Coal mine flood alarm method based on image and hydrological data monitoring

Abstract

The invention belongs to the technical field of coal mine flood early warning, and discloses a coal mine flood alarm method based on image and hydrological data monitoring. The method comprises the following steps: detecting the initial state of nearby underground water in mine tunneling through a lossless detector on a coal mine underground tunneling working face, a coal mining working face or a working face where a water permeation accident possibly occurs, and obtaining the initial water level and water temperature data of the nearby underground water of the mine; the method comprises the following steps: acquiring a change process of water level and water temperature data in a process, presenting the change process in an image video stream, acquiring a change rule of the water level and water temperature data in a future tunneling process in combination with geological environment elements possibly influencing the change of the water level and water temperature data, forming a regular trend sequence, and forming a scatter set for abnormal trend points; and obtaining a credibility score, and visually displaying the credibility score. According to the invention, the early detection is realized, the dangerous information development trend is accurately judged, and the method has practical significance in prevention and protection of life and property safety.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine flood early warning, and in particular relates to a coal mine flood alarm method based on image and hydrological data monitoring. Background Art

[0002] Mine flood is a serious disaster in coal mines. The alarm of mine flood must be timely and accurate in coal mine production. At present, flood warning is mainly based on hydrological detection and prevention, underground water exploration, and observation of precursor phenomena. Hydrological detection and underground water exploration can prevent underground flood accidents, but due to the complex hydrological conditions, improper design, ineffective measures, poor management and mental paralysis of people, hydrological detection and underground water exploration cannot completely prevent the occurrence of water inrush, let alone alarm for sudden underground water inrush; the observation of precursor phenomena is mainly based on human experience and judgment, and there are large subjective factors. At present, for on-site water inrush accidents, it mainly relies on the manual alarm of on-site personnel, but when the water inrush occurs at an unattended time or area, or the on-site personnel flee in a hurry and fail to actively alarm, the dispatch room cannot obtain the information of the water inrush in time, and cannot notify the relevant staff in the mine in time, so that emergency measures cannot be taken in time for the water inrush accident, which is easy to cause water damage out of control and casualties. In order to effectively reduce the property losses and casualties caused by floods in mines, a new underground flood alarm method is needed, which can accurately alarm underground water inrush in the first time, and buy precious rescue and escape time for underground personnel in other areas who are not at the scene of the disaster.

[0003] To solve the above problems, the invention patent of coal mine flood alarm method based on image and hydrological data monitoring (publication number CN105569732A, publication date 20160104) discloses that cameras are placed at underground coal mine excavation working faces, coal mining working faces or other working faces where water seepage accidents may occur, and the water level and water temperature data of the ground hydrological observation holes are monitored at the same time; when abnormal water flow is detected in the set area in the camera video image, and the water flow duration exceeds the set time threshold or the water flow increase speed exceeds the set threshold, and the water level or water temperature data of the ground hydrological observation hole is abnormal, a flood alarm signal is issued. This patent takes into account the characteristics of underground coal mine floods, automatically and promptly takes corresponding measures, and can accurately alarm the mine water inrush in the first time, so as to buy precious disaster relief and escape time for underground personnel in other areas who are not at the site of the water inrush.

[0004] Pre-perception technology has been widely used in network information processing. Combined with the existing knowledge base, it can effectively make intelligent predictions for known and unknown threats, effectively guide the system's active self-evolution recognition, and extract and judge the threats to the system in accordance with the consistency of sensitive information in the processing process. Measures can be taken in advance to prevent further development of the threats, thereby achieving prior detection. Applying pre-perception technology to coal mine floods and accurately judging the development trend of dangerous information is of practical significance for preventing them before they happen and protecting the safety of life and property. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a coal mine flood alarm method based on image and hydrological data monitoring.

[0006] The technical solution is as follows: A coal mine flood alarm method based on image and hydrological data monitoring includes:

[0007] S1, using non-destructive detectors to collect the initial state of groundwater at the coal mine excavation working face, coal mining working face and working face where water seepage accidents occur, and obtain the initial water level and water temperature data of the mine groundwater;

[0008] S2, in the process of excavation, obtain the change history of water level and water temperature data and present it in the form of image video stream; combine the geological environment factors that affect the change of water level and water temperature data to obtain the change law of water level and water temperature data in the future excavation process, form a regular trend sequence, and form a scattered point set for abnormal trend points;

[0009] S3, simulate and compare the historical experience of coal mine flood data anomalies based on image and hydrological data monitoring to obtain a credibility score. The higher the score, the greater the credibility. Based on the credibility score, optimize the water level and water temperature data during the excavation process.

[0010] S4, based on the optimized regular trend sequence, is applied to the coal mine flood alarm terminal for visualization.

[0011] In step S2, a regular trend sequence is formed, including:

[0012] S201, the system is initialized, the excavation process stage is allocated, and the regularity trend evolution data of each water level and water temperature node to be predicted is uploaded to the regularity trend evolution judgment center ECS;

[0013] S202, the water level and water temperature node to be predicted notifies the regularity trend evolution prediction node IA, and the regularity trend evolution prediction node predicts the regularity trend evolution data updated by the water level and water temperature node to be predicted;

[0014] S203, packaging the regular trend evolution prediction results into a trend sequence set, reaching a consensus within all regular trend evolution prediction nodes based on a two-level judgment mechanism combined with clustering verification, and uploading it through a transmission channel.

[0015] In step 1, the system is initialized and the tunneling process phases are allocated, including:

[0016] (1) The authentication center IA generates a main hydrological data encoding and decoding pair (MPK, MSK)←Steup(λ), where λ is a mapping parameter. The main hydrological data encoding MPK of the system is public, and the main hydrological data decoding MSK is decrypted and saved by the authentication center IA during the excavation process. Steup(λ) is the generation method.

[0017] (2) Node EC to be predicted i Submit an application to join the transmission channel chain AC. After the excavation process certification center IA reviews the excavation process, it will become the node to be predicted EC. i Assigning the evolution set of trend sequence of tunneling process stages in, It is the node's globally unique tunneling process stage identifier. Encode and decode the hydrological data of the node;

[0018] (3) The regular trend evolution prediction node RA submits an application to join the transmission channel chain AC. After the excavation process stage certification center IA passes the qualification review, the regular trend evolution prediction node RA is allocated the excavation process stage trend sequence evolution set And for Ra j The generated node attribute main hydrological data decoding ASK, ASK = KeyGen (MPK, MSK, A p ),in, It is the node's globally unique tunneling process stage identifier. is the node’s hydrological data encoding and decoding pair, A p =(a1, a2…a n ) indicates Ra j The attribute set of the node; KeyGen() is the node attribute main hydrological data interpretation function;

[0019] (4) Node to be predicted Ec i Combine each hydrological data trend sequence evolution message CT with the index index to form a set M, and attach the access strategy A in the form of hydrological data trend evolution mapping s , symmetric hydrological data encoding key′ after attribute decoding m , the summary value of M is MD, to be predicted node Eci Node verification of MD Composition file file j After that, it is sent to the regularity trend evolution judgment center Ecs;

[0020] (5) The regularity trend evolution judgment center Ecs receives the corresponding data and verifies the node to be predicted Ec i , and calculate the hash value of the set M to verify MD, and send the prediction to the node Ec i Returns file file j The storage path sl j ; Node to be predicted Ec i Storage path to sl j After that, the message of data update will be sent to Send to RA node; is the address of the node to be predicted, Verification information of the node to be predicted;

[0021] (6) The regularity trend evolution prediction node RA predicts the regularity trend evolution data updated by the water level and water temperature node EC to be predicted, and calculates the position of the regional block ID of each regional block in different time periods of the excavation process on the hash ring according to the number of regional blocks X in different time periods of the excavation process, and calculates the position of each RA node on the hash ring by the formula Hash(ID+random); ID is the address, and random is the random position; according to the position of the regularity trend evolution prediction node RA on the hash ring, search for the regional block nodes in different time periods of the excavation process clockwise, and the first regional block node in different time periods of the excavation process found is the regional block in different time periods of the excavation process to which the RA node belongs;

[0022] (7) Verify whether the number of regular trend evolution prediction nodes RA contained in each area block of different time periods of the excavation process is greater than or equal to four. If not, return to reselect random numbers to calculate the RA node position and re-excavate the area blocks of different time periods of the excavation process; if the verification passes, determine the area blocks of different time periods of the excavation process, and all nodes will retain a list of all nodes and a list of nodes in the area blocks of different time periods of the excavation process;

[0023] (8) Based on the regional block results of different time periods of the excavation process, set the regional block markers of different time periods of the excavation process for the RA node, and set the hydrological data image markers v of the regional blocks of different time periods of the excavation process. g The number of nodes in the area block at different time periods during the excavation process g , determine the marker p of the main node in the area block of different time periods of the tunneling process in the current hydrological data image g , p g =vg mod|num g |, where mod|| is the positive orientation value determined by the number of nodes in the regional block at different time periods of the excavation process; a global marker is set for the main node in the regional block at different time periods of each excavation process, and the global hydrological data image marker v a The number of blocks in different time periods of the excavation process a , determine the global main node marker p in the current hydrological data image a , p a =v a mod|num a |;

[0024] (9) Node to be predicted Ec i Send its own update message and the verification sig of the message update The message is sent to the global master node Ra k ; Ra k After receiving the update message, the verification sig of the update message update Verify the signature. If it is illegal, a scattered point set is formed. If it is legal, the regular trend evolution prediction related data is determined.

[0025] (10) Global master node Ra k After verifying the authenticity and integrity of the regularity trend evolution prediction related data, the prediction is made and recorded in the update request table Utable0, the global master node Ra k Treat prediction node Ec i Recently updated m regular trend evolution prediction algorithms, predict and obtain their respective regular trend evolution values ​​VaR m , and update Ec i The final regular trend evolution value VaR generates a regular trend evolution value change information tx, which is packaged into the trend sequence set; among them, VaR p is the final regular trend evolution value before the change, VaR a is the final regularity trend evolution value after update, ID alg It is the identifier of the regular trend evolution algorithm based on which the current evaluation result is based. Height is the exact height of the trend sequence set to which the current tx belongs. Index is the index.

[0026] (11) Global master node Ra k Collect the regular trend evolution value change information tx at time t, and form the collected regular trend evolution value change information tx into Blockheader, then Among them, Prehash is the hash pointer pointing to the previous trend sequence set, and Timestamp represents the block timestamp. Indicates the master node Ra k Verification of the trend sequence set, TX Root represents the root of the Merkle tree composed of all txs in the current trend sequence set; Is the master node address;

[0027] (12) Global master node Ra k Send consensus proposal <Proposal,V a , Height, Blockheader, UtableO) to the regional block master node of each excavation process in different time periods, and the regional block master node of each excavation process in different time periods receives the global master node Ra k After the consensus proposal is sent, the hydrological data image marker V in the area block at different time periods of the excavation process is attached. g After that, it is broadcasted in the regional blocks at different time periods of the excavation process, and the replica RA nodes in the regional blocks at different time periods of the excavation process verify the trend sequence set; Proposal is a proposal, V a is the regular trend evolution value;

[0028] (13) After verification is completed, consensus response messages are sent to the blocks in different time periods of the excavation process. Give the master nodes of the regional blocks in different time periods of the excavation process, among which, is the identifier of the current replica RA node, Verify the consensus response message for the current replica RA node; Respond g is the response value;

[0029] (14) The master node of the regional block in different time periods of the excavation process collects the consensus response messages in the regional block in different time periods of the excavation process. When more than or equal to 2f2+1 legitimate regional block consensus response messages including the master node itself are collected, the verification of each replica RA node in the message is clustered into And send a global consensus response message to the global master node Among them, f2 is the number of Byzantine nodes in the regional block at different time periods of the mining process, list g Respond to the ID list of nodes in the regional blocks at different time periods for all excavation processes involved in generating cluster verification; a is the response value of node a;

[0030] (15) Global master node Ra kCollect the global consensus response messages sent by the regional block master nodes in different time periods of the excavation process. When more than or equal to 2f2+1 global consensus response messages are collected, cluster verification in the message is clustered again into And transmit a consensus confirmation message Among them, list a It is the ID list of all RA nodes participating in cluster verification. Commit is the delegated data.

[0031] (16) RA node receives the global master node Ra k After the consensus confirmation message is sent, use the list a Validation of hydrological data encoding in nodes After verification, the consensus trend sequence set of this round will be synchronized to the transmission channel of this node.

[0032] In step S203, a consensus is reached based on a two-level judgment mechanism combined with cluster verification, including:

[0033] Calculate the location of the regional block IDs in different time periods of the excavation process, calculate the location of the RA node, determine the regional blocks in different time periods of the excavation process to which the node belongs, and verify the number of RA nodes contained in the regional blocks in different time periods of each excavation process;

[0034] Set the markers in the area blocks and global markers for different time periods of the excavation process. k Consensus proposals are sent to the master nodes of the regional blocks in different time periods of each excavation process. Consensus is reached within the regional blocks in different time periods of the excavation process. The master nodes of the regional blocks in different time periods of the excavation process send global consensus responses to the global master node. Global consensus is reached to form a regular trend sequence. For nodes that have not reached consensus, abnormal trend points are determined and a scattered point set is formed.

[0035] In step S4, based on the optimized regular trend sequence, it is applied to the coal mine flood alarm terminal, including:

[0036] First, Ec i Every time t h The data that occurs once in a time period is processed, and the update first predicts the relevant data m for each updated regular trend evolution, and establishes an index index used to identify the specific serial number of the data in the regular trend evolution judgment center storage path;

[0037] Secondly, the hydrological data encoding key is randomly selected from the symmetric file in the hydrological data encoding space. m Decode m to get a hydrological data trend sequence evolution message CT, and follow the custom access strategy A of the current node s , use MPK to decode the symmetric file hydrological data encoding keym Get the key m ←Encrypt(MPK,A s ,key m ), Encrypt() is the compilation function.

[0038] In step (6), the regularity trend evolution prediction node RA predicts the regularity trend evolution data updated by the water level to be predicted and the water temperature node EC, including:

[0039] On-duty node Ra k After receiving the update, use Ec i Hydrological data coding right Verify the signature and request the regularity trend evolution judgment center to download the data stored in sl j File in j ;

[0040] ECS will determine Ra based on the access policy corresponding to the requested file. k Whether the conditions are met: If the conditions are met, Ra is allowed k Download file j Get M by comparing update with file j In and MD, to determine whether the data source is authentic and valid and whether the data stored in the regularity trend evolution judgment center has been tampered with;

[0041] Decryption key m Get file hydrological data encoding and decoding And use key m The hydrological data trend evolution mapping data m is obtained, and a regularity trend evolution evaluation algorithm is selected to calculate the regularity trend evolution value.

[0042] In step (12), the trend sequence set is verified, including:

[0043] Verification of block producing nodes Verify the signature and determine the v in the consensus proposal a , v g , whether the Height is correct; by n+1 The trend sequence collection header is hashed, Block n+1 Is the parent trend sequence set of Block n ; Check Block n+1 Check whether there is any abnormal value assignment for tx in the trend sequence set, and check whether its root hash value is the same as the TX Toot in the trend sequence set header;

[0044] When the master node in the regional block of different time periods of the global or excavation process fails to produce a block within the specified time, the next RA node will produce a block to replace the hydrological data image. The RA node initiates a request to replace the hydrological data image, and the replica RA node verifies the request to replace the hydrological data image. The RA node collects legal change confirmation messages, and the new RA master node performs the responsibility of generating the prediction and trend sequence set.

[0045] Furthermore, the next RA node generates a block to replace the hydrological data image, including:

[0046] 1) The replica RA node initiates a request to replace the hydrological data image:

[0047] The replica RA node discovers the master node Ra k Send hydrographic data image change request when there is a problem To the replica RA node, where v a +1 indicates the changed hydrological data image marker. Indicates the ID of the replica RA node that initiates the hydrological data image change request. Indicates the node's verification of the hydrological data image change request, sig agg-p Represents Ra u The cluster verification collected in the previous round of consensus is used to prove the correctness of the trend sequence set with height Height;

[0048] 2) Replica node verifies the hydrological data image change request: After receiving the hydrological data image change request, the replica RA node verifies the request and broadcasts the change confirmation message after the verification is passed. in, Indicates the ID of the current replica RA node, Indicates the verification of the change confirmation message by the current replica RA node;

[0049] 3) The replica RA node collects legal change confirmation messages: After the replica RA node collects more than or equal to 2f+1 legal change confirmation messages, the hydrological data image in the regional block in different time periods of the excavation process is changed to f=f2, the global hydrological data image is changed to f=f1, and the hydrological data image marker is updated to v a +1; The new RA master node will be responsible for generating forecast and trend series sets.

[0050] In step S3, a simulation comparison is performed based on the historical experience of abnormal coal mine flood data monitored by images and hydrological data, including: placing cameras at the underground excavation working face, coal mining working face and working face where water seepage accidents occur in the coal mine, drilling hydrological observation holes on the well to monitor the groundwater status of the mine, and monitoring the water level and water temperature data in the hydrological observation holes;

[0051] The video image data is monitored in real time. When abnormal water flow is detected in the set area in the camera video image, and the water flow duration exceeds the set time threshold or the water flow increase rate exceeds the set threshold, it is judged as data abnormality; and when it is found that the water level or water temperature change of the hydrological observation hole exceeds the set threshold, a flood alarm signal is immediately issued.

[0052] Furthermore, a simulation comparison is conducted based on the formation of regular trend sequences and the anomaly historical experience of coal mine flood data based on image and hydrological data monitoring to obtain a credibility score.

[0053] In combination with all the above-mentioned technical solutions, the beneficial effects of the present invention are as follows: based on the acquired monitoring images and hydrological data, and in combination with non-destructive detection technology, the present invention utilizes advance perception technology to predict the pre-evolution trend before the occurrence of coal mine floods, effectively extracts sensitive information in the process, and consistently determines the threat of the perception system, and takes measures in advance for the further development of the threat, thereby achieving prior detection and accurately judging the development trend of dangerous information, which is of practical significance for preventing accidents before they happen and protecting the safety of life and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0055] Figure 1 It is a flow chart of a coal mine flood alarm method based on image and hydrological data monitoring provided by an embodiment of the present invention;

[0056] Figure 2 It is a flow chart of forming a regular trend sequence provided by an embodiment of the present invention;

[0057] Figure 3 Schematic diagram of a coal mine flood alarm system based on image and hydrological data monitoring provided by an embodiment of the present invention;

[0058] Figure 4 It is a principle diagram of dividing the regional blocks in different time periods of each excavation process provided by an embodiment of the present invention;

[0059] Figure 5 It is a schematic diagram of regional block transmission in different time periods of each excavation process provided by an embodiment of the present invention;

[0060] In the figure: 1. Initial water level and water temperature data acquisition module; 2. Regular trend sequence acquisition module; 3. Simulation comparison module; 4. Coal mine flood alarm display module. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0062] Embodiment 1, as Figure 1 As shown, the coal mine flood alarm method based on image and hydrological data monitoring provided by the embodiment of the present invention includes:

[0063] S1, using non-destructive detectors to collect the initial state of groundwater at the coal mine excavation working face, coal mining working face and working face where water seepage accidents occur, and obtain the initial water level and water temperature data of the mine groundwater;

[0064] S2, in the process of excavation, obtain the change history of water level and water temperature data and present it in the form of image video stream; combine the geological environment factors that affect the change of water level and water temperature data to obtain the change law of water level and water temperature data in the future excavation process, form a regular trend sequence, and form a scattered point set for abnormal trend points;

[0065] S3, simulate and compare the historical experience of coal mine flood data anomalies based on image and hydrological data monitoring to obtain a credibility score. The higher the score, the greater the credibility. Based on the credibility score, optimize the water level and water temperature data during the excavation process.

[0066] S4, based on the optimized regular trend sequence, is applied to the coal mine flood alarm terminal for visualization.

[0067] like Figure 2 As shown, in step S2, a regular trend sequence is formed, including:

[0068] S201, the system is initialized, the excavation process stage is allocated, and the regularity trend evolution data of each water level and water temperature node to be predicted is uploaded to the regularity trend evolution judgment center ECS;

[0069] S202, the water level and water temperature node to be predicted notifies the regularity trend evolution prediction node IA, and the regularity trend evolution prediction node predicts the regularity trend evolution data updated by the water level and water temperature node to be predicted;

[0070] S203, packaging the regular trend evolution prediction results into a trend sequence set, reaching a consensus within all regular trend evolution prediction nodes based on a two-level judgment mechanism combined with clustering verification, and uploading it through a transmission channel.

[0071] Exemplarily, in step S203, a consensus is reached based on a two-level judgment mechanism combined with cluster verification, including:

[0072] Calculate the location of the regional block IDs in different time periods of the excavation process, calculate the location of the RA node, determine the regional blocks in different time periods of the excavation process to which the node belongs, and verify the number of RA nodes contained in the regional blocks in different time periods of each excavation process;

[0073] Set the markers in the area blocks and global markers for different time periods of the excavation process. k Consensus proposals are sent to the master nodes of the regional blocks in different time periods of each excavation process. Consensus is reached within the regional blocks in different time periods of the excavation process. The master nodes of the regional blocks in different time periods of the excavation process send global consensus responses to the global master node. Global consensus is reached to form a regular trend sequence. For nodes that have not reached consensus, abnormal trend points are determined and a scattered point set is formed.

[0074] In order to ensure that the algorithm is resistant to the influence of abnormal trend point interference, the number of regional blocks X in different time periods of the excavation process needs to satisfy X ≥ 4, and the number of nodes Y in the regional blocks in different time periods of each excavation process also needs to satisfy Y ≥ 4. First, the position of each ID on the hash ring is calculated according to the number X. At this time, multiple hash calculations can be performed on the regional block IDs in different time periods of each excavation process to generate multiple virtual nodes on the hash ring, thereby reducing the problem of data skew, such as Figure 4 The node transmission principle in each block in different time periods of each excavation process is as follows: Figure 5 shown.

[0075] Embodiment 2, exemplary, system initialization, and allocation of tunneling process stages, including:

[0076] (1) The authentication center IA generates a main hydrological data encoding and decoding pair (MPK, MSK)←Steup(λ), where λ is a mapping parameter. The main hydrological data encoding MPK of the system is public, and the main hydrological data decoding MSK is decrypted and saved by the authentication center IA during the excavation process. Steup(λ) is the generation method.

[0077] (2) Node EC to be predicted i Submit an application to join the transmission channel chain AC. After the excavation process certification center IA reviews the excavation process, it will become the node to be predicted EC. i Assigning the evolution set of trend sequence of tunneling process stages in, It is the node's globally unique tunneling process stage identifier. Encode and decode the hydrological data of the node;

[0078] (3) The regular trend evolution prediction node RA submits an application to join the transmission channel chain AC. After the excavation process stage certification center IA passes the qualification review, the regular trend evolution prediction node RA is allocated the excavation process stage trend sequence evolution set And for Ra j The generated node attribute main hydrological data decoding ASK, ASK = KeyGen (MPK, MSK, A p ),in, It is the node's globally unique tunneling process stage identifier. is the node’s hydrological data encoding and decoding pair, A p =(a1, a2…a n ) indicates Ra j The attribute set of the node; KeyGen() is the node attribute main hydrological data interpretation function;

[0079] (4) Node to be predicted Ec i Combine each hydrological data trend sequence evolution message CT with the index index to form a set M, and attach the access strategy A in the form of hydrological data trend evolution mapping s , symmetric hydrological data encoding key′ after attribute decoding m , the summary value of M is MD, to be predicted node Ec i Node verification of MD Composition file file j After that, it is sent to the regularity trend evolution judgment center Ecs;

[0080] (5) The regularity trend evolution judgment center Ecs receives the corresponding data and verifies the node to be predicted Ec i , and calculate the hash value of the set M to verify MD, and send the prediction to the node Ec i Returns file file j The storage path sl j ; Node to be predicted Ec i Storage path to sl j After that, the message of data update will be sent to Send to RA node; is the address of the node to be predicted, Verification information of the node to be predicted;

[0081] (6) The regularity trend evolution prediction node RA predicts the regularity trend evolution data updated by the water level and water temperature node EC to be predicted, and calculates the position of the regional block ID of each regional block in different time periods of the excavation process on the hash ring according to the number of regional blocks X in different time periods of the excavation process, and calculates the position of each RA node on the hash ring by the formula Hash(ID+random); ID is the address, and random is the random position; according to the position of the regularity trend evolution prediction node RA on the hash ring, search for the regional block nodes in different time periods of the excavation process clockwise, and the first regional block node in different time periods of the excavation process found is the regional block in different time periods of the excavation process to which the RA node belongs;

[0082] (7) Verify whether the number of regular trend evolution prediction nodes RA contained in each area block of different time periods of the excavation process is greater than or equal to four. If not, return to reselect random numbers to calculate the RA node position and re-excavate the area blocks of different time periods of the excavation process; if the verification passes, determine the area blocks of different time periods of the excavation process, and all nodes will retain a list of all nodes and a list of nodes in the area blocks of different time periods of the excavation process;

[0083] (8) Based on the regional block results of different time periods of the excavation process, set the regional block markers of different time periods of the excavation process for the RA node, and set the hydrological data image markers v of the regional blocks of different time periods of the excavation process. g The number of nodes in the area block at different time periods during the excavation process g , determine the marker p of the main node in the area block of different time periods of the tunneling process in the current hydrological data image g , p g =v g mod|num g |, where mod|| is the positive orientation value determined by the number of nodes in the regional block at different time periods of the excavation process; a global marker is set for the main node in the regional block at different time periods of each excavation process, and the global hydrological data image marker v a The number of blocks in different time periods of the excavation process a , determine the global main node marker p in the current hydrological data image a , p a =v a mod|num a |;

[0084] (9) Node to be predicted Ec i Send its own update message and the verification sig of the message update The message is sent to the global master node Ra k ; Ra k After receiving the update message, the verification sig of the update messageupdate Verify the signature. If it is illegal, a scattered point set is formed. If it is legal, the regular trend evolution prediction related data is determined.

[0085] (10) Global master node Ra k After verifying the authenticity and integrity of the regularity trend evolution prediction related data, the prediction is made and recorded in the update request table Utable0, the global master node Ra k Treat prediction node Ec i Recently updated m regular trend evolution prediction algorithms, predict and obtain their respective regular trend evolution values ​​VaR m , and update Ec i The final regular trend evolution value VaR generates a regular trend evolution value change information tx, which is packaged into the trend sequence set; among them, VaR p is the final regular trend evolution value before the change, VaR a is the final regularity trend evolution value after update, ID alg It is the identifier of the regular trend evolution algorithm based on which the current evaluation result is based. Height is the exact height of the trend sequence set to which the current tx belongs. Index is the index.

[0086] (11) Global master node Ra k Collect the regular trend evolution value change information tx at time t, and form the collected regular trend evolution value change information tx into Blockheader, then Among them, Prehash is the hash pointer pointing to the previous trend sequence set, and Timestamp represents the block timestamp. Indicates the master node Ra k Verification of the trend sequence set, TX Root represents the root of the Merkle tree composed of all txs in the current trend sequence set; Is the master node address;

[0087] (12) Global master node Ra k Send consensus proposal <Proposal,V a , Height, Blockheader, UtableO) to the regional block master node of each excavation process in different time periods, and the regional block master node of each excavation process in different time periods receives the global master node Ra k After the consensus proposal is sent, the hydrological data image marker V in the area block at different time periods of the excavation process is attached. gAfter that, it is broadcasted in the regional blocks at different time periods of the excavation process, and the replica RA nodes in the regional blocks at different time periods of the excavation process verify the trend sequence set; Proposal is a proposal, V a is the regular trend evolution value;

[0088] (13) After verification is completed, consensus response messages are sent to the blocks in different time periods of the excavation process. Give the master nodes of the regional blocks in different time periods of the excavation process, among which, is the identifier of the current replica RA node, Verify the consensus response message for the current replica RA node; Respond g is the response value;

[0089] (14) The master node of the regional block in different time periods of the excavation process collects the consensus response messages in the regional block in different time periods of the excavation process. When more than or equal to 2f2+1 legitimate regional block consensus response messages including the master node itself are collected, the verification of each replica RA node in the message is clustered into And send a global consensus response message to the global master node Among them, f2 is the number of Byzantine nodes in the regional block at different time periods of the mining process, list g Respond to the ID list of nodes in the regional blocks at different time periods for all excavation processes involved in generating cluster verification; a is the response value of node a;

[0090] (15) Global master node Ra k Collect the global consensus response messages sent by the regional block master nodes in different time periods of the excavation process. When more than or equal to 2f2+1 global consensus response messages are collected, cluster verification in the message is clustered again into And transmit a consensus confirmation message Among them, list a It is the ID list of all RA nodes participating in cluster verification. Commit is the delegated data.

[0091] (16) RA node receives the global master node Ra k After the consensus confirmation message is sent, use the list a Validation of hydrological data encoding in nodes After verification, the consensus trend sequence set of this round will be synchronized to the transmission channel of this node.

[0092] Another exemplary embodiment is based on the optimized regular trend sequence and is applied to a coal mine flood alarm terminal, including:

[0093] First, Ec i Every time t h The data that occurs once in a time period is processed, and the update first predicts the relevant data m for each updated regular trend evolution, and establishes an index index used to identify the specific serial number of the data in the regular trend evolution judgment center storage path;

[0094] Secondly, the hydrological data encoding key is randomly selected from the symmetric file in the hydrological data encoding space. m Decode m to get a hydrological data trend sequence evolution message CT, and follow the custom access strategy A of the current node s , use MPK to decode the symmetric file hydrological data encoding key m Get the key m ←Encrypt(MPK,A s ,key m ), Encrypt() is the compilation function.

[0095] In another exemplary embodiment, in step (6), the RA node predicts the regular trend evolution data updated by the EC node, including:

[0096] On-duty node Ra k After receiving the update, use Ec i Hydrological data coding right Verify the signature and request the regularity trend evolution judgment center to download the data stored in sl j File in j ;

[0097] ECS will determine Ra based on the access policy corresponding to the requested file. k Whether the conditions are met: If the conditions are met, Ra is allowed k Download file j Get M by comparing update with file j In and MD, to determine whether the data source is authentic and valid and whether the data stored in the regularity trend evolution judgment center has been tampered with;

[0098] Decryption key m Get file hydrological data encoding and decoding And use key m The hydrological data trend evolution mapping data m is obtained, and a regularity trend evolution evaluation algorithm is selected to calculate the regularity trend evolution value.

[0099] In another exemplary embodiment, in step (12), verifying the trend sequence set includes:

[0100] Verification of block producing nodes Verify the signature and determine the v in the consensus proposal a , v g , whether the Height is correct; by n+1 The trend sequence collection header is hashed, Block n+1 Is the parent trend sequence set of Block n ; Check Block n+1 Check whether there is any abnormal value assignment for tx in the trend sequence set, and check whether its root hash value is the same as the TX Toot in the trend sequence set header;

[0101] When the master node in the regional block of different time periods of the global or excavation process fails to produce a block within the specified time, the next RA node will produce a block to replace the hydrological data image. The RA node initiates a request to replace the hydrological data image, and other replica nodes verify the request. The RA node collects legal change confirmation messages, and the new RA master node performs the responsibility of generating the prediction and trend sequence set.

[0102] The steps for replacing the hydrological data image by the next replica RA node are as follows:

[0103] 1) The replica RA node initiates a request to replace the hydrological data image:

[0104] The replica RA node discovers the master node Ra k Send hydrographic data image change request when there is a problem To other replica RA nodes, where v a +1 indicates the changed hydrological data image marker. Indicates the ID of the replica RA node that initiates the hydrological data image change request. Indicates the node's verification of the hydrological data image change request, sig agg-p Represents Ra u The cluster verification collected in the previous round of consensus is used to prove the correctness of the trend sequence set with height Height;

[0105] 2) Other replica nodes verify the hydrological data image change request: After receiving the hydrological data image change request, other replica RA nodes verify the request and broadcast the change confirmation message after the verification is passed. in, Indicates the ID of the current replica RA node. Indicates the verification of the change confirmation message by the current replica RA node;

[0106] 3) The replica RA node collects legal change confirmation messages: After the replica RA node collects more than or equal to 2f+1 legal change confirmation messages, the hydrological data image in the regional block in different time periods of the excavation process is changed to f=f2, the global hydrological data image is changed to f=f1, and the hydrological data image marker is updated to v a +1; The new RA master node will be responsible for generating forecast and trend series sets.

[0107] It can be seen from the above embodiments that the present invention utilizes hydrological data trend sequence evolution message strategy attribute decoding to ensure the secure storage of privacy data, while ensuring the data's control over regular trend evolution prediction data.

[0108] Combining the idea of ​​network sharding and clustering verification technology, a two-level judgment mechanism is proposed. First, the consistent hashing algorithm is used to perform regional blocks in different time periods of the network node excavation process. PBFT combined with clustering verification is used within the regional blocks in different time periods of the excavation process and between groups to reach a consensus. This consensus mechanism improves the processing efficiency of regular trend evolution prediction results and reduces communication overhead, while supporting the dynamic changes of nodes. Compared with the existing technology, the present invention can reduce the number of communications required for nodes to reach a consensus from the square level to the linear level, and the throughput is increased by about 2-3 times.

[0109] The present invention takes advantage of the idea of ​​network sharding and proposes a two-level judgment mechanism combined with cluster verification. First, the consistent hash algorithm is used to perform regional blocks in different time periods of the network node excavation process, and cluster verification is used within the regional blocks in different time periods of the excavation process and between groups.

[0110] Among them, the excavation process stage authentication center (IA): plays the role of trend sequence evolution collection organization and attribute organization in this model. Its main responsibility is to make global settings, including system master hydrological data encoding and master hydrological data decoding. In addition, it is also responsible for reviewing the excavation process stage of the enterprise whose water level and water temperature are to be predicted, reducing the waste of resources caused by the regular trend evolution prediction organization for invalid data calculation, and allocating excavation process stage and hydrological data encoding and decoding pairs to it. At the same time, it is responsible for reviewing the qualifications of the regular trend evolution prediction organization, increasing its cost of doing evil by raising the threshold for the regular trend evolution prediction organization to join the chain, and allocating excavation process stage and attribute hydrological data decoding to it;

[0111] Water level and water temperature nodes to be predicted (EC): Alliance members in the heterogeneous excavation process stage, providing the data required for the regular trend evolution prediction process (existing loopholes, regular trend evolution behavior), and responsible for decoding and uploading the collected data to the regular trend evolution judgment center;

[0112] Regularity trend evolution prediction node (RA): responsible for the election of the representative prediction node group and the generation of the regularity trend evolution prediction data trend sequence set;

[0113] Transmission Channel Chain (AC): responsible for storing summary values ​​and important parameters of regularity trend evolution prediction related data;

[0114] Regularity Trend Evolution Judgment Center (ECS): responsible for storing the decoded regularity trend evolution prediction related data.

[0115] Embodiment 3, illustratively, in step S3, the abnormal historical experience of coal mine flood data based on image and hydrological data monitoring includes: placing cameras at underground coal mine excavation working faces, coal mining working faces or other working faces where water seepage accidents may occur, drilling hydrological observation holes on the well to monitor the relevant groundwater status near the mine, and monitoring the water level and water temperature data in the hydrological observation holes;

[0116] The video image data is monitored in real time. When abnormal water flow is detected in the set area in the camera video image, and the water flow duration exceeds the set time threshold or the water flow increase rate exceeds the set threshold, it is judged as data abnormality; and when it is found that the water level or water temperature change of the hydrological observation hole exceeds the set threshold, a flood alarm signal is immediately issued.

[0117] In the simulation comparison between the regular trend sequence formed in step S3 and the abnormal historical experience of coal mine flood data based on image and hydrological data monitoring, the principal component analysis method is used to perform simulation comparison to obtain the credibility score.

[0118] Embodiment 4, as Figure 3 As shown, the coal mine flood alarm system based on image and hydrological data monitoring provided by the embodiment of the present invention includes:

[0119] The initial water level and water temperature data acquisition module 1 is used to collect the initial state of groundwater from the underground excavation working face, coal mining working face and working face where water seepage accidents occur in the coal mine through a non-destructive detector, and obtain the initial water level and water temperature data of the mine groundwater;

[0120] The regular trend sequence acquisition module 2 is used to obtain the change history of water level and water temperature data during the excavation process and present it in the form of image video streams; combined with the geological environment factors that affect the change of water level and water temperature data, the change law of water level and water temperature data in the future excavation process is obtained to form a regular trend sequence and form a scattered point set for abnormal trend points;

[0121] Simulation comparison module 3 is used to simulate and compare the abnormal historical experience of coal mine flood data based on image and hydrological data monitoring to obtain a credibility score. The higher the score, the greater the credibility. Based on the credibility score, the water level and water temperature data during the excavation process are optimized;

[0122] The coal mine flood alarm display module 4 is used for applying to the coal mine flood alarm terminal for visual display based on the optimized regular trend sequence.

[0123] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] Since the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention, their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0125] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment.

[0126] An embodiment of the present invention further provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program.

[0127] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0128] An embodiment of the present invention further provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A coal mine flood alarm method based on image and hydrological data monitoring, characterized in that: The method includes: S1, using non-destructive detectors to collect the initial state of groundwater at the coal mine excavation working face, coal mining working face and working face where water seepage accidents occur, and obtain the initial water level and water temperature data of the mine groundwater; S2, in the process of excavation, obtain the change history of water level and water temperature data and present it in the form of image video stream; combine the geological environment factors that affect the change of water level and water temperature data to obtain the change law of water level and water temperature data in the future excavation process, form a regular trend sequence, and form a scattered point set for abnormal trend points; S3, simulate and compare the historical experience of coal mine flood data anomalies based on image and hydrological data monitoring to obtain a credibility score. The higher the score, the greater the credibility. Based on the credibility score, optimize the water level and water temperature data during the excavation process. S4, based on the optimized regular trend sequence, is applied to the coal mine flood alarm terminal for visualization.

2. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 1 is characterized in that: In step S2, a regular trend sequence is formed, including: S201, the system is initialized, the excavation process stage is allocated, and the regularity trend evolution data of each water level and water temperature node to be predicted is uploaded to the regularity trend evolution judgment center ECS; S202, the water level and water temperature node to be predicted notifies the regularity trend evolution prediction node IA, and the regularity trend evolution prediction node predicts the regularity trend evolution data updated by the water level and water temperature node to be predicted; S203, packaging the regular trend evolution prediction results into a trend sequence set, reaching a consensus within all regular trend evolution prediction nodes based on a two-level judgment mechanism combined with clustering verification, and uploading it through a transmission channel.

3. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 2 is characterized in that: In step 1, the system is initialized and the tunneling process phases are allocated, including: (1) The authentication center IA generates a main hydrological data encoding and decoding pair (MPK, MSK)←Steup(λ), where λ is a mapping parameter. The main hydrological data encoding MPK of the system is public, and the main hydrological data decoding MSK is decrypted and saved by the authentication center IA during the excavation process. Steup(λ) is the generation method. (2) Node EC to be predicted i Submit an application to join the transmission channel chain AC. After the excavation process stage certification center IA reviews the excavation process stage and passes, it will become the node to be predicted EC i Assigning the evolution set of trend sequence of tunneling process stages in, It is the node's globally unique tunneling process stage identifier. Encode and decode the hydrological data of the node; (3) The regular trend evolution prediction node RA submits an application to join the transmission channel chain AC. After the excavation process stage certification center IA passes the qualification review, the regular trend evolution prediction node RA is allocated the excavation process stage trend sequence evolution set And for Ra j The generated node attribute main hydrological data decoding ASK, ASK = KeyGen (MPK, MSK, A p ),in, It is the node's globally unique tunneling process stage identifier. is the node’s hydrological data encoding and decoding pair, A p =(a1, a2…a n ) indicates Ra j The attribute set of the node; KeyGen() is the node attribute main hydrological data interpretation function; (4) Node to be predicted Ec i Combine each hydrological data trend sequence evolution message CT with the index index to form a set M, and attach the access strategy A in the form of hydrological data trend evolution mapping s , symmetric hydrological data encoding key′ after attribute decoding m , the summary value of M is MD, to be predicted node Ec i Node verification of MD Composition file file j After that, it is sent to the regularity trend evolution judgment center Ecs; (5) The regularity trend evolution judgment center Ecs receives the corresponding data and verifies the node to be predicted Ec i , and calculate the hash value of the set M to verify MD, and send the prediction to the node Ec i Returns file file j The storage path sl j ; Node to be predicted Ec i Storage path to sl j After that, the message of data update will be sent to Send to RA node; is the address of the node to be predicted, Verification information of the node to be predicted; (6) The regularity trend evolution prediction node RA predicts the regularity trend evolution data updated by the water level and water temperature node EC to be predicted, and calculates the position of the regional block ID of each regional block in different time periods of the excavation process on the hash ring according to the number of regional blocks X in different time periods of the excavation process, and calculates the position of each RA node on the hash ring by the formula Hash(ID+random); ID is the address, and random is the random position; according to the position of the regularity trend evolution prediction node RA on the hash ring, search for the regional block nodes in different time periods of the excavation process clockwise, and the first regional block node in different time periods of the excavation process found is the regional block in different time periods of the excavation process to which the RA node belongs; (7) Verify whether the number of regular trend evolution prediction nodes RA contained in each area block of different time periods of the excavation process is greater than or equal to four. If not, return to reselect random numbers to calculate the RA node position and re-excavate the area blocks of different time periods of the excavation process; if the verification passes, determine the area blocks of different time periods of the excavation process, and all nodes will retain a list of all nodes and a list of nodes in the area blocks of different time periods of the excavation process; (8) Based on the regional block results of different time periods of the excavation process, set the regional block markers of different time periods of the excavation process for the RA node, and set the hydrological data image markers v of the regional blocks of different time periods of the excavation process. g The number of nodes in the area block at different time periods during the excavation process g , determine the marker p of the main node in the area block of different time periods of the tunneling process in the current hydrological data image g , p g =v g mod|num g |, where mod|| is the positive orientation value determined by the number of nodes in the regional block at different time periods of the excavation process; a global marker is set for the main node in the regional block at different time periods of each excavation process, and the global hydrological data image marker v a The number of blocks in different time periods of the excavation process a , determine the global main node marker p in the current hydrological data image a , p a =v a mod|num a |; (9) Node to be predicted Ec i Send its own update message and the verification sig of the message update The message is sent to the global master node Ra k ; Ra k After receiving the update message, the verification sig of the update message update Verify the signature. If it is illegal, a scattered point set is formed. If it is legal, the regular trend evolution prediction related data is determined. (10) Global master node Ra k After verifying the authenticity and integrity of the regularity trend evolution prediction related data, the prediction is made and recorded in the update request table Utable0, the global master node Ra k Treat prediction node Ec i Recently updated m regular trend evolution prediction algorithms, predict and obtain their respective regular trend evolution values ​​VaR m , and update Ec i The final regular trend evolution value VaR generates a regular trend evolution value change information tx, which is packaged into the trend sequence set; among them, VaR p is the final regular trend evolution value before the change, VaR a is the final regularity trend evolution value after update, ID alg It is the identifier of the regular trend evolution algorithm based on which the current evaluation result is based. Height is the exact height of the trend sequence set to which the current tx belongs. Index is the index. (11) Global master node Ra k Collect the regular trend evolution value change information tx at time t, and compose the collected regular trend evolution value change information tx into Blockheader, then Blockheader= Among them, Prehash is the hash pointer pointing to the previous trend sequence set, and Timestamp represents the block timestamp. Indicates the master node Ra k Verification of the trend sequence set, TX Root represents the root of the Merkle tree composed of all txs in the current trend sequence set; Is the master node address; (12) Global master node Ra k Send consensus proposal <Proposal,V a ,Height,Blockheader,Utable0>to each excavation process in different time periods of the regional block master node, each excavation process in different time periods of the regional block master node receives the global master node Ra k After the consensus proposal is sent, the hydrological data image marker V in the area block at different time periods of the excavation process is attached. g After that, it is broadcasted in the regional blocks at different time periods of the excavation process, and the replica RA nodes in the regional blocks at different time periods of the excavation process verify the trend sequence set; Proposal is a proposal, V a is the regular trend evolution value; (13) After verification is completed, consensus response messages are sent to the blocks in different time periods of the excavation process. Give the master nodes of the regional blocks in different time periods of the excavation process, among which, is the identifier of the current replica RA node, Verify the consensus response message for the current replica RA node; Respond g is the response value; (14) The master node of the regional block in different time periods of the excavation process collects the consensus response messages in the regional block in different time periods of the excavation process. When more than or equal to 2f2+1 legitimate regional block consensus response messages including the master node itself are collected, the verification of each replica RA node in the message is clustered into And send a global consensus response message to the global master node Among them, f2 is the number of Byzantine nodes in the regional block at different time periods of the mining process, list g Respond to the ID list of nodes in the regional blocks at different time periods for all excavation processes involved in generating cluster verification; a is the response value of node a; (15) Global master node Ra k Collect the global consensus response messages sent by the regional block master nodes in different time periods of the excavation process. When more than or equal to 2f2+1 global consensus response messages are collected, the clustering verification in the message is clustered again into And transmit a consensus confirmation message Among them, list a It is the ID list of all RA nodes participating in cluster verification. Commit is the delegated data. (16) RA node receives the global master node Ra k After sending the consensus confirmation message, use the list a Validation of hydrological data encoding in nodes After verification, the consensus trend sequence set of this round will be synchronized to the transmission channel of this node.

4. The coal mine flood alarm method based on image and monitoring according to claim 2 is characterized in that: In step S203, a consensus is reached based on a two-level judgment mechanism combined with cluster verification, including: Calculate the location of the regional block IDs in different time periods of the excavation process, calculate the location of the RA node, determine the regional blocks in different time periods of the excavation process to which the node belongs, and verify the number of RA nodes contained in the regional blocks in different time periods of each excavation process; Set the markers in the area blocks and global markers for different time periods of the excavation process. k Consensus proposals are sent to the master nodes of the regional blocks in different time periods of each excavation process. Consensus is reached within the regional blocks in different time periods of the excavation process. The master nodes of the regional blocks in different time periods of the excavation process send global consensus responses to the global master node. Global consensus is reached to form a regular trend sequence. For nodes that have not reached consensus, abnormal trend points are determined and a scattered point set is formed.

5. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 1 is characterized in that: In step S4, based on the optimized regular trend sequence, it is applied to the coal mine flood alarm terminal, including: First, Ec i Every time t h The data that occurs once in a time period is processed, and the update first predicts the relevant data m for each updated regular trend evolution, and establishes an index index used to identify the specific serial number of the data in the regular trend evolution judgment center storage path; Secondly, the hydrological data encoding key is randomly selected from the symmetric file in the hydrological data encoding space. m Decode m to get a hydrological data trend sequence evolution message CT, and follow the custom access strategy A of the current node s , use MPK to decode the symmetric file hydrological data encoding key m Get the key m ←Encrypt(MPK,A s ,key m ), Encrypt() is the compilation function.

6. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 3 is characterized in that: In step (6), the regularity trend evolution prediction node RA predicts the regularity trend evolution data updated by the water level to be predicted and the water temperature node EC, including: On-duty node Ra k After receiving the update, use Ec i Hydrological data coding right Verify the signature and request the regularity trend evolution judgment center to download the data stored in sl j File in j ; ECS will determine Ra based on the access policy corresponding to the requested file. k Whether the conditions are met: If the conditions are met, Ra is allowed k Download file j Get M by comparing update with file j In and MD, to determine whether the data source is authentic and valid and whether the data stored in the regularity trend evolution judgment center has been tampered with; Decryption key m Get file hydrological data encoding and decoding And use key m The hydrological data trend evolution mapping data m is obtained, and a regularity trend evolution evaluation algorithm is selected to calculate the regularity trend evolution value.

7. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 3 is characterized in that: In step (12), the trend sequence set is verified, including: Verification of block producing nodes Verify the signature and determine the v in the consensus proposal a , v g , whether the Height is correct; by n+1 The trend sequence collection header is hashed, Block n+1 Is the parent trend sequence set of Block n ; Check Block n+1 Check whether there is any abnormal value assignment for tx in the trend sequence set, and check whether its root hash value is the same as the TX Toot in the trend sequence set header; When the master node in the regional block of different time periods of the global or excavation process fails to produce a block within the specified time, the next RA node will produce a block to replace the hydrological data image. The RA node initiates a request to replace the hydrological data image, and the replica RA node verifies the request to replace the hydrological data image. The RA node collects legal change confirmation messages, and the new RA master node performs the responsibility of generating the prediction and trend sequence set.

8. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 7 is characterized in that: The next RA node generates a block to replace the hydrological data image, including: 1) The replica RA node initiates a request to replace the hydrological data image: The replica RA node discovers the master node Ra k Send hydrographic data image change request when there is a problem To the replica RA node, where v a +1 indicates the changed hydrological data image marker. Indicates the ID of the replica RA node that initiates the hydrological data image change request. Indicates the node's verification of the hydrological data image change request, sig agg-p Represents Ra u The cluster verification collected in the previous round of consensus is used to prove the correctness of the trend sequence set with height Height; 2) Replica node verifies the hydrological data image change request: After receiving the hydrological data image change request, the replica RA node verifies the request and broadcasts the change confirmation message after the verification is passed. in, Indicates the ID of the current replica RA node, Indicates the verification of the change confirmation message by the current replica RA node; 3) The replica RA node collects legal change confirmation messages: After the replica RA node collects more than or equal to 2f+1 legal change confirmation messages, the hydrological data image in the regional block in different time periods of the excavation process is changed to f=f2, the global hydrological data image is changed to f=f1, and the hydrological data image marker is updated to v a +1; The new RA master node will be responsible for generating forecast and trend series sets.

9. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 1, characterized in that: In step S3, a simulation comparison is performed based on the historical experience of abnormal coal mine flood data monitored by images and hydrological data, including: placing cameras at the underground excavation working face, coal mining working face and working face where water seepage accidents occur in the coal mine, drilling hydrological observation holes on the well to monitor the groundwater status of the mine, and monitoring the water level and water temperature data in the hydrological observation holes; The video image data is monitored in real time. When abnormal water flow is detected in the set area in the camera video image, and the water flow duration exceeds the set time threshold or the water flow increase rate exceeds the set threshold, it is judged as data abnormality; and when it is found that the water level or water temperature change of the hydrological observation hole exceeds the set threshold, a flood alarm signal is immediately issued.

10. The method for coal mine flood alarm based on image and hydrological data monitoring according to claim 5, characterized in that: The credibility score is obtained by simulation and comparison based on the formation of regular trend sequences and the historical experience of coal mine flood data anomalies based on image and hydrological data monitoring.

Images