Aquaculture water quality monitoring method and system based on Internet of Things

Abstract

The embodiment of the invention relates to the technical field of aquaculture water quality monitoring, and particularly discloses an aquaculture water quality monitoring method and system based on the Internet of Things. According to the embodiment of the invention, based on the Internet of Things technology, wireless control of water quality monitoring is carried out on a target culture area, and multiple pieces of directly fed-back first monitoring data are received; determining a plurality of direct feedback sensors and a plurality of anomaly sensors; monitoring auxiliary planning is carried out, and auxiliary sensors corresponding to the multiple abnormal sensors are selected; performing auxiliary control on the plurality of abnormal sensors, and receiving a plurality of indirectly fed-back second monitoring data; and generating and displaying water quality monitoring information. After wireless control of water quality monitoring is carried out, a plurality of abnormal sensors are identified, monitoring auxiliary planning is carried out, a plurality of auxiliary sensors are selected to carry out auxiliary control, complete monitoring data are received, and then water quality monitoring information is generated and displayed, so that the problems of data loss, abnormal work, incomplete transmission and the like are effectively avoided; and complete monitoring of aquaculture water quality is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of aquaculture water quality monitoring, and particularly relates to an aquaculture water quality monitoring method and system based on the Internet of Things. Background Art

[0002] Aquaculture water quality monitoring is to systematically and continuously detect and analyze the physical, chemical, and biological indicators of water bodies during the aquaculture process to ensure that the water quality is suitable for the healthy growth of aquaculture organisms and at the same time prevent environmental risks. Its core purpose is to maintain the health of water bodies, prevent diseases, and improve aquaculture efficiency.

[0003] In the prior art, in the aquaculture water quality monitoring process based on the Internet of Things, only wireless data transmission is used, which has a certain risk of unreliability. Due to various reasons (such as signal interference, network congestion, battery problems, software configuration, electromagnetic interference, etc.), data loss, abnormal operation, incomplete transmission, etc. are likely to occur, affecting the complete monitoring of aquaculture water quality. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an aquaculture water quality monitoring method and system based on the Internet of Things, aiming to solve the problems raised in the background art.

[0005] To achieve the above purpose, the embodiments of the present invention provide the following technical solutions: An aquaculture water quality monitoring method based on the Internet of Things, the method specifically includes the following steps: Based on the Internet of Things technology, perform wireless control of water quality monitoring on the target aquaculture area according to a preset water quality monitoring period, and receive a plurality of directly feedback first monitoring data; Analyze the plurality of first monitoring data to determine a plurality of directly feedback sensors and a plurality of abnormal sensors; Perform monitoring assistance planning according to the preset sensing interconnection information, and select auxiliary sensors corresponding to the plurality of abnormal sensors from the plurality of directly feedback sensors; Perform auxiliary control on the plurality of abnormal sensors through the plurality of auxiliary sensors, and receive a plurality of indirectly feedback second monitoring data; Process the plurality of first monitoring data and the plurality of second monitoring data to generate and display water quality monitoring information.

[0006] As a further limitation of the technical solution of the embodiments of the present invention, the step of performing wireless control of water quality monitoring on the target aquaculture area according to a preset water quality monitoring period based on the Internet of Things technology and receiving a plurality of directly feedback first monitoring data specifically includes the following steps: Periodically generate wireless monitoring commands according to the preset water quality monitoring period; Based on Internet of Things technology, obtain the sensor communication addresses of multiple monitoring sensors; According to the multiple sensor communication addresses, wirelessly send the wireless monitoring instructions to the multiple monitoring sensors; Perform wireless control for water quality monitoring in the target aquaculture area and receive multiple directly feedback first monitoring data.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the steps of analyzing the multiple first monitoring data to determine multiple directly feedback sensors and multiple abnormal sensors specifically include the following steps: Perform address analysis on the multiple first monitoring data to determine multiple directly feedback addresses; According to the multiple sensor communication addresses, perform address matching on the multiple directly feedback addresses and record the address matching information; Determine multiple directly feedback sensors according to the address matching information; Determine multiple abnormal sensors according to the address matching information.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing monitoring assistance planning according to the preset sensing interconnection information and selecting auxiliary sensors corresponding to the multiple abnormal sensors from the multiple directly feedback sensors specifically include the following steps: Perform sensing interconnection identification according to the preset sensing interconnection information to determine multiple interconnection sensors corresponding to the multiple abnormal sensors; Perform identity matching between the multiple interconnection sensors and the multiple directly feedback sensors and record the identity matching results; Perform monitoring assistance planning according to the identity matching results and select auxiliary sensors corresponding to the multiple abnormal sensors from the multiple directly feedback sensors.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing auxiliary control on the multiple abnormal sensors through the multiple auxiliary sensors and receiving multiple second monitoring data indirectly feedback specifically include the following steps: Generate auxiliary monitoring instructions; Wirelessly send the auxiliary monitoring instructions to the multiple auxiliary sensors; Through the multiple auxiliary sensors, wire-transfer the auxiliary monitoring instructions to the multiple abnormal sensors; Perform auxiliary control on the multiple abnormal sensors and receive multiple second monitoring data indirectly feedback by the multiple auxiliary sensors.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the processing of the multiple first monitoring data and the multiple second monitoring data to generate and display water quality monitoring information specifically includes the following steps: Effectively extract the multiple first monitoring data and the multiple second monitoring data to obtain effective monitoring data; Sort out the effective monitoring data according to a preset data sorting template to generate water quality monitoring information; Obtain the information display address; Transmit and display the water quality monitoring information according to the information display address.

[0011] The aquaculture water quality monitoring system based on the Internet of Things, the system includes a direct feedback receiving unit, a sensor identification and classification unit, a monitoring assistance planning unit, an indirect feedback receiving unit and a monitoring information display unit, wherein: The direct feedback receiving unit is used to wirelessly control the water quality monitoring of the target aquaculture area according to a preset water quality monitoring period based on the Internet of Things technology, and receive multiple directly feedback first monitoring data; The sensor identification and classification unit is used to analyze the multiple first monitoring data to determine multiple directly feedback sensors and multiple abnormal sensors; The monitoring assistance planning unit is used to perform monitoring assistance planning according to preset sensing interconnection information, and select auxiliary sensors corresponding to the multiple abnormal sensors from the multiple directly feedback sensors; The indirect feedback receiving unit is used to assist in controlling the multiple abnormal sensors through the multiple auxiliary sensors and receive multiple indirectly feedback second monitoring data; The monitoring information display unit is used to process the multiple first monitoring data and the multiple second monitoring data to generate and display water quality monitoring information.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the direct feedback receiving unit specifically includes: The wireless instruction generation module is used to periodically generate wireless monitoring instructions according to a preset water quality monitoring period; The communication address acquisition module is used to acquire the sensor communication addresses of multiple monitoring sensors based on the Internet of Things technology; The instruction wireless sending module is used to wirelessly send the wireless monitoring instructions to the multiple monitoring sensors according to the multiple sensor communication addresses; The direct feedback receiving module is used to wirelessly control the water quality monitoring of the target aquaculture area and receive multiple directly feedback first monitoring data.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the sensor identification and classification unit specifically includes: An address analysis module, configured to perform address analysis on the multiple first monitoring data to determine multiple direct feedback addresses; An address matching module, configured to perform address matching on the multiple direct feedback addresses according to the multiple sensor communication addresses, and record the address matching information; A direct feedback sensor determination module, configured to determine multiple direct feedback sensors according to the address matching information; An abnormal sensor determination module, configured to determine multiple abnormal sensors according to the address matching information.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the monitoring information display unit specifically includes: An effective extraction module, configured to effectively extract the multiple first monitoring data and the multiple second monitoring data to obtain effective monitoring data; A data sorting module, configured to sort the effective monitoring data according to a preset data sorting template to generate water quality monitoring information; A display address acquisition module, configured to acquire an information display address; A transmission and display module, configured to transmit and display the water quality monitoring information according to the information display address.

[0015] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, through wireless control of water quality monitoring in a target aquaculture area based on Internet of Things technology, multiple directly feedback first monitoring data are received; multiple direct feedback sensors and multiple abnormal sensors are determined; monitoring auxiliary planning is performed, and auxiliary sensors corresponding to the multiple abnormal sensors are selected; the multiple abnormal sensors are assisted in control, and multiple indirectly feedback second monitoring data are received; water quality monitoring information is generated and displayed. After wireless control of water quality monitoring can be performed, multiple abnormal sensors are identified, monitoring auxiliary planning is performed, multiple auxiliary sensors are selected for auxiliary control, complete monitoring data are received, and then water quality monitoring information is generated and displayed, effectively avoiding problems such as data loss, abnormal operation, and incomplete transmission, and ensuring complete monitoring of aquaculture water quality. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0017] Figure 1 Shows the flowchart of the method provided by the embodiment of the present invention.

[0018] Figure 2 Shows the flowchart of receiving the first monitoring data in the method provided by the embodiment of the present invention.

[0019] Figure 3 Shows the flowchart of determining multiple direct feedback sensors and multiple abnormal sensors in the method provided by the embodiment of the present invention.

[0020] Figure 4 Shows the flowchart of performing monitoring assistance planning in the method provided by the embodiment of the present invention.

[0021] Figure 5 Shows the flowchart of receiving the second monitoring data in the method provided by the embodiment of the present invention.

[0022] Figure 6 Shows the flowchart of displaying water quality monitoring information in the method provided by the embodiment of the present invention.

[0023] Figure 7 Shows the application architecture diagram of the system provided by the embodiment of the present invention.

[0024] Figure 8 Shows the structural block diagram of the direct feedback receiving unit in the system provided by the embodiment of the present invention.

[0025] Figure 9 Shows the structural block diagram of the sensor identification and classification unit in the system provided by the embodiment of the present invention.

[0026] Figure 10 Shows the structural block diagram of the monitoring information display unit in the system provided by the embodiment of the present invention. Detailed implementation manners

[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] It can be understood that in the prior art, in the process of aquaculture water quality monitoring based on the Internet of Things, only through wireless data transmission, there are certain risks of unreliability, and it is easy to cause situations such as data loss, abnormal operation, and incomplete transmission due to various reasons (such as signal interference, network congestion, battery problems, software configuration, electromagnetic interference, etc.), affecting the complete monitoring of aquaculture water quality.

[0029] To solve the above problems, in the embodiments of the present invention, based on the Internet of Things technology, wireless control of water quality monitoring in the target aquaculture area is carried out according to a preset water quality monitoring period, and a plurality of directly feedback first monitoring data are received; the plurality of first monitoring data are analyzed to determine a plurality of directly feedback sensors and a plurality of abnormal sensors; according to the preset sensing interconnection information, monitoring assistance planning is carried out, and auxiliary sensors corresponding to the plurality of abnormal sensors are selected from the plurality of directly feedback sensors; the plurality of abnormal sensors are assisted and controlled through the plurality of auxiliary sensors, and a plurality of indirectly feedback second monitoring data are received; the plurality of first monitoring data and the plurality of second monitoring data are processed to generate and display water quality monitoring information. After wireless control of water quality monitoring can be carried out, a plurality of abnormal sensors are identified, monitoring assistance planning is carried out, a plurality of auxiliary sensors are selected for auxiliary control, complete monitoring data are received, and then water quality monitoring information is generated and displayed, effectively avoiding problems such as data loss, abnormal operation, and incomplete transmission, and ensuring complete monitoring of aquaculture water quality.

[0030] Figure 1 The flowchart of the method provided by the embodiments of the present invention is shown.

[0031] Specifically, for the aquaculture water quality monitoring method based on the Internet of Things, the method specifically includes the following steps: Step S101, based on the Internet of Things technology, wireless control of water quality monitoring in the target aquaculture area is carried out according to a preset water quality monitoring period, and a plurality of directly feedback first monitoring data are received.

[0032] In the embodiments of the present invention, according to a preset water quality monitoring period, wireless monitoring instructions are periodically generated, and then based on the Internet of Things technology, communication address matching of the Internet of Things is carried out to obtain the sensor communication addresses of a plurality of monitoring sensors, and according to the plurality of sensor communication addresses, the wireless monitoring instructions are wirelessly sent to the plurality of monitoring sensors to realize wireless control of water quality monitoring in the target aquaculture area, and then a plurality of directly feedback first monitoring data are received.

[0033] Specifically, Figure 2 The flowchart of receiving the first monitoring data in the method provided by the embodiments of the present invention is shown.

[0034] Among them, in the preferred embodiment provided by the present invention, the wireless control of water quality monitoring in the target aquaculture area based on the Internet of Things technology and receiving a plurality of directly feedback first monitoring data specifically includes the following steps: Step S1011, wireless monitoring instructions are periodically generated according to a preset water quality monitoring period; Step S1012, based on the Internet of Things technology, the sensor communication addresses of a plurality of monitoring sensors are obtained; Step S1013: wirelessly send the wireless monitoring instruction to multiple said monitoring sensors according to multiple said sensor communication addresses; Step S1014: perform wireless control on water quality monitoring in the target aquaculture area and receive multiple directly feedback first monitoring data.

[0035] Specifically, based on the Internet of Things technology, obtain the sensor communication addresses of multiple monitoring sensors. The specific steps are as follows: Use the RSSI fingerprint triangulation algorithm to calculate the distances between monitoring sensors through a signal attenuation model, and then obtain the adjacency relationship matrix of the monitoring sensors; Implement the precise time synchronization protocol and obtain the communication link information between monitoring sensors through delay jitter measurement; Among them, the physical deployment relationship includes the positions of the monitoring sensors, and the communication link information includes the connection methods between the detection sensors; Construct a topology graph based on the adjacency relationship matrix and communication link information of the monitoring sensors; Analyze the node attributes and edge relationships in the topology graph data, and construct a network graph based on the node attributes and edge relationships in the topology graph; Perform node activity analysis on the structure of the network graph and generate an active node set; Receive the active node set and convert the active node set into a standardized address string set through a protocol conversion rule library; Use regular expressions to filter the standardized communication address string set to obtain a filtered address list; Convert the IPv6 addresses in the filtered address list into 12-bit compact encodings to obtain a communication address set; Perform hash value operations on the communication addresses in the communication address set using SHA-256 to obtain a 64-bit hash digest set; Perform a superimposed GeoHash algorithm on the 64-bit hash digest set to generate a geographical hash value, generate a composite hash value with geographical tags, then generate a new salt value every certain time, and finally obtain a unique hash value set; Establish a Redis index database through the unique hash value set and generate a hash fingerprint index table; Set a distance threshold and screen the hash fingerprint search fingerprint index table through the distance threshold to obtain a spatially deduplicated result set; Apply the allowed maintenance period to the spatially deduplicated result set to obtain a list of valid communication addresses, where the list of valid communication addresses includes the communication addresses of the monitoring sensors with timestamps; Output the list of valid communication addresses as the sensor communication addresses of the monitoring sensors.

[0036] Further, the aquaculture water quality monitoring method based on the Internet of Things further includes the following steps: Step S102: Analyze the multiple first monitoring data to determine multiple direct feedback sensors and multiple abnormal sensors.

[0037] In the embodiment of the present invention, by analyzing the addresses of the multiple first monitoring data, multiple direct feedback addresses are determined. Then, according to the communication addresses of the multiple sensors, address matching is performed on the multiple direct feedback addresses, and the address matching information is recorded. Furthermore, according to the address matching information, multiple direct feedback sensors that have completed normal feedback monitoring and feedback are determined from the multiple monitoring sensors, and multiple abnormal sensors with abnormal feedback monitoring are determined.

[0038] It can be understood that due to reasons such as signal interference, network congestion, battery problems, software configuration, and / or electromagnetic interference, the wireless communication of the sensors is abnormal.

[0039] Specifically, Figure 3 The flowchart shows the method for determining multiple direct feedback sensors and multiple abnormal sensors provided in the embodiment of the present invention.

[0040] Among them, in the preferred embodiment provided by the present invention, the step of analyzing the multiple first monitoring data to determine multiple direct feedback sensors and multiple abnormal sensors specifically includes the following steps: Step S1021: Analyze the addresses of the multiple first monitoring data to determine multiple direct feedback addresses; Step S1022: Perform address matching on the multiple direct feedback addresses according to the communication addresses of the multiple sensors, and record the address matching information; Step S1023: Determine multiple direct feedback sensors according to the address matching information; Step S1024: Determine multiple abnormal sensors according to the address matching information.

[0041] Specifically, the steps of performing address matching on the multiple direct feedback addresses according to the communication addresses of the multiple sensors and recording the address matching information are as follows: Generate a sensor communication address set based on the communication addresses of the multiple sensors; Generate a direct feedback address set based on the multiple direct feedback addresses; Perform standardization processing on the address strings in the sensor communication address set and the direct feedback address set to obtain standardized sensor addresses and standardized direct feedback addresses; Based on the standardized sensor addresses and standardized direct feedback addresses, traverse each combination pair of the standardized sensor addresses and standardized direct feedback addresses; For each pair of combinations, a text difference measurement method is used to obtain a similarity value, and a similarity matrix of addresses is generated; Based on historical data, obtain the communication times of the sensors and the response times of the feedback addresses; Divide the communication times of the sensors by the response times of the feedback addresses to obtain a frequency ratio, and generate a frequency association matrix; Assign matrix weights to the similarity matrix and the frequency association matrix, and obtain a comprehensive weight value; Based on the similarity matrix, the frequency association matrix, and the comprehensive weight value, perform a weighted summation calculation to obtain a matching degree matrix including confidence levels; Set a matrix determination threshold, and use the matrix determination threshold to screen the matching degree matrix including confidence levels to obtain a final matching degree matrix, where the final matching degree matrix contains address matching information.

[0042] Furthermore, the aquaculture water quality monitoring method based on the Internet of Things further includes the following steps: Step S103, according to the preset sensing interconnection information, perform monitoring assistance planning, and select auxiliary sensors corresponding to multiple abnormal sensors from multiple direct feedback sensors.

[0043] In the embodiment of the present invention, according to the preset sensing interconnection information, perform sensing interconnection identification, determine multiple interconnection sensors that can be sensed and interconnected with multiple abnormal sensors from multiple monitoring sensors, then perform identity matching between the multiple interconnection sensors and the multiple direct feedback sensors, record the identity matching results, and further perform monitoring assistance planning according to the identity matching results, and select auxiliary sensors corresponding to multiple abnormal sensors from multiple direct feedback sensors. Specifically, multiple auxiliary sensors belong to both interconnection sensors and direct feedback sensors.

[0044] Specifically, Figure 4 Shows a flowchart of performing monitoring assistance planning in the method provided by the embodiment of the present invention.

[0045] Among them, in the preferred embodiment provided by the present invention, the step of performing monitoring assistance planning according to the preset sensing interconnection information and selecting auxiliary sensors corresponding to multiple abnormal sensors from multiple direct feedback sensors specifically includes the following steps: Step S1031, according to the preset sensing interconnection information, perform sensing interconnection identification to determine multiple interconnection sensors corresponding to multiple abnormal sensors; Step S1032, perform identity matching between multiple interconnection sensors and multiple direct feedback sensors, and record the identity matching results; Step S1033: According to the identity matching result, perform monitoring assistance planning, and select auxiliary sensors corresponding to multiple abnormal sensors from multiple direct feedback sensors.

[0046] Specifically, according to the identity matching result, perform monitoring assistance planning, and select auxiliary sensors corresponding to multiple abnormal sensors from multiple direct feedback sensors. The specific steps are as follows: Generate a list of abnormal sensors, where the list of abnormal sensors includes the position coordinates of the abnormal sensors, the device parameters of the abnormal sensors, and the fault codes of the abnormal sensors; Generate a list of auxiliary sensors, where the list of auxiliary sensors includes the device parameters of the auxiliary sensors and the historical working records of the auxiliary sensors; Perform data cleaning on the list of abnormal sensors to obtain a cleaned list of abnormal sensors; Based on the cleaned list of abnormal sensors and the list of auxiliary sensors, establish an interconnected relationship mapping list and a historical collaboration scoring matrix; Perform normalization processing on the interconnected relationship mapping list to obtain a standardized interconnected relationship list; Based on the standardized interconnected relationship list, use a path analysis algorithm to obtain the optimal communication path from each auxiliary sensor to the abnormal sensor; Generate an original topological correlation degree value according to the hop count and signal attenuation coefficient of the optimal communication path from the auxiliary sensor to the abnormal sensor; Perform dynamic correction on the original topological correlation degree value to generate a dynamic topological correlation degree matrix; Perform dynamic weight adjustment on the dynamic topological correlation degree matrix to obtain a dynamic weight allocation list; Extract the dynamic weight coefficient from the dynamic weight allocation list and extract the dynamic topological correlation degree value from the dynamic topological correlation degree matrix; Perform weighted summation of the dynamic topological correlation degree value and the dynamic weight coefficient to obtain a dynamic correlation value; Obtain environmental parameters according to the local environmental state, and obtain a resource evaluation coefficient according to the device parameters of the auxiliary sensor and the abnormal sensor; Perform non-linear combination of the environmental parameters and the resource evaluation coefficient to obtain a resource environment impact value; Divide the dynamic correlation value by the resource environment impact value to obtain a utility score, and then perform normalization processing on the utility score to generate a utility score list with confidence; Preset a safety threshold, and screen the utility score list with confidence through the safety threshold to obtain a valid candidate list that passes the verification; Based on the valid candidate list that has passed the verification, the auxiliary sensors are sorted in descending order of numerical value, and the N+1 redundancy strategy is adopted to select the auxiliary sensors, and finally the most optimal auxiliary sensor list is generated.

[0047] Further, the aquaculture water quality monitoring method based on the Internet of Things further includes the following steps: Step S104, through the multiple auxiliary sensors, perform auxiliary control on the multiple abnormal sensors, and receive the second monitoring data indirectly fed back by the multiple sensors.

[0048] In the embodiment of the present invention, an auxiliary monitoring instruction is generated, and the auxiliary monitoring instruction is wirelessly sent to the multiple auxiliary sensors. Through the multiple auxiliary sensors, the auxiliary monitoring instruction is wired and transferred to the multiple abnormal sensors. Then, through the multiple auxiliary sensors, the multiple abnormal sensors are assisted and controlled. After the multiple abnormal sensors perform water quality monitoring and wire feedback transmission to the multiple auxiliary sensors, the multiple second monitoring data wirelessly transferred by the multiple auxiliary sensors are received.

[0049] Specifically, Figure 5 The flowchart of receiving the second monitoring data in the method provided by the embodiment of the present invention is shown.

[0050] Among them, in the preferred embodiment provided by the present invention, the step of performing auxiliary control on the multiple abnormal sensors through the multiple auxiliary sensors and receiving the second monitoring data indirectly fed back by the multiple auxiliary sensors specifically includes the following steps: Step S1041, generate an auxiliary monitoring instruction; Step S1042, wirelessly send the auxiliary monitoring instruction to the multiple auxiliary sensors; Step S1043, through the multiple auxiliary sensors, wire-transfer the auxiliary monitoring instruction to the multiple abnormal sensors; Step S1044, perform auxiliary control on the multiple abnormal sensors, and receive the multiple second monitoring data indirectly fed back by the multiple auxiliary sensors.

[0051] Further, the aquaculture water quality monitoring method based on the Internet of Things further includes the following steps: Step S105, process the multiple first monitoring data and the multiple second monitoring data to generate and display water quality monitoring information.

[0052] In the embodiment of the present invention, by effectively extracting the multiple first monitoring data and the multiple second monitoring data, the effective monitoring data is obtained. Then, according to the preset data sorting template, the content of the effective monitoring data is identified and sorted to generate water quality monitoring information, and the information display address is obtained. Furthermore, according to the information display address, the water quality monitoring information is transmitted and displayed.

[0053] Specifically, Figure 6 The flowchart showing the water quality monitoring information in the method provided by the embodiment of the present invention is shown.

[0054] Among them, in the preferred embodiment provided by the present invention, the processing of the plurality of first monitoring data and the plurality of second monitoring data to generate and display water quality monitoring information specifically includes the following steps: Step S1051, effectively extract the plurality of first monitoring data and the plurality of second monitoring data to obtain effective monitoring data; Step S1052, organize the effective monitoring data according to a preset data organization template to generate water quality monitoring information; Step S1053, obtain the information display address; Step S1054, transmit and display the water quality monitoring information according to the information display address.

[0055] Specifically, organizing the effective monitoring data according to a preset data organization template to generate water quality monitoring information, the specific steps are as follows: Obtain the first water quality monitoring value and the second water quality monitoring value from the first monitoring data and the second monitoring data; Obtain the monitoring points through the first monitoring data and the second monitoring data, and each monitoring point corresponds to a set of first water quality monitoring values and second water quality monitoring values; Assign the first monitoring weight coefficient and the second monitoring weight coefficient to the first water quality monitoring value and the second water quality monitoring value respectively; Obtain the timestamp and spatial coordinates of each monitoring point; Calculate the time decay factor based on the timestamp; Calculate the mean of the spatial coordinates and the standard deviation of the spatial coordinates based on the spatial coordinates of the monitoring points, and obtain the spatial influence factor by using the mean of the spatial coordinates and the standard deviation of the spatial coordinates; For each monitoring point, multiply the first water quality monitoring value and the second water quality monitoring value by the first monitoring weight coefficient and the second monitoring weight coefficient respectively to obtain the weighted water quality detection value; Divide the weighted water quality monitoring value by the time decay factor to obtain the water quality detection value after time decay processing; Add up all the water quality detection values after time decay processing to obtain the water quality detection value after preliminary processing; Multiply the water quality detection value after preliminary processing by the spatial influence factor to obtain the final water quality monitoring value, and display the final water quality monitoring value as the water quality monitoring information.

[0056] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0057] Among them, in another preferred embodiment provided by the present invention, the aquaculture water quality monitoring system based on the Internet of Things includes: The direct feedback receiving unit 101 is used to wirelessly control the water quality monitoring of the target aquaculture area according to a preset water quality monitoring period based on the Internet of Things technology, and receive a plurality of directly feedback first monitoring data.

[0058] In the embodiment of the present invention, the direct feedback receiving unit 101 periodically generates a wireless monitoring instruction according to a preset water quality monitoring period, and then based on the Internet of Things technology, performs communication address matching of the Internet of Things, obtains the sensor communication addresses of a plurality of monitoring sensors, and wirelessly sends the wireless monitoring instruction to the plurality of monitoring sensors according to the plurality of sensor communication addresses, so as to realize the wireless control of the water quality monitoring of the target aquaculture area, and then receive a plurality of directly feedback first monitoring data.

[0059] Specifically, Figure 8 The block diagram of the direct feedback receiving unit 101 in the system provided by the embodiment of the present invention is shown.

[0060] Among them, in the preferred embodiment provided by the present invention, the direct feedback receiving unit 101 specifically includes: The wireless instruction generation module 1011 is used to periodically generate a wireless monitoring instruction according to a preset water quality monitoring period; The communication address acquisition module 1012 is used to obtain the sensor communication addresses of a plurality of monitoring sensors based on the Internet of Things technology; The instruction wireless sending module 1013 is used to wirelessly send the wireless monitoring instruction to the plurality of monitoring sensors according to the plurality of sensor communication addresses; The direct feedback receiving module 1014 is used to wirelessly control the water quality monitoring of the target aquaculture area and receive a plurality of directly feedback first monitoring data.

[0061] Furthermore, the aquaculture water quality monitoring system based on the Internet of Things further includes: The sensor identification and classification unit 102 is used to analyze a plurality of the first monitoring data to determine a plurality of directly feedback sensors and a plurality of abnormal sensors.

[0062] In the embodiment of the present invention, the sensor identification and classification unit 102 determines a plurality of direct feedback addresses by analyzing the addresses of a plurality of first monitoring data, and then performs address matching on the plurality of direct feedback addresses according to the plurality of sensor communication addresses, records the address matching information, and then determines a plurality of directly feedback sensors that have completed normal feedback monitoring and feedback from the plurality of monitoring sensors according to the address matching information, and determines a plurality of abnormal sensors with abnormal feedback monitoring.

[0063] Specifically, Figure 9 The block diagram of the sensor identification and classification unit 102 in the system provided by the embodiment of the present invention is shown.

[0064] Among them, in the preferred embodiment provided by the present invention, the sensor identification and classification unit 102 specifically includes: An address analysis module 1021, configured to perform address analysis on the multiple first monitoring data to determine multiple direct feedback addresses; An address matching module 1022, configured to perform address matching on the multiple direct feedback addresses according to the multiple sensor communication addresses, and record address matching information; A direct feedback sensor determination module 1023, configured to determine multiple direct feedback sensors according to the address matching information; An abnormal sensor determination module 1024, configured to determine multiple abnormal sensors according to the address matching information.

[0065] Furthermore, the aquaculture water quality monitoring system based on the Internet of Things further includes: A monitoring assistance planning unit 103, configured to perform monitoring assistance planning according to preset sensing interconnection information, and select auxiliary sensors corresponding to the multiple abnormal sensors from the multiple direct feedback sensors.

[0066] In the embodiment of the present invention, the monitoring assistance planning unit 103 performs sensing interconnection identification according to preset sensing interconnection information, determines multiple interconnection sensors that can perform sensing interconnection with the multiple abnormal sensors from multiple monitoring sensors, then performs identity matching on the multiple interconnection sensors and the multiple direct feedback sensors, records the identity matching result, and further performs monitoring assistance planning according to the identity matching result, and selects auxiliary sensors corresponding to the multiple abnormal sensors from the multiple direct feedback sensors. Specifically, the multiple auxiliary sensors belong to both the interconnection sensors and the direct feedback sensors.

[0067] An indirect feedback receiving unit 104, configured to perform auxiliary control on the multiple abnormal sensors through the multiple auxiliary sensors and receive multiple second monitoring data with indirect feedback.

[0068] In the embodiment of the present invention, the indirect feedback receiving unit 104 generates an auxiliary monitoring instruction, wirelessly sends the auxiliary monitoring instruction to the multiple auxiliary sensors, and the multiple auxiliary sensors wire-transfer the auxiliary monitoring instruction to the multiple abnormal sensors, and then performs auxiliary control on the multiple abnormal sensors through the multiple auxiliary sensors. After the multiple abnormal sensors perform water quality monitoring and wire-feedback transmission to the multiple auxiliary sensors, the indirect feedback receiving unit 104 receives the multiple second monitoring data wirelessly transferred by the multiple auxiliary sensors.

[0069] The monitoring information display unit 105 is configured to process the multiple first monitoring data and the multiple second monitoring data, and generate and display water quality monitoring information.

[0070] In an embodiment of the present invention, the monitoring information display unit 105 effectively extracts the multiple first monitoring data and the multiple second monitoring data to obtain effective monitoring data, then identifies and organizes the content of the effective monitoring data according to a preset data organization template to generate water quality monitoring information, and obtains an information display address, and then transmits and displays the water quality monitoring information according to the information display address.

[0071] Specifically, Figure 10 The structural block diagram of the monitoring information display unit 105 in the system provided by the embodiment of the present invention is shown.

[0072] Among them, in a preferred embodiment provided by the present invention, the monitoring information display unit 105 specifically includes: An effective extraction module 1051, configured to effectively extract the multiple first monitoring data and the multiple second monitoring data to obtain effective monitoring data; A data organization module 1052, configured to organize the effective monitoring data according to a preset data organization template to generate water quality monitoring information; A display address acquisition module 1053, configured to acquire an information display address; A transmission and display module 1054, configured to transmit and display the water quality monitoring information according to the information display address.

[0073] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0075] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0076] The above embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0077] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. Aquaculture water quality monitoring method based on the Internet of Things, characterized in that: The method specifically comprises the following steps: Based on the Internet of Things technology, wireless control of water quality monitoring is performed on the target breeding area according to the preset water quality monitoring cycle, and multiple first monitoring data with direct feedback are received; Analyze the plurality of the first monitoring data to determine a plurality of direct feedback sensors and a plurality of abnormal sensors; According to the preset sensor interconnection information, monitoring auxiliary planning is performed, and auxiliary sensors corresponding to the plurality of abnormal sensors are selected from the plurality of direct feedback sensors; Perform auxiliary control on the plurality of abnormal sensors through the plurality of auxiliary sensors, and receive a plurality of indirect feedback second monitoring data; The plurality of the first monitoring data and the plurality of the second monitoring data are processed to generate and display water quality monitoring information.

2. The aquaculture water quality monitoring method based on the Internet of Things according to claim 1 is characterized in that: The method of wirelessly controlling water quality monitoring of a target breeding area based on the Internet of Things technology according to a preset water quality monitoring cycle and receiving a plurality of first monitoring data directly fed back specifically includes the following steps: Generate wireless monitoring instructions periodically according to the preset water quality monitoring cycle; Based on the Internet of Things technology, obtain the sensor communication addresses of multiple monitoring sensors; wirelessly sending the wireless monitoring instruction to the plurality of monitoring sensors according to the communication addresses of the plurality of sensors; Wireless control of water quality monitoring of the target breeding area is performed to receive a plurality of first monitoring data of direct feedback.

3. The aquaculture water quality monitoring method based on the Internet of Things according to claim 2 is characterized in that: Based on the Internet of Things technology, obtain the sensor communication addresses of multiple monitoring sensors. The specific steps are as follows: Using the RSSI fingerprint triangulation algorithm, the distance between monitoring sensors is calculated through the signal attenuation model, and then the adjacency matrix of the monitoring sensors is obtained; Implement a precise time synchronization protocol and obtain the communication link information between monitoring sensors through delay jitter measurement; Among them, the physical deployment relationship includes the location of the monitoring sensors, and the communication link information includes the link mode between the detection sensors; Construct a topology map based on the adjacency matrix of monitoring sensors and communication link information; Parse the node attributes and edge relationships in the topology data, and build a network graph based on the node attributes and edge relationships in the topology; Perform node activity analysis on the structure of the network graph and generate an active node set; Receive the active node set, and convert the active point set into a standardized address string set through the protocol conversion rule base; Using regular expressions to filter the standardized communication address character string set, obtaining a filtered address list; Convert the IPv6 addresses in the filtered address list into 12-bit simplified codes to obtain a communication address set; Perform hash value calculation on the communication addresses in the communication address set using SHA-256 to obtain a 64-bit hash summary set; The 64-bit hash summary set is superimposed with the GeoHash algorithm to generate a geographic hash value, a composite hash value with a geographic tag, and then a new salt value is generated every certain period of time to finally obtain a unique hash value set; Establish a Redis index database through a unique hash value set and generate a hash fingerprint index table; Set a distance threshold and filter the hash fingerprint search fingerprint index table by the distance threshold to obtain a spatial deduplication result set; Allowing the maintenance period to act on the spatial deduplication result set to obtain a valid communication address list, wherein the valid communication address list includes the communication address of the monitoring sensor with a timestamp; A list of valid communication addresses is output as sensor communication addresses of the monitoring sensor.

4. The aquaculture water quality monitoring method based on the Internet of Things according to claim 3 is characterized in that: The analyzing the plurality of the first monitoring data to determine the plurality of direct feedback sensors and the plurality of abnormal sensors specifically comprises the following steps: Performing address analysis on the plurality of the first monitoring data to determine a plurality of direct feedback addresses; According to the plurality of sensor communication addresses, performing address matching on the plurality of direct feedback addresses, and recording address matching information; Determining a plurality of direct feedback sensors according to the address matching information; According to the address matching information, a plurality of abnormal sensors are determined.

5. The aquaculture water quality monitoring method based on the Internet of Things according to claim 4 is characterized in that: According to the multiple sensor communication addresses, address matching is performed on the multiple direct feedback addresses, and address matching information is recorded. The specific steps are as follows: generating a sensor communication address set based on a plurality of sensor communication addresses; generating a direct feedback address set based on the plurality of direct feedback addresses; Standardizing the address character strings in the sensor communication address set and the direct feedback address set to obtain a standardized sensor address and a standardized direct feedback address; Based on the standardized sensor address and the standardized direct feedback address, traverse each combination pair of the standardized sensor address and the standardized direct feedback address; For each pair of combinations, the text difference measurement method is used to obtain the similarity value and generate the address similarity matrix; Obtain the number of sensor communications and the number of responses to the feedback address based on historical data; Divide the number of sensor communications by the number of responses to the feedback address, the frequency ratio, and generate a frequency correlation matrix; Assign matrix weights to the similarity matrix and the frequency correlation matrix, and obtain the comprehensive weight value; Based on the similarity matrix, frequency association matrix and comprehensive weight value, a weighted sum calculation is performed to obtain a matching degree matrix including confidence; A matrix determination threshold is set, and the matrix determination threshold is used to screen the matching degree matrix including the confidence degree to obtain a final matching degree matrix, wherein the final matching degree matrix includes the address matching information.

6. The aquaculture water quality monitoring method based on the Internet of Things according to claim 5 is characterized in that: The step of performing monitoring auxiliary planning according to the preset sensor interconnection information and selecting auxiliary sensors corresponding to the plurality of abnormal sensors from the plurality of direct feedback sensors specifically comprises the following steps: According to the preset sensor interconnection information, sensor interconnection identification is performed to determine a plurality of interconnected sensors corresponding to the plurality of abnormal sensors; Performing identity matching on the plurality of interconnected sensors and the plurality of direct feedback sensors, and recording identity matching results; According to the identity matching result, monitoring auxiliary planning is performed, and auxiliary sensors corresponding to the multiple abnormal sensors are selected from the multiple direct feedback sensors.

7. The aquaculture water quality monitoring method based on the Internet of Things according to claim 6 is characterized in that: According to the identity matching result, monitoring auxiliary planning is performed, and auxiliary sensors corresponding to the multiple abnormal sensors are selected from the multiple direct feedback sensors. The specific steps are as follows: Generate an abnormal sensor list, wherein the abnormal sensor list includes location coordinates of the abnormal sensor, device parameters of the abnormal sensor, and a fault code of the abnormal sensor; Generate an auxiliary sensor list, wherein the auxiliary sensor list includes device parameters of the auxiliary sensors and historical working records of the auxiliary sensors; Perform data cleaning on the abnormal sensor list to obtain a cleaned abnormal sensor list; Establish an interconnection relationship mapping list and a historical collaboration scoring matrix based on the cleaned abnormal sensor list and the auxiliary sensor list; Normalizing the interconnection relationship mapping list to obtain a standardized interconnection relationship list; Based on the standardized interconnection relationship list, the path analysis algorithm is used to obtain the optimal communication path from each auxiliary sensor to the abnormal sensor; Generate an original topology association value according to the optimal communication path hop number and signal attenuation coefficient from the auxiliary sensor to the abnormal sensor; Dynamically modify the original topological correlation value to generate a dynamic topological correlation matrix; Dynamically adjust the weight of the dynamic topological correlation matrix to obtain a dynamic weight distribution list; Extract the dynamic weight coefficient from the dynamic weight allocation list, and extract the dynamic topology association value from the dynamic topology association matrix; Perform weighted summation of the dynamic topological association value and the dynamic weight coefficient to obtain a dynamic association value; Obtain environmental parameters based on the local environmental status, and obtain resource assessment coefficients based on the equipment parameters of auxiliary sensors and abnormal sensors; The environmental parameters and resource assessment coefficients are nonlinearly combined to obtain the resource and environmental impact value; The dynamic correlation value is divided by the resource and environmental impact value to obtain the utility score, and then the utility score is normalized to generate a utility score list with confidence; Preset a safety threshold, and use the safety threshold to filter the utility score list with confidence to obtain a valid candidate list that has passed the verification; Based on the verified valid candidate list, the auxiliary sensors are sorted from high to low according to the numerical value, and the auxiliary sensors are selected using the N+1 redundancy strategy, and finally the most preferred auxiliary sensor list is generated.

8. The aquaculture water quality monitoring method based on the Internet of Things according to claim 7 is characterized in that: The auxiliary control of the plurality of abnormal sensors by the plurality of auxiliary sensors and receiving the second monitoring data of the plurality of indirect feedbacks specifically comprises the following steps: Generate auxiliary monitoring instructions; wirelessly sending the auxiliary monitoring instruction to the plurality of auxiliary sensors; The auxiliary monitoring instruction is transmitted to the plurality of abnormal sensors by wire through the plurality of auxiliary sensors; Auxiliary control is performed on the plurality of abnormal sensors, and a plurality of second monitoring data indirectly fed back by the plurality of auxiliary sensors are received.

9. The aquaculture water quality monitoring method based on the Internet of Things according to claim 8, characterized in that: The processing of the plurality of the first monitoring data and the plurality of the second monitoring data to generate and display the water quality monitoring information specifically comprises the following steps: Effectively extracting a plurality of the first monitoring data and a plurality of the second monitoring data to obtain effective monitoring data; Arrange the effective monitoring data according to the preset data arrangement template to generate water quality monitoring information; Get the information display address; According to the information display address, the water quality monitoring information is transmitted and displayed; The effective monitoring data is sorted according to the preset data sorting template to generate water quality monitoring information. The specific steps are as follows: Acquire a first water quality monitoring value and a second water quality monitoring value from the first monitoring data and the second monitoring data; Acquire monitoring points through the first monitoring data and the second monitoring data, each monitoring point corresponding to a set of first water quality monitoring values ​​and second water quality monitoring values; Assign a first monitoring weight coefficient and a second monitoring weight coefficient to the first water quality monitoring value and the second water quality monitoring value respectively; Get the timestamp and spatial coordinates of each monitoring point; Calculate a time decay factor based on the timestamp; The mean value of the spatial coordinates and the standard deviation of the spatial coordinates are calculated based on the spatial coordinates of the monitoring points, and the spatial influence factor is obtained by using the mean value of the spatial coordinates and the standard deviation of the spatial coordinates; For each monitoring point, the first water quality monitoring value and the second water quality monitoring value are multiplied by the first monitoring weight coefficient and the second monitoring weight coefficient respectively to obtain a weighted water quality detection value; Divide the weighted water quality monitoring value by the time decay factor to obtain the water quality detection value after time decay processing; Add up all the water quality test values ​​that have been processed by time decay to obtain the water quality test value that has been processed initially; The preliminary processed water quality detection value is multiplied by the spatial impact factor to obtain the final water quality monitoring value, and the final water quality monitoring value is displayed as water quality monitoring information.

10. The aquaculture water quality monitoring system based on the Internet of Things is characterized by: The system applies the aquaculture water quality monitoring method based on the Internet of Things as described in any one of claims 1 to 9 above, and the system comprises: A direct feedback receiving unit is used to wirelessly control water quality monitoring of a target breeding area based on the Internet of Things technology according to a preset water quality monitoring cycle, and receive a plurality of first monitoring data of direct feedback; A sensor identification and classification unit, used for analyzing the plurality of the first monitoring data to determine a plurality of direct feedback sensors and a plurality of abnormal sensors; A monitoring auxiliary planning unit, used to perform monitoring auxiliary planning according to preset sensor interconnection information, and select auxiliary sensors corresponding to the plurality of abnormal sensors from the plurality of direct feedback sensors; an indirect feedback receiving unit, configured to perform auxiliary control on the plurality of abnormal sensors through the plurality of auxiliary sensors, and receive a plurality of indirect feedback second monitoring data; The monitoring information display unit is used to process the plurality of the first monitoring data and the plurality of the second monitoring data to generate and display water quality monitoring information.

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