Remote operation and maintenance system of water quality monitoring equipment and method thereof

Abstract

The invention relates to the technical field of remote supervision and control, in particular to a remote operation and maintenance system and method for water quality monitoring equipment, and the system comprises a communication link optimization module, a state perception and diagnosis module, an instruction adaptation management module, an energy consumption dynamic regulation and control module and a maintenance path planning module. According to the invention, through deep analysis of communication channels and environmental interference, link adaptive optimization is realized, stable data transmission is guaranteed, equipment state deviation and change rate are accurately sensed, error accumulation is quantized, diagnosis accuracy is improved, faults are prevented, instruction matching and issuing sequences are optimized in combination with operation laws and instruction priorities, efficient execution of instructions is guaranteed, and the method has the advantages of high efficiency and high reliability. Energy consumption distribution of equipment and a communication link is dynamically regulated and controlled, according to the demand proportion, energy efficiency is improved, the service life of the equipment is prolonged, equipment operation and instruction execution are monitored in real time, maintenance strategies and resources are dynamically adjusted, system optimization and intelligent maintenance are achieved, and smooth completion of tasks is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote supervisory control, in particular to a remote operation and maintenance system of water quality monitoring equipment and a method thereof. BACKGROUND

[0002] The technical field of remote control belongs to the cross field of automatic control and communication technology, mainly involving the systematic technical system of collecting the state of remote equipment, transmitting parameters and issuing operation instructions through wired or wireless communication. The core matters of this technical field include remote signal transmission, equipment state monitoring, control command response and communication protocol management, relying on sensing detection devices, control terminals and communication networks to realize remote interaction and instruction control of data, and the remote control technology is widely used in industrial equipment operation and maintenance, environmental monitoring, energy management and intelligent manufacturing, etc. and is an important foundation for realizing intelligent management of equipment.

[0003] Among them, the remote operation and maintenance system of the traditional water quality monitoring equipment refers to the system for operation and maintenance management of distributed water quality monitoring equipment based on remote communication technology, which mainly aims at remotely acquiring, controlling and maintaining the running state and sampling data of the water quality monitoring equipment. The traditional remote operation and maintenance system of water quality monitoring equipment uses water quality sensors installed on site to collect water body parameters, transmits the measurement signals to the remote monitoring center in the form of digital data through the communication module, and then realizes the switching of equipment working mode, the issuance of calibration commands and the updating of data record through control instructions. Such system generally uses wireless communication links such as cellular network or narrowband Internet of Things for information transmission, and performs state query and operation and maintenance tasks of the monitoring device through remote terminals.

[0004] The existing water quality monitoring system lacks dynamic management of communication link, cannot intelligently perceive channel changes and adaptively adjust in the face of complex environmental interference, is easy to cause unstable data transmission and information loss, affects the monitoring reliability and real-time performance, the equipment state monitoring focuses on periodic collection and cannot deeply analyze the state deviation accumulation and change rate, causes delayed fault diagnosis and maintenance response, increases the operation risk and unplanned downtime, in addition, the instruction execution mechanism is relatively fixed and does not fully consider the equipment load and instruction priority, which is easy to cause uneven resource allocation, delay or conflict of key instructions, reduce the system control flexibility and efficiency, and the energy consumption management lacks collaborative optimization of equipment and communication link, cannot dynamically allocate energy according to actual demand, causes low energy utilization efficiency and shortens the service life of equipment, and the maintenance strategy is mostly passive response, which is difficult to effectively predict potential problems of equipment and reduces the overall operation and maintenance efficiency. SUMMARY

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide a remote operation and maintenance system of water quality monitoring equipment and a method thereof. The technical solution is as follows:

[0006] In one aspect, a remote operation and maintenance system for a water quality monitoring device is provided, the system comprising:

[0007] The communication link optimization module extracts signal transmission characteristics according to the communication channel quality parameter curve, analyzes the influence of environmental interference factors on the communication link, sorts out the communication performance and device state relationship model, and generates a communication link adaptation table;

[0008] The state awareness and diagnosis module extracts device operating state values and target state deviations based on the communication link adaptation table, identifies state change rates and detection periods, quantifies state error accumulation effects, induces state correction weights, and generates a device state correction dataset;

[0009] The instruction adaptation management module extracts instruction demand distribution parameters based on the device state correction dataset, combines device operation rules and instruction execution priorities for hierarchical weighted analysis, and generates an instruction demand matching table;

[0010] The energy consumption dynamic regulation module sorts the instruction priority and energy consumption demand ratio according to the instruction demand matching table, identifies the energy consumption distribution relationship between the device and the communication link, adjusts the distribution order through energy consumption node fluctuation, and constructs a monitoring device communication and energy consumption collaborative optimization scheme.

[0011] As a further scheme of the present application, the communication link adaptation table includes signal strength classification parameters, transmission delay range, interference compensation factor, and state coupling coefficient, the device state correction dataset includes state error factor, response delay parameter, change rate index, and correction weighting value, the instruction demand matching table includes hierarchical instruction interval, demand priority level, fluctuation threshold, and matching weight, and the monitoring device communication and energy consumption collaborative optimization scheme includes energy consumption distribution structure, node adjustment order, fluctuation correction factor, and power distribution factor.

[0012] As a further scheme of the present application, the communication link optimization module comprises:

[0013] The channel characteristic extraction submodule extracts signal transmission characteristics according to the communication channel quality parameter curve, classifies environmental interference factors, and generates a communication performance characteristic table;

[0014] The signal interference analysis submodule analyzes the influence of environmental interference on signal transmission efficiency based on the communication performance characteristic table, calculates the communication performance adaptation value under different working conditions, and generates a communication performance and device state relationship model;

[0015] The adaptation table generation submodule sorts out the communication performance parameters based on the communication performance and device state relationship model, analyzes the corresponding relationship with the target device state value, and generates a communication link adaptation table.

[0016] As a further scheme of the present application, the state awareness and diagnosis module comprises:

[0017] The state deviation extraction submodule extracts the device real-time state value and target state deviation based on the communication link adaptation table, records the state change rate and detection period data, and generates a state deviation data table;

[0018] The error accumulation quantification submodule quantifies the state error accumulation effect based on the state deviation data table, analyzes the communication performance distribution and state demand proportion difference, and generates a state error accumulation effect table;

[0019] The correction weight induction submodule extracts the state deviation magnitude and correction frequency based on the state error accumulation effect table, filters high-frequency error sections and labels deviation directions, induces state correction weights, and generates a device state correction dataset.

[0020] As a further scheme of the present application, the instruction adaptation management module comprises:

[0021] The instruction demand extraction submodule extracts instruction demand distribution parameters based on the device state correction dataset, classifies instruction demand priority data, and generates an instruction demand distribution table;

[0022] The state fluctuation analysis submodule generates a state fluctuation state table based on the instruction demand distribution table, combining device operation rules and instruction execution priority to calculate state node fluctuation state values;

[0023] The hierarchical weighting analysis submodule performs multi-dimensional comparison of instruction demand and state fluctuation based on the state fluctuation state table, filters instruction demand distribution adaptation relationships, and generates an instruction demand matching table.

[0024] As a further scheme of the present application, the energy consumption dynamic regulation module comprises:

[0025] The priority sorting submodule extracts instruction peak position and energy consumption threshold based on the instruction demand matching table, analyzes instruction execution contribution degree under unit energy consumption, and generates an instruction priority sorting table;

[0026] The energy consumption distribution identification submodule extracts main channel node energy consumption change trajectory based on the instruction priority sorting table, identifies energy consumption balance points and deviation directions, and generates an energy consumption distribution relationship table;

[0027] The fluctuation adjustment submodule adjusts the distribution order based on the energy consumption distribution relationship table through energy consumption node fluctuation, extracts node energy consumption change frequency and amplitude sequence, counts the deviation amplitude and duration of energy consumption over-limit nodes, divides stable intervals and fluctuation transition sections, filters main channel access order and bypass auxiliary channel connection nodes, and generates a monitoring device communication and energy consumption collaborative optimization scheme.

[0028] As a further scheme of the present application, the energy consumption node fluctuation condition adjusts the allocation order, which refers to reducing the priority of the energy consumption over-limit node in the allocation order when the offset amplitude of the energy consumption over-limit node exceeds the preset range;

[0029] The offset amplitude and the number of times of the energy consumption over-limit node refer to recording the over-limit amount and the duration when the energy consumption exceeds the energy consumption threshold;

[0030] The stable interval and the fluctuation transition section refer to determining the interval with energy consumption fluctuation less than the preset stable threshold as a stable interval, and determining the interval with energy consumption fluctuation between the stable threshold and the preset fluctuation over-limit threshold as a fluctuation transition section.

[0031] As a further scheme of the present application, the system further comprises a maintenance path planning module:

[0032] The maintenance path planning module monitors the device operation state and instruction execution based on the monitoring device communication and energy consumption cooperative optimization scheme, compares the uncompleted task demand with the residual resource capacity in real time, fills in the energy consumption fluctuation gap by adjusting the communication link performance and instruction execution matching order, and generates a device dynamic maintenance adjustment scheme;

[0033] The device dynamic maintenance adjustment scheme includes residual error adjustment parameters, execution order configuration, residual resource utilization rate, and maintenance completion criterion.

[0034] As a further scheme of the present application, the maintenance path planning module comprises:

[0035] The state monitoring submodule collects energy consumption node values and device state feedback based on the monitoring device communication and energy consumption cooperative optimization scheme, records the jumping time and the deviation amplitude, labels the energy consumption mutation and the state offset position, and generates a state monitoring data table;

[0036] The demand comparison submodule extracts the corresponding time point of the uncompleted task based on the state monitoring data table, identifies the resource residual and the instantaneous gap, matches the target gap and the resource section, and generates a demand comparison result table;

[0037] The dynamic adjustment submodule fills in the energy consumption fluctuation gap by adjusting the communication link performance and the instruction execution matching order based on the demand comparison result table, identifies the resource gap node and the response lag section, extracts the unexecuted section and the standby path, updates the communication link timing and the device control curve, and generates a device dynamic maintenance adjustment scheme.

[0038] On the other hand, a remote operation and maintenance method of a water quality monitoring device is based on the above-mentioned remote operation and maintenance system of the water quality monitoring device, which comprises the following steps:

[0039] S1: According to the communication channel quality parameter curve, the signal transmission characteristics and the device target state value are extracted, the environmental interference influence factor is normalized, the communication performance and the target state relationship are matched, and the communication link adaptation table is generated;

[0040] S2: Based on the communication link adaptation table, the target and real-time state deviation value is extracted, the offset amplitude and detection duration are calculated, the state change rate and detection period data are screened, and the device state correction data set is generated;

[0041] S3: Based on the device state correction data set, the instruction change frequency and peak node in unit time are extracted, the energy consumption response value is associated to identify abnormal transition points and stable recovery points, the echo time and jump boundary in the energy consumption fluctuation interval are extracted, and the instruction demand matching table is generated;

[0042] S4: Based on the instruction demand matching table, the high-frequency instruction priority section and the fluctuation peak position are analyzed, the step change node is identified and the main channel and compensation path are reconstructed, and the monitoring device communication and energy consumption collaborative optimization scheme is constructed;

[0043] S5: Based on the monitoring device communication and energy consumption collaborative optimization scheme, the uncompleted task demand parameter and the key node fluctuation state value are screened, the offset frequency peak value is extracted and the state adjustment control logic is corrected, and the device dynamic maintenance adjustment scheme is generated.

[0044] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0045] By deeply analyzing the communication channel quality parameters and environmental interference, the adaptive optimization of the communication link is realized, the stability and reliability of data transmission are ensured, the device running state deviation and change rate are finely perceived, the state error accumulation is quantized, the diagnosis accuracy of the device running state is greatly improved, the device running law and instruction priority are comprehensively considered, the instruction demand matching and delivery order are optimized, the timely and effective execution of the control instruction is ensured, the energy consumption distribution of the device and the communication link is dynamically controlled, and the energy utilization efficiency of the system as a whole is significantly improved according to the instruction priority and energy consumption demand ratio, the service life of the device is effectively prolonged, the device running and instruction execution are monitored in real time, the maintenance strategy and resource allocation are dynamically adjusted, the intelligentization of the system running and maintenance management is realized, and the smooth completion of various tasks is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating any inventive labor.

[0047] Figure 1 Fig. 1 is a schematic diagram of a remote operation and maintenance system of a water quality monitoring device according to an embodiment of the present application;

[0048] Figure 2 Fig. 2 is a schematic diagram of a system framework according to an embodiment of the present application;

[0049] Figure 3 Fig. 3 is a flowchart of a communication link optimization module according to an embodiment of the present application;

[0050] Figure 4 Fig. 4 is a flowchart of a state awareness and diagnosis module according to an embodiment of the present application;

[0051] Figure 5 Fig. 5 is a flowchart of an instruction adaptation management module according to an embodiment of the present application;

[0052] Figure 6 Fig. 6 is a flowchart of an energy consumption dynamic regulation module according to an embodiment of the present application;

[0053] Figure 7 Fig. 7 is a flowchart of a maintenance path planning module according to an embodiment of the present application;

[0054] Figure 8 Fig. 8 is a flowchart of a remote operation and maintenance method of a water quality monitoring device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the present application will be described below with reference to the drawings.

[0056] In the embodiments of the present application, the words such as "for example", "for instance", "such as", "for example", "for instance", "such as" and the like are used to represent an example, an illustration or a description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0057] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0058] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. The meanings expressed are consistent when the distinction is not emphasized.

[0059] In order to make the technical problems, technical solutions and advantages of the present application clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0060] The embodiment of the present application provides a remote operation and maintenance system of a water quality monitoring device. Figures 1-2 As shown in the schematic diagram of the remote operation and maintenance system of the water quality monitoring device, the system comprises:

[0061] The communication link optimization module extracts signal transmission characteristics according to the communication channel quality parameter curve, analyzes the influence of environmental interference factors on the communication link, sorts the communication performance and device state relationship model, and generates a communication link adaptation table;

[0062] The state awareness and diagnosis module extracts device operating state values and target state deviations based on the communication link adaptation table, identifies state change rates and detection periods, quantifies state error accumulation effects, induces state correction weights, and generates a device state correction dataset;

[0063] The instruction adaptation management module extracts instruction demand distribution parameters based on the device state correction dataset, and performs hierarchical weighted analysis combined with device operation rules and instruction execution priorities to generate an instruction demand matching table;

[0064] The energy consumption dynamic regulation and control module sorts the instruction priority and energy consumption demand proportion according to the instruction demand matching table, identifies the energy consumption distribution relationship between the device and the communication link, adjusts the distribution order through the energy consumption node fluctuation, and constructs a monitoring device communication and energy consumption collaborative optimization scheme;

[0065] The maintenance path planning module monitors the device operating state and instruction execution based on the monitoring device communication and energy consumption collaborative optimization scheme, compares the uncompleted task demand and the remaining resource capacity in real time, fills in the energy consumption fluctuation gap by adjusting the communication link performance and instruction execution matching order, and generates a device dynamic maintenance adjustment scheme.

[0066] The communication link adaptation table includes signal strength classification parameters, transmission delay range, interference compensation factor and state coupling coefficient, the device state correction dataset includes state error factor, response delay parameter, change rate index and correction weighting value, the instruction demand matching table includes hierarchical instruction interval, demand priority level, fluctuation threshold and matching weight, the monitoring device communication and energy consumption collaborative optimization scheme includes energy consumption distribution structure, node adjustment order, fluctuation correction factor and power distribution factor, and the device dynamic maintenance adjustment scheme includes residual adjustment parameter, execution order configuration, remaining resource utilization rate and maintenance completion criterion.

[0067] Specifically, as shown in Figure 2 、 3 The communication link optimization module comprises:

[0068] The channel characteristic extraction submodule extracts signal transmission characteristics according to the communication channel quality parameter curve, classifies environmental interference factors, and generates a communication performance characteristic table.

[0069] According to the communication channel quality parameter curve, the signal-to-noise ratio, received signal strength, packet loss rate, and time delay data are continuously obtained, the environmental temperature and humidity are recorded, the data mean and standard deviation are calculated, the signal transmission characteristics are extracted, and the extracted characteristics are compared with the baseline performance value. The baseline performance value is set as the average communication performance data of one week of operation in an ideal environment, and the signal-to-noise ratio deviation threshold is set as 3 dB. When the characteristics deviate from the baseline by more than the threshold, potential interference factors are identified. The module matches the current signal characteristics with environmental interference characteristic patterns, such as a 5 dB decrease in average signal-to-noise ratio, a 10 dBm decrease in received signal strength, and a 0.02 increase in packet loss rate, which are associated with "welding robot operation" interference. The interference factors are classified, such as "welding robot operation" being classified as "electromagnetic interference type". The signal transmission characteristic mean and standard deviation during the existence of each type of interference factor are calculated, and a communication performance characteristic table is generated.

[0070] The signal interference analysis submodule analyzes the impact of environmental interference on signal transmission efficiency based on the communication performance characteristic table, calculates the communication performance adaptation value under different working conditions, and generates a communication performance and device state relationship model.

[0071] Based on the communication performance characteristic table, each environmental interference factor and transmission characteristic is extracted, the channel transmission efficiency is calculated, and the communication efficiency evaluation index is defined as (1 minus average packet loss rate) multiplied by (1 minus (average time delay minus minimum baseline time delay) divided by (maximum tolerated time delay minus minimum baseline time delay)). The minimum baseline time delay is set to 30 milliseconds, and the maximum tolerated time delay is set to 100 milliseconds. For example, the baseline communication efficiency is 0.8529, and the communication efficiency under welding robot operation interference is 0.1993. The impact of environmental interference on signal transmission efficiency is quantified, the communication performance adaptation value under different working conditions is calculated, and the device state is classified, such as "routine monitoring" target packet loss rate less than 0.01 and target time delay less than 100 ms, "emergency alarm" target packet loss rate 0 and target time delay less than 20 ms, and "firmware update" target packet loss rate less than 0.1 and target time delay less than 200 ms. According to the actual and target gap, adaptation parameters are generated, such as increasing the retransmission number by 3 times, increasing the transmission power by 5 dB, setting a high-level priority queue, and generating a communication performance and device state relationship model.

[0072] The adaptation table generation submodule generates a communication link adaptation table based on the communication performance and device state relationship model, organizes the communication performance parameters, and analyzes the corresponding relationship with the target device state value.

[0073] Based on the communication performance and device state relationship model, the communication performance adaptation value under the combination of each environmental interference and device state is extracted, the adaptation value includes the increase of retransmission times, the increase of transmission power, the increase of priority queue level, and the baseline communication parameters are integrated, the baseline retransmission times is set to 1, the baseline transmission power is set to 10 dBm, the baseline priority queue level is set to 0, and the baseline coding scheme is set to QPSK, and the adjusted communication parameters are calculated, that is, the adaptation value is superimposed on the baseline parameters, for example, the adjusted retransmission times is 4, the transmission power is 15 dBm, and the priority queue level is 2, the correspondence between the adjusted communication parameters and the target device state value is analyzed, the target device state value refers to the required communication performance of the device, whether the adjusted parameter combination meets or exceeds the target performance requirement is verified, for example, 4 times of retransmission reduces the packet loss rate from 0.07 to 0.0005, 15 dBm transmission power improves the signal-to-noise ratio to 26 dB, and high priority queue ensures fast and reliable data transmission, the final communication link configuration of all environmental interference factors and device state combinations is sorted, and a communication link adaptation table is generated.

[0074] Specifically, as shown in Figure 2 、 4 , the state awareness and diagnosis module includes:

[0075] The state deviation extraction submodule extracts the real-time state value and target state deviation of the device based on the communication link adaptation table, records the state change rate and detection period data, and generates a state deviation data table;

[0076] Based on the communication link adaptation table, the expected communication parameter configuration under the current environmental interference and device state is obtained, for example, the expected retransmission times is 2 times, the transmission power is 13 dBm, and the priority queue level is 0 under the "routine monitoring", the real-time device running state value is collected, for example, the sensor data reporting frequency, the data processing time delay, and the battery consumption rate, the detection period is set to 60 seconds, and the target device running parameter is compared, the target device running parameter is set as the ideal performance index of the device, each state deviation is calculated, for example, the real-time data reporting frequency is 0.8 samples per minute, the target is 1.0 samples per minute, the deviation is-0.2 samples per minute, the real-time processing time delay is 70 milliseconds, the target is 50 milliseconds, and the deviation is 20 milliseconds, the time stamp and detection period of each detection are recorded, the change rate of each state parameter is calculated, for example, the change rate of processing time delay (20 minus 10) divided by 60 seconds is about 0.167 milliseconds per second, the real-time state value, target state value, state deviation, state change rate and detection period data are sorted, and a state deviation data table is generated.

[0077] The error accumulation quantification submodule quantifies the state error accumulation effect based on the state deviation data table, analyzes the difference between the communication performance distribution and the state demand ratio, and generates a state error accumulation effect table;

[0078] Based on the state deviation data table, real-time deviation values ​​of various equipment operating parameters are extracted, such as reporting frequency deviation and processing delay deviation. Within the monitoring time window (e.g., 5 detection cycles, i.e., 5 minutes), the state deviations are accumulated and quantified. The sum of the absolute values ​​of each deviation is calculated as the error accumulation index. For example, the processing delay error accumulation index is 100 milliseconds multiplied by the cycle. A significant accumulation threshold is set. For example, if the processing delay error accumulation index exceeds 100 milliseconds multiplied by the cycle, it is judged as significant accumulation. The difference between the communication performance distribution and the state requirement ratio during the significant accumulation period is analyzed. The actual communication performance under the current environmental interference influence factor is obtained from the communication performance characteristic table. Combined with the current state of the equipment, the target communication performance requirement is obtained from the equipment state and performance requirement table. The state requirement ratio difference is calculated. For example, the delay state requirement ratio difference is (40 minus 85) divided by 40 equals -1.125. The quantified error accumulation index, communication performance distribution, and state requirement ratio difference are sorted out to generate a state error accumulation effect table.

[0079] The correction weight summarization submodule extracts the magnitude and correction frequency of state deviation based on the state error cumulative effect table, filters high-frequency error segments and marks the deviation direction, summarizes the state correction weight, and generates the equipment state correction dataset.

[0080] Based on the state error cumulative effect table, the cumulative quantitative values ​​of each state error are extracted as the state deviation level. For example, the cumulative deviation level of processing delay is 250 milliseconds multiplied by the period. Within a preset observation period (e.g., 24 hours), the number of times the cumulative error level exceeds the significant cumulative threshold is counted as the correction frequency. For example, a correction frequency of 4 times per day is defined as a high deviation level, where the cumulative processing delay level is greater than 200 milliseconds multiplied by the period, or the cumulative reporting frequency level is less than -1.0 samples per minute multiplied by the period. A high correction frequency is defined as more than 3 times per day. High-frequency error segments are then selected. For example, if the cumulative delay deviation is 250 milliseconds multiplied by the period and the correction frequency is 4 times per day, it is determined to be a high-frequency error segment. Based on the positive or negative sign of the original deviation value, the direction of each error deviation is marked. For example, if the delay deviation direction is positive, a state correction weight is assigned. The correction weight calculation method is: base weight multiplied by magnitude factor multiplied by frequency factor multiplied by state criticality factor. For example, the delay deviation correction weight is 1.17. The extracted state deviation magnitude, correction frequency, deviation direction and the summarized state correction weight are sorted out to generate the equipment state correction dataset.

[0081] Specifically, such as Figure 2 , 5 As shown, the instruction adaptation management module includes:

[0082] The instruction requirement extraction submodule extracts instruction requirement distribution parameters based on the equipment status correction dataset, categorizes instruction requirement priority data, and generates an instruction requirement distribution table.

[0083] Based on the device status calibration dataset, various error terms, deviation directions, and status calibration weights are extracted. For example, if the processing latency deviation direction is positive, the calibration weight is 1.17. The instruction request type is identified according to the error terms and deviation directions. For example, a positive processing latency deviation is identified as a "reduce processing load" instruction. Instruction request distribution parameters are set according to the cumulative error magnitude and status criticality factors. For example, a cumulative error magnitude of 250 milliseconds multiplied by the period processing latency deviation calculates a target percentage reduction of 30% in CPU load. The status calibration weights are used to prioritize and classify the various instruction requests. Instructions with a calibration weight greater than 2.0 are classified as "high priority," instructions with a calibration weight between 1.0 and 2.0 are classified as "medium-high priority," instructions with a calibration weight between 0.5 and 1.0 are classified as "medium priority," and instructions with a calibration weight less than 0.5 are classified as "low priority." For example, a processing latency deviation with a calibration weight of 1.17 is classified as medium-high priority. The instruction request types, instruction parameters, and priority data are organized to generate an instruction request distribution table.

[0084] The status fluctuation analysis submodule calculates the status fluctuation value of the status node based on the instruction demand distribution table, combined with the equipment operation rules and instruction execution priority, and generates a status fluctuation status table.

[0085] Based on the instruction demand distribution table, the instruction demand type, parameters, and priority are extracted. Combined with preset equipment operation pattern data, the state changes after instruction execution are predicted. The equipment operation pattern is a series of empirical performance models. For example, reducing CPU load by 30% leads to a 20% reduction in processing latency and a 5% increase in power consumption; increasing the reporting frequency by 0.2 samples per minute leads to a 10% increase in CPU load and an 8% increase in power consumption. Conflict handling and effect aggregation are performed according to instruction priority. For example, in the "welding robot running" and "routine monitoring" states, the initial CPU load is 80%, the processing latency is 70 milliseconds, and the power consumption is 10. With a power consumption of 0 milliwatts and a data reporting frequency of 1.0 samples per minute, the higher-priority instruction "reduce processing load" is executed first, reducing the CPU load to 50%, the processing latency to 56 milliseconds, and the power consumption to 105 milliwatts. Then, the lower-priority instruction "increase reporting frequency" is executed, increasing the CPU load to 55% and the power consumption to 113.4 milliwatts. The predicted values ​​are summarized to obtain the state node fluctuation values ​​after the instruction execution. For example, the predicted CPU load is 55%, the predicted processing latency is 56 milliseconds, the predicted power consumption is 113.4 milliwatts, and the predicted data reporting frequency is 1.2 samples per minute, generating a state fluctuation table.

[0086] The hierarchical weighted analysis submodule performs multi-dimensional comparison between instruction requirements and state fluctuations based on the state fluctuation table, filters the distribution adaptation relationship of instruction requirements, and generates an instruction requirement matching table.

[0087] Based on the state fluctuation table, the fluctuation values ​​of each state node are extracted. For example, if the predicted CPU load is 55% and the predicted processing latency is 56 milliseconds, a multi-dimensional comparison is performed between instruction requirements and state fluctuations. The first layer of comparison verifies whether the fluctuating state meets the basic performance requirements. For example, the predicted CPU load of 55% is lower than the target of 60%, the predicted processing latency of 56 milliseconds is lower than the target of 60 milliseconds, the predicted power consumption of 113.4 milliwatts is lower than the target of 120 milliwatts, and the predicted data reporting frequency of 1.2 samples per minute is higher than the target of 1.0 samples per minute. The second layer of comparison evaluates the instruction execution effect. To ensure consistency with expected requirements, for example, the instruction "reduce processing load" requires a target CPU load reduction of 30%, but the actual reduction is only 25%, resulting in a slight discrepancy. Different weighting coefficients are assigned to various parameters: the processing latency weighting coefficient is set to 0.4, the CPU load weighting coefficient is set to 0.3, the power consumption weighting coefficient is set to 0.2, and the data reporting frequency weighting coefficient is set to 0.1. The overall adaptation score for each instruction is calculated. For example, if the overall adaptation score is 0.85, an adaptation threshold of 0.8 is set to filter the adaptation relationships of instruction requirements and generate an instruction requirement matching table.

[0088] Specifically, such as Figure 2 , 6 As shown, the energy consumption dynamic control module includes:

[0089] The priority sorting submodule extracts the peak position and energy consumption threshold of instructions based on the instruction demand matching table, analyzes the contribution of instruction execution under unit energy consumption, and generates an instruction priority sorting table.

[0090] Based on the instruction requirement matching table, the comprehensive fit score and fit status of each instruction combination are extracted. For example, the comprehensive fit score is 0.85 and the fit status is "yes". The peak position of the instruction after execution and the energy consumption threshold are extracted. The energy consumption threshold is set as the maximum allowable energy consumption value that the device should not exceed under a specific state. For example, the energy consumption threshold is 150 milliwatts under the "routine monitoring" state. The energy consumption peak during instruction execution is identified. For example, the energy consumption peak is 140 milliwatts. The contribution of instruction execution per unit of energy consumption is analyzed. The contribution quantifies the performance improvement benefit brought by consuming a unit of energy consumption. For example, consuming 10 milliwatts of energy consumption brings a 25% reduction in CPU load, and the contribution per unit of energy consumption is 2.5% per milliwatt. Based on the comprehensive fit score, instruction peak position and energy consumption threshold, and the contribution of instruction execution per unit of energy consumption, each instruction is prioritized and an instruction priority ranking table is generated.

[0091] The energy consumption distribution identification submodule extracts the energy consumption change trajectory of the main channel node based on the instruction priority sorting table, identifies the energy consumption balance point and offset direction, and generates an energy consumption distribution relationship table.

[0092] Based on the instruction priority sorting table, the final execution priority and predicted peak energy consumption data of each instruction are extracted. For example, if the priority is high, the predicted peak energy consumption is 180 milliwatts. By monitoring the total energy consumption data of the device in real time and combining it with the historical data of the device operation, the energy consumption change trajectory of the main channel node is identified. The main channel node refers to the component in the device that contributes the most to the total energy consumption and is directly affected by the execution of instructions, such as the CPU, communication module, and sensor module. The energy consumption balance point and the offset direction are identified. The energy consumption balance point is set as the energy consumption is stable within the target range when the device meets various performance requirements. The energy consumption balance point is set as the benchmark energy consumption value. When the real-time energy consumption deviates from the benchmark energy consumption by more than the offset threshold (e.g., 10 milliwatts), it is identified as an energy consumption offset. The offset direction is recorded. The energy consumption change trajectory of the main channel node, the energy consumption balance point, and the offset direction are sorted out to generate an energy consumption distribution relationship table.

[0093] The fluctuation adjustment submodule is based on the energy consumption distribution table. It adjusts the allocation order according to the fluctuation of energy consumption nodes, extracts the frequency and amplitude sequence of node energy consumption changes, counts the offset amplitude and duration of nodes with excessive energy consumption, divides the stable interval and fluctuation transition segment, filters the main channel access order and bypass auxiliary channel connection nodes, and generates a communication and energy consumption collaborative optimization scheme for monitoring equipment.

[0094] The allocation order is adjusted based on the fluctuation of energy consumption nodes. This means that when the offset of a node exceeding the energy consumption limit exceeds a preset range, the priority of the node exceeding the energy consumption limit in the allocation order is reduced.

[0095] The offset magnitude and duration of energy consumption exceeding the limit refer to the amount and duration of energy consumption exceeding the energy consumption threshold.

[0096] Stable range and fluctuation transition section: The range in which energy consumption fluctuation is less than the preset stable threshold is defined as the stable range, and the range in which energy consumption fluctuation is between the stable threshold and the preset fluctuation over-limit threshold is defined as the fluctuation transition section.

[0097] Based on the energy consumption distribution table, the energy consumption change data, energy balance point, and offset direction of the main channel nodes are extracted. The allocation order is adjusted according to the energy consumption node fluctuation. When the offset of the energy consumption exceeding the limit exceeds the preset range (20 milliwatts above the energy balance point), the priority of the energy consumption exceeding the limit node in the allocation order is reduced. The frequency and amplitude sequence of node energy consumption changes are extracted. The frequency refers to the number of times the energy consumption offset exceeds the preset range, and the amplitude sequence records the specific offset amount of each exceeding limit. The offset amplitude and duration of the energy consumption exceeding the limit nodes are statistically analyzed. The offset amplitude refers to the amount of energy consumption exceeding the energy consumption threshold, and the duration refers to the duration of exceeding the energy consumption threshold. Stable intervals and fluctuation transition sections are divided. The energy consumption fluctuation in the stable interval is less than 5 milliwatts, and the energy consumption fluctuation in the fluctuation transition section is between 5 milliwatts and 20 milliwatts. The main channel access order and bypass auxiliary channel connection nodes are screened to determine which main channels can continue to be used, which need to adjust the access order, and which need to be connected to the bypass auxiliary channel for energy diversion. All adjustment strategies and node connection schemes are sorted out to generate a monitoring equipment communication and energy consumption collaborative optimization scheme.

[0098] Specifically, such as Figure 2 , 7 As shown, the maintenance path planning module includes:

[0099] The status monitoring submodule is based on the monitoring equipment communication and energy consumption collaborative optimization scheme. It collects energy consumption node values ​​and equipment status feedback, records the jump time and deviation magnitude, marks the energy consumption change and status offset position, and generates a status monitoring data table.

[0100] Based on the monitoring equipment communication and energy consumption collaborative optimization scheme, the energy consumption adjustment strategy and node connection scheme are obtained. Energy consumption node values ​​and equipment status feedback are collected. For example, if the actual CPU energy consumption is 110 milliwatts and the processing latency drops to 55 milliseconds, the jump time and deviation amplitude are recorded. The jump time refers to the time point when the energy consumption or status feedback changes significantly. The deviation amplitude records the difference between the jump and the baseline value. The significant jump threshold is set as the energy consumption change exceeding 20 milliwatts within 1 minute or the processing latency change exceeding 15 milliseconds within 1 minute. The energy consumption mutation and status offset position are marked. For example, at 10:30, the CPU energy consumption changes suddenly and the processing latency status shifts. The collected energy consumption values, equipment status feedback, jump time, deviation amplitude, and marked mutation and offset position data are organized to generate a status monitoring data table.

[0101] The demand comparison submodule extracts the time points corresponding to unfinished tasks based on the status monitoring data table, identifies the remaining resources and instantaneous gaps, matches the target gaps with resource segments, and generates a demand comparison result table.

[0102] Based on the status monitoring data table, real-time monitoring data such as energy consumption node values, equipment status feedback, jump times, and deviation amplitudes are extracted. The corresponding time points for incomplete tasks are extracted. Incomplete tasks refer to instructions or data transmission tasks that failed to execute as planned due to sudden energy consumption changes or status deviations. By comparing the equipment operation log with the instruction execution plan, the original execution time points of the tasks are found. Resource surplus and instantaneous gaps are identified. Resource surplus refers to the equipment's currently available communication bandwidth, processing capacity, or energy. Instantaneous gaps refer to the insufficient amount of resources required to complete incomplete tasks compared to the currently available resources. The actual throughput of the current communication link, processor idle time, and remaining battery power are calculated to quantify resource surplus. Instantaneous gaps are determined by comparing the resource requirements of incomplete tasks. Target gaps and resource segments are matched. Target gaps refer to the specific amount of resource supplementation to compensate for instantaneous resource insufficiency. Resource segments refer to backup communication links or processing units that the equipment can provide with specific attributes. For example, to compensate for a 10Mbps bandwidth instantaneous gap, a bypass auxiliary communication channel providing 15Mbps bandwidth is matched. A demand comparison result table is generated.

[0103] Based on the demand comparison result table, the dynamic adjustment submodule fills the gaps in energy consumption fluctuations by adjusting the performance of the communication link and the order of command execution, identifies resource gap nodes and response lag segments, extracts unexecuted segments and backup paths, updates the communication link timing and equipment control curves, and generates a dynamic maintenance and adjustment plan for the equipment.

[0104] Based on the demand comparison results table, information on incomplete tasks, resource gaps, and matching resource segments are extracted. Energy consumption fluctuation gaps are filled by adjusting communication link performance and command execution order. This includes adjusting main communication link parameters (e.g., reducing data rate, increasing retransmission), transferring commands to bypass auxiliary channels for execution, rearranging command execution order, prioritizing critical tasks (e.g., transferring high-priority data upload tasks to auxiliary channel A), identifying resource gap nodes and response lag segments (resource gap nodes refer to components with persistently insufficient resources, response lag segments refer to the time period required for performance recovery after the policy takes effect; for example, identifying a bandwidth gap in the main communication module, requiring a 30-second lag time for full performance recovery), extracting unexecuted segments and backup paths (unexecuted segments refer to periods where resources are insufficient or performance is degraded, preventing data transmission as planned, backup paths refer to alternative communication paths that replace the current link), updating communication link timing and equipment control curves, replanning data packet transmission timing, updating various equipment operating parameter control curves, ensuring coordinated operation of communication links and equipment, and generating a dynamic maintenance and adjustment plan for the equipment.

[0105] Please see Figure 8 The remote operation and maintenance method for water quality monitoring equipment is implemented based on the aforementioned remote operation and maintenance system for water quality monitoring equipment, and includes the following steps:

[0106] S1: Based on the communication channel quality parameter curve, extract the signal transmission characteristics and device target state values, normalize the environmental interference impact factor, match the relationship between communication performance and target state, and generate a communication link adaptation table.

[0107] S2: Based on the communication link adapter table, extract the deviation value between the target and the real-time status, calculate the offset amplitude and detection duration, filter the status change rate and detection cycle data, and generate the equipment status correction dataset.

[0108] S3: Based on the equipment status correction dataset, extract the frequency of command changes and peak nodes per unit time, associate energy consumption response values ​​to identify abnormal transition points and stable recovery points, extract echo time and jump boundaries within the energy consumption fluctuation range, and generate a command demand matching table.

[0109] S4: Based on the instruction demand matching table, analyze the priority segment and fluctuation peak position of high-frequency instructions, identify step change nodes and reconstruct the main channel and compensation path, and build a collaborative optimization scheme for communication and energy consumption of monitoring equipment.

[0110] S5: Based on the monitoring equipment communication and energy consumption collaborative optimization scheme, filter the unfinished task requirement parameters and key node fluctuation status values, extract the offset frequency peak and correct the status adjustment control logic to generate a dynamic maintenance and adjustment scheme for the equipment.

[0111] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote operation and maintenance system of a water quality monitoring device, characterized in that, The system comprises: The communication link optimization module extracts signal transmission characteristics according to the communication channel quality parameter curve, analyzes the influence of environmental interference factors on the communication link, sorts out the communication performance and device state relationship model, and generates a communication link adaptation table; The state awareness and diagnosis module extracts the device operating state value and target state deviation based on the communication link adaptation table, identifies the state change rate and detection period, quantifies the state error accumulation effect, induces the state correction weight, and generates a device state correction dataset; The instruction adaptation management module extracts instruction demand distribution parameters based on the device state correction dataset, combines device operation rules and instruction execution priority for hierarchical weighted analysis, and generates an instruction demand matching table; The energy consumption dynamic regulation module sorts the instruction priority and energy consumption demand proportion according to the instruction demand matching table, identifies the energy consumption distribution relationship between the device and the communication link, adjusts the distribution order through the energy consumption node fluctuation, and constructs a monitoring device communication and energy consumption collaborative optimization scheme.

2. The remote operation and maintenance system of the water quality monitoring device according to claim 1, characterized in that: The communication link adaptation table includes signal strength classification parameters, transmission delay range, interference compensation factors, and state coupling coefficients. The device state correction dataset includes state error factors, response delay parameters, change rate indicators, and correction weighting values. The instruction demand matching table includes hierarchical instruction intervals, demand priority levels, fluctuation thresholds, and matching weights. The monitoring device communication and energy consumption collaborative optimization scheme includes energy consumption distribution structure, node adjustment order, fluctuation correction factor, and power distribution factor. 3.The remote operation and maintenance system of water quality monitoring equipment according to claim 1, characterized in that: The communication link optimization module comprises: The channel characteristic extraction submodule extracts signal transmission characteristics according to the communication channel quality parameter curve, classifies environmental interference factors, and generates a communication performance characteristic table; The signal interference analysis submodule analyzes the influence of environmental interference on signal transmission efficiency based on the communication performance characteristic table, calculates the communication performance adaptation value under different working conditions, and generates a communication performance and device state relationship model; The adaptation table generation submodule sorts out the communication performance parameters based on the communication performance and device state relationship model, analyzes the corresponding relationship with the target device state value, and generates a communication link adaptation table.

4. The remote operation and maintenance system of water quality monitoring equipment according to claim 3, characterized in that: The state awareness and diagnosis module comprises: The state deviation extraction submodule extracts the device real-time state value and target state deviation based on the communication link adaptation table, records the state change rate and detection period data, and generates a state deviation data table; The error accumulation quantification submodule quantifies the state error accumulation effect based on the state deviation data table, analyzes the communication performance distribution and state demand proportion difference, and generates a state error accumulation effect table; The correction weight induction submodule extracts the state deviation magnitude and correction frequency based on the state error accumulation effect table, screens high-frequency error sections and labels the deviation direction, induces the state correction weight, and generates a device state correction dataset.

5. The remote operation and maintenance system of water quality monitoring equipment according to claim 4, characterized in that: The instruction adaptation management module comprises: The instruction demand extraction submodule extracts instruction demand distribution parameters based on the device state correction dataset, classifies instruction demand priority data, and generates an instruction demand distribution table; The state fluctuation analysis submodule calculates state node fluctuation state values based on the instruction demand distribution table, in combination with device operation rules and instruction execution priority, and generates a state fluctuation state table; The hierarchical weighting analysis submodule performs instruction demand and state fluctuation multi-dimensional comparison based on the state fluctuation state table, screens instruction demand distribution adaptation relationships, and generates an instruction demand matching table.

6. The remote operation and maintenance system of water quality monitoring equipment according to claim 5, characterized in that: The energy consumption dynamic regulation module includes: The priority sorting submodule extracts instruction peak position and energy consumption threshold based on the instruction demand matching table, analyzes instruction execution contribution degree under unit energy consumption, and generates an instruction priority sorting table; The energy consumption distribution identification submodule extracts main channel node energy consumption change trajectory based on the instruction priority sorting table, identifies energy consumption balance points and offset directions, and generates an energy consumption distribution relationship table; The fluctuation adjustment submodule adjusts distribution order based on the energy consumption distribution relationship table, extracts node energy consumption change frequency and amplitude sequence, and counts offset amplitude and duration of energy consumption over-limit nodes, divides stable intervals and fluctuation transition sections, screens main channel access order and bypass auxiliary channel connection nodes, and generates a monitoring device communication and energy consumption collaborative optimization scheme.

7. The remote operation and maintenance system of water quality monitoring equipment according to claim 6, characterized in that: When the offset amplitude of the energy consumption over-limit node exceeds a preset range, the priority of the energy consumption over-limit node in the distribution order is reduced. The offset amplitude and duration of the energy consumption over-limit node refer to the amount of energy consumption exceeding the energy consumption threshold and the duration when the energy consumption exceeds the energy consumption threshold. The stable interval and fluctuation transition section refer to an interval with energy consumption fluctuation less than a preset stable threshold being determined as a stable interval, and an interval with energy consumption fluctuation between the stable threshold and a preset fluctuation over-limit threshold being determined as a fluctuation transition section. 8.The remote operation and maintenance system of water quality monitoring equipment of claim 1, characterized in that: The system also includes a maintenance path planning module: The maintenance path planning module monitors device operation state and instruction execution based on the monitoring device communication and energy consumption collaborative optimization scheme, compares uncompleted task demand and remaining resource capacity in real time, fills in energy consumption fluctuation gaps by adjusting communication link performance and instruction execution matching order, and generates a device dynamic maintenance adjustment scheme; The device dynamic maintenance adjustment scheme includes residual error adjustment parameters, execution order configuration, remaining resource utilization rate, and maintenance completion criteria.

9. The remote operation and maintenance system of water quality monitoring equipment according to claim 8, characterized in that: The maintenance path planning module includes: The state monitoring submodule collects energy consumption node values and device state feedback based on the monitoring device communication and energy consumption collaborative optimization scheme, records jump time and deviation amplitude, labels energy consumption mutation and state offset position, and generates a state monitoring data table; The demand comparison submodule extracts uncompleted task corresponding time points based on the state monitoring data table, identifies resource remaining and instantaneous gap, matches target gap and resource section, and generates a demand comparison result table; The dynamic adjustment submodule fills in energy consumption fluctuation gaps by adjusting communication link performance and instruction execution matching order based on the demand comparison result table, identifies resource gap nodes and response lag section, extracts unexecuted sections and standby paths, updates communication link timing and device control curve, and generates a device dynamic maintenance adjustment scheme. 10.A method for remote operation and maintenance of a water quality monitoring device, characterized in that, The remote operation and maintenance system of the water quality monitoring equipment according to any one of claims 1-9, comprising the following steps: S1: According to the communication channel quality parameter curve, the signal transmission characteristics and the equipment target state value are extracted, the environmental interference influence factor is normalized, the communication performance and the target state relationship are matched, and the communication link adaptation table is generated; S2: Based on the communication link adaptation table, the target and real-time state deviation value is extracted, the offset amplitude and detection duration are calculated, the state change rate and detection period data are screened, and the equipment state correction data set is generated; S3: Based on the equipment state correction data set, the instruction change frequency and peak node per unit time are extracted, the abnormal transition point and stable recovery point are identified by associating the energy consumption response value, the echo time and jump boundary in the energy consumption fluctuation interval are extracted, and the instruction demand matching table is generated; S4: Based on the instruction demand matching table, the high-frequency instruction priority section and fluctuation peak position are analyzed, the step change node is identified and the main channel and compensation path are reconstructed, and the monitoring equipment communication and energy consumption collaborative optimization scheme is constructed; S5: Based on the monitoring equipment communication and energy consumption collaborative optimization scheme, the uncompleted task demand parameters and key node fluctuation state value are screened, the offset frequency peak value is extracted and the state adjustment control logic is corrected, and the equipment dynamic maintenance adjustment scheme is generated.

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