CN117082113A - Distributed equipment monitoring system and method based on data fusion - Google Patents
Distributed equipment monitoring system and method based on data fusion Download PDFInfo
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Abstract
The invention relates to the technical field of distributed equipment monitoring, in particular to a distributed equipment monitoring system and a distributed equipment monitoring method based on data fusion, wherein the distributed equipment monitoring system comprises an equipment data acquisition module, a storage unit building module, an execution index analysis module, an initial task chain building module, an optimal execution equipment determining module and a real-time updating and optimizing module; the device data acquisition module is used for acquiring historical reading data recorded by distributed devices distributed in the monitoring area and enabling data of the devices; the storage unit establishment module is used for storing historical read data to a monitoring center connected with each distributed device in a manner of enabling classification of the data and setting a storage unit of the corresponding distributed device; the execution index analysis module is used for analyzing the execution index of the corresponding distributed equipment; the initial task chain construction module is used for constructing an initial task execution chain by matching distributed equipment meeting the requirements of the enabling nodes; the optimal execution device determining module is used for analyzing the optimal execution device corresponding to each enabling node.
Description
Technical Field
The invention relates to the technical field of distributed equipment monitoring, in particular to a distributed equipment monitoring system and method based on data fusion.
Background
The distributed equipment is a main carrier applied to monitoring systems such as security monitoring, sensing analysis and the like, the existing distributed monitoring system usually utilizes the distributed monitoring equipment and an analysis center of the Internet of things connected with control to jointly realize monitoring analysis of data, the key point of the distributed monitoring system is that the distributed equipment collects data and transmits the data to the analysis center to perform data fusion analysis to obtain a target result and store the target result, but an user usually ignores one analysis management of the distributed equipment, and because of the large number of the distributed equipment, how to maximally utilize the execution characteristics of each equipment in the whole task execution process to solve different task problems, and the distributed equipment is not taken as a data acquisition tool, and the center of gravity is completely transferred to the analysis center of the Internet of things to be processed, so that careful consideration is needed; and how to realize the combination of the distributed devices under different task events and the utilization of resources, and dynamically updating the actual execution device of each event to realize real-time regulation and control are also a problem which needs to be solved.
Disclosure of Invention
The invention aims to provide a distributed equipment monitoring system and a distributed equipment monitoring method based on data fusion, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed equipment monitoring method based on data fusion comprises the following analysis steps:
step S1: acquiring historical reading data recorded by distributed equipment distributed in a monitoring area and enabling data of the equipment, wherein the enabling data refers to functional attributes executed by the distributed equipment; storing historical read data to a monitoring center connected with each distributed device according to the classification of the enabling data, and setting a storage unit of the corresponding distributed device by the monitoring center according to the classification of the enabling data;
the distributed equipment is primarily divided by the enabling data, so that the equipment can be effectively managed and distinguished;
step S2: analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
step S3: when the monitoring center issues a task event execution signal in real time, an enabling node contained in the task event is built into a task execution chain, and the enabling node representation corresponds to enabling data of required distributed equipment; traversing the storage unit, and constructing an initial task execution chain by matching distributed equipment meeting the requirements of enabling nodes;
step S4: based on the initial task execution chain and the execution index of the corresponding distributed equipment, after analyzing the optimal execution equipment corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain for early warning execution;
step S5: before the next task event execution signal occurs, returning to step S2 to update the execution index of the distributed device, and updating the optimized task execution chain when the next task event occurs.
Further, step S2 includes the following analysis steps:
the historical read data comprises historical execution events, abnormal rates, processing data amounts, processing time lengths and equipment switching connection time lengths;
obtaining the abnormality rate U of the ith distributed equipment in each storage unit i Abnormality rate U i The ratio of the number of times that the distributed equipment performs the task event and fails to be transmitted to the next distributed equipment or the monitoring center within the set time length to the total number of times of performing the task event is represented; the set time length is determined by the average transmission time length of the corresponding distributed equipment recorded by the monitoring center;
extracting average data quantity Q processed by ith distributed device in storage unit in historical task event i And the average duration T of the corresponding processing i Calculating the processing efficiency X of the ith distributed device i =Q i /T i ；
Using the formula: w (W) i =k 1 *(1/U i )+k 2 *X i +k 3 *(1/P i )；
Computing an execution index for an ith distributed deviceW i Wherein P is i Representing the average connection duration of the i-th distributed device switching connection with another distributed device, P i =(1/n)∑L i N represents the number of the other distributed devices which are switched and connected by the execution event corresponding to the ith distributed device, and the enabling data corresponding to the other distributed devices are different; k1, k2 and k3 represent the corresponding fitting coefficients. The larger the execution index is, the stronger the execution capability of the distributed device is;
starting from the health degree of the equipment, the processing efficiency of executing tasks and the connection efficiency when the communication connection needs to be switched, the execution capacities of different distributed equipment under the same function attribute type are determined, so that the monitoring analysis of the equipment is dataized and visualized.
Further, step S3 includes the following analysis steps:
the construction sequence of the task execution chain is the execution procedure sequence corresponding to the task event, and the enabling data of the distributed equipment corresponding to each execution procedure determines an enabling node;
meeting the requirement of the enabling node means that the corresponding functional attribute of the distributed equipment is the same as the functional attribute required by the enabling node;
traversing the storage unit, storing all the distributed devices meeting the demands of the enabling nodes in the same enabling node set, determining that each enabling node set at least comprises one distributed device, and outputting to construct an initial task execution chain by the enabling nodes and the corresponding enabling node sets.
Further, after analyzing the optimal execution device corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain for early warning execution, including the following analysis steps:
step S41: when the enabling node only comprises one distributed device in the initial task execution chain, outputting the current distributed device of the enabling node as the optimal execution device;
step S42: when at least two distributed devices exist in each enabling node in the initial task execution chain, judging whether task events which are the same as the initial task execution chain exist in the historical read data or not, if so, extracting the corresponding historical task event as a standard event, and acquiring the minimum value of the execution index of the distributed devices contained in each enabling node corresponding to the standard event as a basic requirement index; the basic demand index is analyzed because the distributed equipment under each enabling node performs task execution in the historical task event, so that the minimum execution index under the premise of realizing task demand is analyzed, the utilization rate of the equipment can be effectively realized, the resources are reasonably utilized, and the waste of the demand is avoided;
step S43: extracting distributed equipment with an execution index greater than or equal to a basic demand index in each enabling node set as target equipment, screening out equipment in a working state in the target equipment, outputting the rest target equipment as effective equipment, calculating a demand difference value of the effective equipment, subtracting the basic demand index corresponding to the enabling node from the execution index of the effective equipment, and sequencing the effective equipment according to the order of the demand difference value from small to large to generate a first sequence; if the task event which is the same as the initial task execution chain is not recorded in the historical read data, only screening out the equipment which is in a working state in the target equipment, outputting the rest target equipment as effective equipment, and sequencing the effective equipment according to the order of the execution index from large to small to generate a first sequence;
step S44: acquiring a minimum value H of communication distance between the jth effective device and the effective device corresponding to the next adjacent enabled node in the first sequence j Sequencing the effective equipment from small to large according to the minimum communication distance value to generate a second sequence;
step S45: calculating a sequence number value F, f=f, of the active device based on the first sequence and the second sequence 1 +f 2 ，f 1 Representing the sequence number, f, recorded by the active device in the first sequence 2 A sequence number representing a record of the active device in the second sequence; sorting the effective devices according to the values of the sequence numbers from small to large to generate a third sequence, and if no effective devices with the same sequence number value exist in the third sequence, outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node; if the third sequence has valid devices with the same sequence number value, then the third sequence has the same sequence number valueThe magnitude of the second sequence value corresponding to the effective equipment is used as the magnitude basis of the effective equipment; outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node;
step S46: the optimal execution device of each enabling node forms an optimal task execution chain.
When the optimal execution equipment is selected, not only the difference between the optimal execution equipment and the demand is considered, the reasonable utilization of resources is realized as much as possible, the waste of the resources is avoided, but also the probability that whether the position relation between the optimal execution equipment and the direct association equipment is abnormal or not is considered, because the shorter the communication distance is, the higher the efficiency of communication data transmission is in a normal state of the network environment, the lower the possibility of errors is, and the completion degree of the event is primarily considered when the task event is executed and managed, so that the communication distance is preferentially judged as a reference basis when the sequence number values are the same;
further, step S5 includes the following:
storing the read data recorded by the real-time task event after the early warning execution is completed before the next task event execution signal occurs, and returning to the step S2 to update the execution index of the distributed equipment; if the difference value between the updated execution index and the historical execution index is smaller than or equal to the difference value threshold value, extracting an optimized task execution chain which is the same as the next task event in the historical record to perform early warning execution when the next task event occurs, and returning to the step S4 if the distributed equipment which is being executed exists in the optimized task execution chain; the error is smaller, so that the ordering relation of the distributed equipment under each enabling node is not influenced, the same task chain can be directly extracted from the system for execution on the premise of not changing the equipment position, the analysis time of the system is saved, and the monitoring efficiency of the equipment is improved;
if the difference value between the updated execution index and the historical execution numerical value is greater than the difference value threshold, step S3 to step S4 are needed to be executed, the optimization task execution chain is updated, and early warning execution is performed.
The distributed equipment monitoring system comprises an equipment data acquisition module, a storage unit establishment module, an execution index analysis module, an initial task chain construction module, an optimal execution equipment determination module and a real-time updating optimization module;
the device data acquisition module is used for acquiring historical reading data recorded by distributed devices distributed in the monitoring area and enabling data of the devices;
the storage unit establishment module is used for storing historical read data to a monitoring center connected with each distributed device according to classification of the enabling data, and the monitoring center sets storage units of the corresponding distributed devices according to the enabling data as the classification;
the execution index analysis module is used for analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
the initial task chain construction module is used for constructing a task execution chain of an enabling node contained in the task event when the monitoring center issues a task event execution signal in real time, traversing the storage unit and constructing the initial task execution chain by matching distributed equipment meeting the requirement of the enabling node;
the optimal execution device determining module is used for analyzing the optimal execution device corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain and performing early warning execution;
the real-time updating and optimizing module is used for updating the execution index of the distributed equipment before the next task event execution signal occurs and updating an optimizing task execution chain when the next task event occurs.
Further, the execution index analysis module comprises a processing efficiency calculation unit and an execution index calculation unit;
the processing efficiency calculation unit is used for extracting the average data quantity processed by the distributed equipment in the storage unit in the historical task event and the average duration of corresponding processing, and calculating the processing efficiency of the distributed equipment;
the execution index calculation unit is used for calculating an execution index based on the processing efficiency, the abnormal rate and the average connection duration.
Further, the optimal execution device determining module comprises a device number analyzing unit, a marking event determining unit, an effective device determining unit, a sequence analyzing unit and an optimizing task execution chain output unit;
the device number analysis unit is used for analyzing the number of distributed devices contained in the enabling node in the initial task execution chain; outputting the optimal execution equipment of the enabling node as the current distributed equipment when only one distributed equipment is included;
the marking event determining unit is used for determining that the corresponding historical task event is a marking event when at least two distributed devices exist in each enabling node; obtaining a basic demand index corresponding to the standard event;
the effective equipment determining unit is used for determining effective equipment according to the basic demand index and the execution index of the distributed equipment;
the sequence analysis unit is used for generating different sequences of the effective equipment according to different analysis bases;
the optimization task execution chain output unit is used for comprehensively comparing the priority positions after different sequences and outputting the first ordered effective equipment as the optimal execution equipment under the corresponding enabling node; and outputting the optimal execution equipment under each enabled node as an optimal task execution chain.
Compared with the prior art, the invention has the following beneficial effects: according to the method, the equipment is classified and analyzed based on the enabling data of the distributed equipment, the capacity of the equipment for executing tasks is evaluated by combining the historical reading data corresponding to the equipment, when the optimal execution equipment is comprehensively analyzed corresponding to each execution event, the difference between the equipment and the demand is considered, the reasonable utilization of resources is realized as much as possible, the waste of the resources is avoided, the probability that the position relation between the equipment and the directly-related equipment causes communication abnormality is considered, the real-time analysis can be effectively performed on the data after the last task event is executed when the next task event occurs, whether the execution capacity of the distributed equipment is changed is determined, the demand of the current task chain is rapidly met, and the accurate and effective optimal equipment consideration is realized for dynamic regulation; the distributed equipment can not only bear the requirements of executing functions, but also cooperate with each other according to the issued task instructions to fulfill the task purpose, so that the resource utilization rate of the distributed equipment is improved, and the abnormal conditions caused by multiple and complex data transmission of the distributed equipment are reduced as much as possible.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a distributed device monitoring system based on data fusion according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: a distributed equipment monitoring method based on data fusion comprises the following analysis steps:
step S1: acquiring historical reading data recorded by distributed equipment distributed in a monitoring area and enabling data of the equipment, wherein the enabling data refers to functional attributes executed by the distributed equipment; storing historical read data to a monitoring center connected with each distributed device according to the classification of the enabling data, and setting a storage unit of the corresponding distributed device by the monitoring center according to the classification of the enabling data; if the function attribute of the first distributed equipment is data detection, a storage unit is built by taking the data detection as a class, the distributed equipment and the contained historical reading data are stored in the storage unit, and the monitoring center comprises a plurality of storage units;
the distributed equipment is primarily divided by the enabling data, so that the equipment can be effectively managed and distinguished;
step S2: analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
step S2 comprises the following analysis steps:
the historical read data comprises historical execution events, abnormal rates, processing data amounts, processing time lengths and equipment switching connection time lengths;
obtaining the abnormality rate U of the ith distributed equipment in each storage unit i Abnormality rate U i The ratio of the number of times that the distributed equipment performs the task event and fails to be transmitted to the next distributed equipment or the monitoring center within the set time length to the total number of times of performing the task event is represented; the set time length is determined by the average transmission time length of the corresponding distributed equipment recorded by the monitoring center;
extracting average data quantity Q processed by ith distributed device in storage unit in historical task event i And the average duration T of the corresponding processing i Calculating the processing efficiency X of the ith distributed device i =Q i /T i ；
Using the formula: w (W) i =k 1 *(1/U i )+k 2 *X i +k 3 *(1/P i )；
Calculating an execution index W of an ith distributed device i Wherein P is i Representing the average connection duration of the i-th distributed device switching connection with another distributed device, P i =(1/n)∑L i N represents the number of the other distributed devices which are switched and connected by the execution event corresponding to the ith distributed device, and the enabling data corresponding to the other distributed devices are different; k1, k2 and k3 represent the corresponding fitting coefficients. The larger the execution index is, the stronger the execution capability of the distributed device is;
the execution mode of the distributed equipment in the execution event of the historical reading data record is that the unified response of the monitoring center is transmitted to all the distributed equipment to acquire data, and then transmitted to the monitoring center to carry out analysis decision;
the analysis execution index is obtained by acquiring data information of different layers of the distributed equipment and fusing the data information;
starting from the health degree of the equipment, the processing efficiency of executing tasks and the connection efficiency when the communication connection needs to be switched, the execution capacities of different distributed equipment under the same function attribute type are determined, so that the monitoring analysis of the equipment is dataized and visualized.
Step S3: when the monitoring center issues a task event execution signal in real time, an enabling node contained in the task event is built into a task execution chain, and the enabling node representation corresponds to enabling data of required distributed equipment; traversing the storage unit, and constructing an initial task execution chain by matching distributed equipment meeting the requirements of enabling nodes;
because of the large number of distributed devices, a plurality of monitoring devices with the same corresponding functional attributes may exist; at this time, the distributed device of each enabling node in the initial task execution chain has a plurality of communication transmissions and task execution selections;
step S3 comprises the following analysis steps:
the construction sequence of the task execution chain is the execution procedure sequence corresponding to the task event, and the enabling data of the distributed equipment corresponding to each execution procedure determines an enabling node;
meeting the requirement of the enabling node means that the corresponding functional attribute of the distributed equipment is the same as the functional attribute required by the enabling node;
traversing the storage unit, storing all the distributed devices meeting the demands of the enabling nodes in the same enabling node set, determining that each enabling node set at least comprises one distributed device, and outputting to construct an initial task execution chain by the enabling nodes and the corresponding enabling node sets.
The order of enabling nodes is determined by the execution process required by the issued task event, such as the issued task event is "execute procedure one", and the task execution chain corresponding to the execute procedure one is: acquiring sensing data, identifying a tag, matching the sensing data and integrating and outputting; enabling the nodes to respectively correspond to a data reading function, a tag identification function, a matching function and an output function;
step S4: based on the initial task execution chain and the execution index of the corresponding distributed equipment, after analyzing the optimal execution equipment corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain for early warning execution;
after analyzing the optimal execution device corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain for early warning execution, wherein the early warning execution comprises the following analysis steps:
step S41: when the enabling node only comprises one distributed device in the initial task execution chain, outputting the current distributed device of the enabling node as the optimal execution device;
step S42: when at least two distributed devices exist in each enabling node in the initial task execution chain, judging whether task events which are the same as the initial task execution chain exist in the historical read data or not, if so, extracting the corresponding historical task event as a standard event, and acquiring the minimum value of the execution index of the distributed devices contained in each enabling node corresponding to the standard event as a basic requirement index; the marking event indicates that the execution completes the effective task event, and the completion is effective, namely that no abnormal transmission exists and the task requirement is realized; the basic demand index is analyzed because the distributed equipment under each enabling node performs task execution in the historical task event, so that the minimum execution index under the premise of realizing task demand is analyzed, the utilization rate of the equipment can be effectively realized, the resources are reasonably utilized, and the waste of the demand is avoided;
step S43: extracting distributed equipment with an execution index greater than or equal to a basic demand index in each enabling node set as target equipment, screening out equipment in a working state in the target equipment, outputting the rest target equipment as effective equipment, calculating a demand difference value of the effective equipment, subtracting the basic demand index corresponding to the enabling node from the execution index of the effective equipment, and sequencing the effective equipment according to the order of the demand difference value from small to large to generate a first sequence; if the task event which is the same as the initial task execution chain is not recorded in the historical read data, only screening out the equipment which is in a working state in the target equipment, outputting the rest target equipment as effective equipment, and sequencing the effective equipment according to the order of the execution index from large to small to generate a first sequence;
step S44: acquiring a minimum value H of communication distance between the jth effective device and the effective device corresponding to the next adjacent enabled node in the first sequence j The effective equipment is most according to the communication distanceThe small values are sequentially ordered from small to large to generate a second sequence;
step S45: calculating a sequence number value F, f=f, of the active device based on the first sequence and the second sequence 1 +f 2 ，f 1 Representing the sequence number, f, recorded by the active device in the first sequence 2 A sequence number representing a record of the active device in the second sequence; sorting the effective devices according to the values of the sequence numbers from small to large to generate a third sequence, and if no effective devices with the same sequence number value exist in the third sequence, outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node; if the third sequence has the effective equipment with the same sequence number value, the value of the second sequence corresponding to the effective equipment with the same sequence number value is used as the size basis of the effective equipment; outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node;
step S46: the optimal execution device of each enabling node forms an optimal task execution chain.
When the optimal execution equipment is selected, not only the difference between the optimal execution equipment and the demand is considered, the reasonable utilization of resources is realized as much as possible, the waste of the resources is avoided, but also the probability that whether the position relation between the optimal execution equipment and the direct association equipment is abnormal or not is considered, because the shorter the communication distance is, the higher the efficiency of communication data transmission is in a normal state of the network environment, the lower the possibility of errors is, and the completion degree of the event is primarily considered when the task event is executed and managed, so that the communication distance is preferentially judged as a reference basis when the sequence number values are the same;
as shown in the examples:
when the task event comprises an enabling node 1, an enabling node 2 and an enabling node 3;
the effective devices contained in the enabling node 1 are distributed devices 1 and 2;
the effective devices contained in the enabling node 2 are distributed devices 3, 4 and 5;
the effective devices contained in the enabling node 3 are distributed devices 6 and 7;
the first sequence generated by the analysis enabling node 1 is distributed equipment 2 and distributed equipment 1;
the second sequence is a distributed device 2, a distributed device 1,
the analysis process is as follows: the distance between the distributed device 2 and the distributed device 3 in the enabled node 2 is 1m, the distance between the distributed device 2 and the distributed device 4 in the enabled node 2 is 1.5m, and the distance between the distributed device 2 and the distributed device 5 in the enabled node 2 is 3m; the minimum value is 1m;
the distance between the distributed device 1 and the distributed device 3 in the enabled node 2 is 2m, the distance between the distributed device 1 and the distributed device 4 in the enabled node 2 is 3.2m, and the distance between the distributed device 1 and the distributed device 5 in the enabled node 2 is 1.9m; the minimum value is 1.9m; so 1<1.9;
calculating a third sequence as distributed device 2 (1+1=2), distributed device 1 (2+2=4);
the best performing device of the enabling node 1 is the distributed device 2.
If the second sequence is the distributed device 1 and when the distributed device 2 is the distributed device 1, the serial numbers of the distributed device 1 and the distributed device 2 in the third sequence are the same, and the finally generated actual third sequence is the distributed device 1 based on the sequence of the second sequence, and the distributed device 2, the optimal execution device of the output enabling node 1 is the distributed device 1.
Step S5: before the next task event execution signal occurs, returning to step S2 to update the execution index of the distributed device, and updating the optimized task execution chain when the next task event occurs.
Step S5 includes the following:
storing the read data recorded by the real-time task event after the early warning execution is completed before the next task event execution signal occurs, and returning to the step S2 to update the execution index of the distributed equipment; if the difference value between the updated execution index and the historical execution index is smaller than or equal to the difference value threshold value, extracting an optimized task execution chain which is the same as the next task event in the historical record to perform early warning execution when the next task event occurs, and returning to the step S4 if the distributed equipment which is being executed exists in the optimized task execution chain; the error is smaller, so that the ordering relation of the distributed equipment under each enabling node is not influenced, the same task chain can be directly extracted from the system for execution on the premise of not changing the equipment position, the analysis time of the system is saved, and the monitoring efficiency of the equipment is improved;
if the difference value between the updated execution index and the historical execution numerical value is greater than the difference value threshold, step S3 to step S4 are needed to be executed, the optimization task execution chain is updated, and early warning execution is performed.
The distributed equipment monitoring system comprises an equipment data acquisition module, a storage unit establishment module, an execution index analysis module, an initial task chain construction module, an optimal execution equipment determination module and a real-time updating optimization module;
the device data acquisition module is used for acquiring historical reading data recorded by distributed devices distributed in the monitoring area and enabling data of the devices;
the storage unit establishment module is used for storing historical read data to a monitoring center connected with each distributed device according to classification of the enabling data, and the monitoring center sets storage units of the corresponding distributed devices according to the enabling data as the classification;
the execution index analysis module is used for analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
the initial task chain construction module is used for constructing a task execution chain of an enabling node contained in the task event when the monitoring center issues a task event execution signal in real time, traversing the storage unit and constructing the initial task execution chain by matching distributed equipment meeting the requirement of the enabling node;
the optimal execution device determining module is used for analyzing the optimal execution device corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain and performing early warning execution;
the real-time updating and optimizing module is used for updating the execution index of the distributed equipment before the next task event execution signal occurs and updating an optimizing task execution chain when the next task event occurs.
The execution index analysis module comprises a processing efficiency calculation unit and an execution index calculation unit;
the processing efficiency calculation unit is used for extracting the average data quantity processed by the distributed equipment in the storage unit in the historical task event and the average duration of corresponding processing, and calculating the processing efficiency of the distributed equipment;
the execution index calculation unit is used for calculating an execution index based on the processing efficiency, the abnormal rate and the average connection duration.
The optimal execution device determining module comprises a device quantity analyzing unit, a targeting event determining unit, an effective device determining unit, a sequence analyzing unit and an optimization task execution chain output unit;
the device number analysis unit is used for analyzing the number of distributed devices contained in the enabling node in the initial task execution chain; outputting the optimal execution equipment of the enabling node as the current distributed equipment when only one distributed equipment is included;
the marking event determining unit is used for determining that the corresponding historical task event is a marking event when at least two distributed devices exist in each enabling node; obtaining a basic demand index corresponding to the standard event;
the effective equipment determining unit is used for determining effective equipment according to the basic demand index and the execution index of the distributed equipment;
the sequence analysis unit is used for generating different sequences of the effective equipment according to different analysis bases;
the optimization task execution chain output unit is used for comprehensively comparing the priority positions after different sequences and outputting the first ordered effective equipment as the optimal execution equipment under the corresponding enabling node; and outputting the optimal execution equipment under each enabled node as an optimal task execution chain.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The distributed equipment monitoring method based on data fusion is characterized by comprising the following analysis steps:
step S1: acquiring historical reading data recorded by distributed equipment distributed in a monitoring area and enabling data of the equipment, wherein the enabling data refers to functional attributes executed by the distributed equipment; storing historical read data to a monitoring center connected with each distributed device according to classification of the enabling data, wherein the monitoring center sets a storage unit of the corresponding distributed device according to the enabling data as the classification;
step S2: analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
step S3: when the monitoring center issues a task event execution signal in real time, an enabling node contained in the task event is built into a task execution chain, and the enabling node representation corresponds to enabling data of required distributed equipment; traversing the storage unit, and constructing an initial task execution chain by matching distributed equipment meeting the requirements of enabling nodes;
step S4: based on the initial task execution chain and the execution index of the corresponding distributed equipment, after analyzing the optimal execution equipment corresponding to each enabling node, updating the initial task execution chain into the optimal task execution chain for early warning execution;
step S5: before the next task event execution signal occurs, returning to step S2 to update the execution index of the distributed device, and updating the optimized task execution chain when the next task event occurs.
2. The distributed device monitoring method based on data fusion according to claim 1, wherein: the step S2 includes the following analysis steps:
the history read data comprises history execution events, abnormal rate, processing data quantity, processing time length and equipment switching connection time length;
obtaining the abnormality rate U of the ith distributed equipment in each storage unit i The abnormality rate U i The ratio of the number of times that the distributed equipment performs the task event and fails to be transmitted to the next distributed equipment or the monitoring center within the set time length to the total number of times of performing the task event is represented; the set time length is determined by the average transmission time length of the corresponding distributed equipment recorded by the monitoring center;
extracting average data quantity Q processed by ith distributed device in storage unit in historical task event i And the average duration T of the corresponding processing i Calculating the processing efficiency X of the ith distributed device i =Q i /T i ；
Using the formula: w (W) i =k 1 *(1/U i )+k 2 *X i +k 3 *(1/P i )；
Calculating an execution index W of an ith distributed device i Wherein P is i Representing the average connection duration of the i-th distributed device switching connection with another distributed device, P i =(1/n)∑L i N represents the number of the other distributed devices which are switched and connected by the execution event corresponding to the ith distributed device, and the enabling data corresponding to the other distributed devices are different; k1, k2 and k3 represent the corresponding fitting coefficients.
3. A distributed device monitoring method based on data fusion according to claim 2, characterized in that: the step S3 includes the following analysis steps:
the construction sequence of the task execution chain is the execution procedure sequence corresponding to the task event, and the enabling data of the distributed equipment corresponding to each execution procedure determines an enabling node;
the meeting of the enabling node requirement means that the corresponding functional attribute of the distributed equipment is the same as the functional attribute required by the enabling node;
traversing the storage unit, storing all the distributed devices meeting the demands of the enabling nodes in the same enabling node set, determining that each enabling node set at least comprises one distributed device, and outputting to construct an initial task execution chain by the enabling nodes and the corresponding enabling node sets.
4. A distributed device monitoring method based on data fusion according to claim 3, characterized in that: the analysis of the output of the optimal execution device corresponding to each enabling node as an optimal task execution chain comprises the following analysis steps:
step S41: when an enabling node only comprises one distributed device in an initial task execution chain, outputting the current distributed device of the enabling node as the optimal execution device;
step S42: when at least two distributed devices exist in each enabling node in the initial task execution chain, judging whether task events which are the same as the initial task execution chain exist in the historical read data or not, if so, extracting the corresponding historical task event as a standard event, and acquiring the minimum value of the execution index of the distributed devices contained in each enabling node corresponding to the standard event as a basic requirement index;
step S43: extracting distributed equipment with an execution index greater than or equal to a basic demand index in each enabling node set as target equipment, screening out equipment in a working state in the target equipment, outputting the rest target equipment as effective equipment, and calculating a demand difference value of the effective equipment, wherein the demand difference value is obtained by subtracting the basic demand index corresponding to the enabling node from the execution index of the effective equipment, and sequencing the effective equipment from small to large according to the demand difference value to generate a first sequence; if the task event which is the same as the initial task execution chain is not recorded in the historical read data, only screening out the equipment which is in a working state in the target equipment, outputting the rest target equipment as effective equipment, and sequencing the effective equipment according to the order of the execution index from large to small to generate a first sequence;
step S44: acquiring a minimum value H of communication distance between the jth effective device and the effective device corresponding to the next adjacent enabled node in the first sequence j Sequencing the effective equipment from small to large according to the minimum communication distance value to generate a second sequence;
step S45: calculating a sequence number value F, f=f, of the active device based on the first sequence and the second sequence 1 +f 2 ，f 1 Representing the sequence number, f, recorded by the active device in the first sequence 2 A sequence number representing a record of the active device in the second sequence; sorting the effective devices according to the values of the sequence numbers from small to large to generate a third sequence, and if no effective devices with the same sequence number value exist in the third sequence, outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node; if the third sequence has the effective equipment with the same sequence number value, the value of the second sequence corresponding to the effective equipment with the same sequence number value is used as the size basis of the effective equipment; outputting the first effective device in the third sequence as the optimal execution device under the corresponding enabling node;
step S46: the optimal execution device of each enabling node forms an optimal task execution chain.
5. The distributed device monitoring method based on data fusion according to claim 4, wherein: the step S5 includes the following steps:
storing the read data recorded by the real-time task event after the early warning execution is completed before the next task event execution signal occurs, and returning to the step S2 to update the execution index of the distributed equipment; if the difference value between the updated execution index and the historical execution index is smaller than or equal to the difference value threshold value, extracting an optimized task execution chain which is the same as the next task event in the historical record to perform early warning execution when the next task event occurs, and returning to the step S4 if the distributed equipment which is being executed exists in the optimized task execution chain;
if the difference value between the updated execution index and the historical execution numerical value is greater than the difference value threshold, step S3 to step S4 are needed to be executed, the optimization task execution chain is updated, and early warning execution is performed.
6. A distributed device monitoring system applying the data fusion-based distributed device monitoring method according to any one of claims 1 to 5, which is characterized by comprising a device data acquisition module, a storage unit establishment module, an execution index analysis module, an initial task chain construction module, an optimal execution device determination module and a real-time update optimization module;
the device data acquisition module is used for acquiring historical reading data recorded by distributed devices distributed in the monitoring area and enabling data of the devices;
the storage unit building module is used for storing historical read data to a monitoring center connected with each distributed device according to classification of the enabling data, and the monitoring center sets storage units of the corresponding distributed devices according to the enabling data as the classification;
the execution index analysis module is used for analyzing the execution index of the corresponding distributed equipment based on the historical read data recorded by the distributed equipment in each storage unit;
the initial task chain construction module is used for constructing a task execution chain of an enabling node contained in a task event when the monitoring center issues a task event execution signal in real time, traversing the storage unit and constructing the initial task execution chain by matching distributed equipment meeting the requirement of the enabling node;
the optimal execution device determining module is used for analyzing the optimal execution device corresponding to each enabling node and then updating the initial task execution chain into the optimal task execution chain for early warning execution;
the real-time updating and optimizing module is used for updating the execution index of the distributed equipment before the next task event execution signal occurs and updating an optimizing task execution chain when the next task event occurs.
7. The distributed device monitoring system of claim 6, wherein: the execution index analysis module comprises a processing efficiency calculation unit and an execution index calculation unit;
the processing efficiency calculation unit is used for extracting the average data volume processed by the distributed equipment in the storage unit in the historical task event and the average duration of corresponding processing, and calculating the processing efficiency of the distributed equipment;
the execution index calculation unit is used for calculating an execution index based on the processing efficiency, the abnormal rate and the average connection duration.
8. The distributed device monitoring system of claim 7, wherein: the optimal execution device determining module comprises a device quantity analyzing unit, a marking event determining unit, an effective device determining unit, a sequence analyzing unit and an optimizing task execution chain output unit;
the equipment quantity analysis unit is used for analyzing the quantity of distributed equipment contained in the enabling node existing in the initial task execution chain; outputting the optimal execution equipment of the enabling node as the current distributed equipment when only one distributed equipment is included;
the marking event determining unit is used for determining that the corresponding historical task event is a marking event when at least two distributed devices exist in each enabling node; obtaining a basic demand index corresponding to the standard event;
the effective equipment determining unit is used for determining effective equipment according to the basic demand index and the execution index of the distributed equipment;
the sequence analysis unit is used for generating different sequences of the effective equipment according to different analysis and sequencing;
the optimization task execution chain output unit is used for comprehensively comparing the priority positions after different sequences and outputting the first-ordered effective equipment as the optimal execution equipment under the corresponding enabling node; and outputting the optimal execution equipment under each enabled node as an optimal task execution chain.
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