[0046] In some embodiments, the weight corresponding to each indicator data is preset. For example, the weight of temperature data and the weight of gas concentration data are both 0.25, which are directly related to the safety of personnel life and major losses of equipment. Giving higher weights can detect potential major accidents at an early stage; the weight of smoke concentration data is 0.2, and since it is easily disturbed by dust, etc., its weight is slightly lower than that of temperature and gas concentration; the weight of water level data is 0.15, and water level changes affect equipment and traffic safety, but the urgency and impact are slightly lower; since the impact range and urgency of equipment failure are relatively small, the weights of voltage and current are 0.1 and 0.05 respectively.

[0047] S323. According to the value of the comprehensive score, the space state is divided into different levels to obtain the space state.

[0048] For example, when S≥0.8, the space state is determined to be a safe state; when 0.5≤S<0.8, the space state is determined to be a warning state; when S<0.5, the space state is determined to be a dangerous state.

[0049] When the UPS power supply is not detected to be enabled, it indicates that the tunnel has a certain degree of passability. The comprehensive scoring method is used for data fusion, which can effectively integrate the data of different types of equipment in the same space to obtain a comprehensive evaluation index, which is convenient for quickly judging the overall safety status of the space and enables maintenance personnel to quickly reach the abnormal location.

[0050] Accordingly, when it is detected that the UPS power supply is enabled, the passability in the tunnel may be affected to a certain extent, and maintenance or search and rescue personnel are required to accurately locate and quickly determine the cause of the fault.

[0051] Therefore, in some embodiments, step S3 further includes the following steps: S33. In response to detecting that the UPS power supply is started, based on the node device fusion data, determine whether an abnormality occurs in the current space. If an abnormality occurs, the node device fusion data is fused based on the node position information to obtain fused node data, and the fused node data includes an abnormal status code.

[0052] In some embodiments, the abnormal status code includes five groups of status codes and node location information, including temperature abnormality code, gas concentration abnormality code, smoke abnormality code, water level abnormality code and device status code, and the status code is obtained based on a preset mapping rule, and the preset mapping rule is: , Among them, L is the level after mapping, to is a preset threshold value for the reading of the local sensor of each of the nodes, is the actual reading of the local sensor of each node.

[0053] Specifically, for temperature abnormality code, smoke abnormality code, and water level abnormality code: 1 indicates no abnormality (normal value), and 2-9 indicates positive abnormality.

[0054] For gas (oxygen) concentration abnormality codes: 5 means no abnormality (normal value), 1-4 means negative abnormality, and 6-9 means positive abnormality.

[0055] For negative anomalies, the lower the value, the smaller it is than the normal value. For positive anomalies, the higher the value, the larger it is than the normal value.

[0056] For the device status exception code, 0 means no exception and 1 means exception.

[0057] For the temperature sensor, the individual temperature thresholds are: , .

[0058] For the gas (oxygen) concentration sensor, the individual gas concentration thresholds are: , .

[0059] For the smoke density sensor, the smoke density thresholds are: , .

[0060] For the water level sensor, the water level thresholds are: , .

[0061] Through the above mapping rules, the sensor data can be transformed into a code string of abnormal status codes. For example, the code string of the abnormal status code is: 432011230, which means that the temperature is three levels higher than the normal temperature, the gas concentration (such as oxygen) is two levels lower than the normal value, and the smoke concentration is one level higher than the normal value, but the water level and the equipment are working normally. The location where the abnormality occurs is on the left side of the 123rd section in the tunnel. This code string shows that the temperature rises, the oxygen content decreases, and smoke appears, but it has not yet affected the working equipment. Based on this data, it can be judged that an early fire may have occurred, and it is not a fire caused by a short circuit in the equipment due to water immersion.

[0062] In some embodiments, step S4 includes: S41. Generate a local node fusion data packet based on the fused node data, and receive node fusion data packets from other nodes.

[0063] In some embodiments, in response to not detecting that the UPS power supply is started and the power supply of each device and module is normal, the generated local node fusion data packet includes multiple fields: node ID, location information, spatial status, and timestamp. For example, the generated code may be: [0123 | 1230 | warning | 1627545600] In some embodiments, in response to not detecting that the UPS power supply is started, each device and module switches to a low power consumption mode, and the generated local node fusion data packet includes the following fields: node ID, location information, abnormal status code, and timestamp. For example, the generated code may be: [0123 | 1230 | 32110 | 1627545600].

[0064] S42: In response to detecting that the UPS power supply is started, determining whether there is an abnormality in the local node based on the local node fusion data packet.

[0065] In some embodiments, the abnormal status code in the local node fusion data packet can be directly used to determine whether the local node is abnormal. For example, when the level of a code does not belong to no abnormality, the node where the local node fusion data packet is located is determined to be abnormal.

[0066] S43. If there is an abnormality, the local node fusion data packet is merged with the node fusion data packets of other nodes, and a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded.

[0067] In some embodiments, a hash table deduplication algorithm may be used to remove duplicate data based on timestamp and location information.

[0068] In some embodiments, in order to avoid redundancy and loops, a cache mechanism may be used to forward data packets based on their unique identifiers (such as location information + timestamp) to prevent repeated forwarding.

[0069] For example, the fusion and forwarding strategy codes are:

[0070]

[0071]

[0072]

[0073] S44. If there is no abnormality, the node fusion data packets received from other nodes are directly forwarded.

[0074] By adopting the above-mentioned fusion forwarding strategy, for nodes with abnormalities, local abnormal data and received abnormal data are fused to generate more comprehensive abnormal information, and the fused data packets are continuously sent to improve the reliability of abnormal information transmission; for nodes without abnormalities, only data packets are received and forwarded to reduce calculation and energy consumption; for the overall network, data fusion is used to enhance the accuracy and reliability of abnormality detection, while the strategy in low-power mode balances energy consumption and monitoring requirements and prolongs the battery life of the device. At the same time, the cache mechanism is used to avoid redundancy and circulation of data packets.

[0075] In some embodiments, in response to detecting that a UPS power supply is started, the forwarding method is broadcasting; In response to not detecting that the UPS power supply is started, the forwarding mode is multi-hop unicast.

[0076] In low-power mode, the sending strategy adopts broadcasting, so that abnormal information can be received by all relevant nodes. Even if some nodes are disconnected, it will not affect the propagation of abnormal information.

[0077] Finally, it should be understood that the embodiments described in the present invention are only used to illustrate the principles of the embodiments of the present invention. Other variations may also fall within the scope of the present invention. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present invention may be considered consistent with the teachings of the present invention. Accordingly, the embodiments of the present invention are not limited to the embodiments explicitly introduced and described in the present invention.

Claims

1. A method for monitoring electromechanical equipment in a highway tunnel, characterized in that: The following steps are involved: Based on the node local sensor and the node local device, obtaining the node local data, wherein the node local data includes the node location information; Preprocessing the local data of the node, including signal filtering and feature extraction, to obtain preprocessed data; Based on the node position information, spatially fuse the preprocessed data to obtain fused node data; A local node fusion data packet is generated based on the fused node data, node fusion data packets of other nodes are received, the local node fusion data packet is merged with the node fusion data packets of other nodes, a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded.

2. The monitoring method according to claim 1, characterized in that: The spatial fusion comprises the following steps: Based on the node location information, the node data of the same type of devices in the same space are fused to obtain node device fusion data, which includes temperature data, gas concentration data, smoke concentration data, water level data and device status data.

3. The monitoring method according to claim 2, characterized in that: The temperature data and the gas concentration data are obtained based on the following algorithm: ； in, represents the reading of the i-th sensor; n represents the total number of sensors; Represents the temperature data after fusion; Indicates the gas concentration data after fusion; The smoke concentration data and water level data are obtained based on the following algorithm: ； in, represents the reading of the i-th smoke sensor, Indicates the minimum smoke density data after fusion; Indicates the minimum water level data after fusion; The device status data is obtained based on the following method: counting the status of each device in the same space, and then determining the fusion status based on the counting result; The counting algorithm is: , s∈{normal, fault}; in, Indicates the number of devices with status s in the same space; k indicates the kth device; Indicates the actual state of the kth device, which can be "normal" or "faulty"; s indicates the target state, which can be "normal" or "faulty"; n indicates the total number of devices in the same space, which is used to traverse all devices; is an indicative function used to determine whether the device state is equal to s; Among them, the indicative function is defined as: ; The algorithm for determining the fusion state is: ； in, Indicates the device status of the same type of devices in the same space after fusion; Indicates the use The largest state s, find the state with the most occurrences.

4. The monitoring method according to claim 3, characterized in that: The spatial fusion further comprises the following steps: Based on the node location information, the node device fusion data is fused to obtain fused node data, and the fused node data includes a spatial state.

5. The monitoring method according to claim 4, characterized in that: Based on the node location information, the node device fusion data is fused to obtain fused node data, wherein the fused node data includes a spatial state, including: Based on the node location information, the node device fusion data is standardized, and the data is mapped to the [0,1] interval to obtain standardized indicator data; The standardized indicator data are weighted and summed according to the weight corresponding to each indicator data to obtain a comprehensive score of the space where the node is located; According to the value of the comprehensive score, the space state is divided into different levels to obtain the space state.

6. The monitoring method according to claim 3, characterized in that: The acquiring of node local data comprises: in response to detecting that the UPS power supply is started, acquiring node local data of a first preset time period based on a first preset time interval; The spatial fusion further comprises the following steps: In response to detecting that the UPS power supply is started, based on the node device fusion data, it is determined whether an abnormality occurs in the current space. If an abnormality occurs, the node device fusion data is fused based on the node position information to obtain fused node data, and the fused node data includes an abnormal status code.

7. The monitoring method according to claim 6, characterized in that: The abnormal status code includes five groups of status codes and node location information, including temperature abnormality code, gas concentration abnormality code, smoke abnormality code, water level abnormality code and equipment status code. The five groups of status codes are obtained based on preset mapping rules. The preset mapping rules are: ， Among them, L is the level after mapping, to is a preset threshold value for the reading of the local sensor of each of the nodes, is the actual reading of the local sensor of each node.

8. The monitoring method according to claim 1, characterized in that: The method generates a local node fusion data packet based on the fused node data, receives node fusion data packets from other nodes, fuses the local node fusion data packet with node fusion data packets from other nodes, generates a new local node fusion data packet after removing duplicate data, and forwards the new local node fusion data packet, including: Generate a local node fusion data packet based on the fused node data, and receive node fusion data packets from other nodes; In response to detecting that the UPS power supply is started, determining whether the local node is abnormal based on the local node fusion data packet; If there is an abnormality, the local node fusion data packet is merged with the node fusion data packets of other nodes, and a new local node fusion data packet is generated after removing duplicate data, and the new local node fusion data packet is forwarded; If there is no abnormality, the node fusion data packets received from other nodes are directly forwarded.

9. The monitoring method according to claim 8, characterized in that: In response to detecting that the UPS power supply is started, the forwarding method is broadcasting; In response to not detecting that the UPS power supply is started, the forwarding mode is multi-hop unicast.

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