Control operation management system and method for intelligent shelter

Abstract

The invention discloses a control operation management system and method for an intelligent square cabin, and relates to the technical field of intelligent square cabins, and the management method comprises the following steps: carrying out the step division of each control record, and carrying out the evaluation of the operation accuracy of each step; based on the environmental data change presented in any control record, performing accuracy evaluation and abnormality judgment on the control record; abnormal feature extraction is carried out on the abnormal control records, and the influence degree of each abnormal feature is analyzed; extracting feature steps from the control records based on the evaluation result of any control record and the operation accuracy evaluation result of each step, and calculating the risk degree of each feature step; when a worker needs to use the control equipment to adjust the intelligent shelter, an abnormal control risk value is obtained through calculation, and if the abnormal control risk value exceeds a set risk threshold value, the worker is reminded in the adjusting process.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent shelters, and in particular to a control and operation management system and method for intelligent shelters. Background Art

[0002] Smart shelters are agricultural production facilities that integrate advanced technologies. They are designed to improve crop production efficiency through intelligent means. By integrating multiple advanced technologies, they realize intelligent management of agricultural production, which can significantly improve crop growth efficiency and quality and promote sustainable agricultural development.

[0003] In the prior art, the environment in the cabin can be adjusted through a number of monitoring devices and surveillance devices to help the crops in the cabin to be cultivated normally; a series of operations are performed on the cabin through the control equipment. Since the stability of various environmental factors in the cabin needs to be strictly controlled, the control equipment needs to be constantly adjusted. However, this depends on the operating proficiency of the staff. If the proficiency of the staff is low, the operating steps will be omitted or wrong, resulting in abnormal cabin environment, affecting the production efficiency and quality of the crops. Summary of the invention

[0004] The purpose of the present invention is to provide a control and operation management system and method for an intelligent cabin to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a control and operation management method for a smart cabin, the management method comprising the following steps:

[0006] Step S100: adjust the environmental data in the smart cabin through the control device, establish an equipment control data management system to record each control process of the control device, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step;

[0007] Step S200: Obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make an abnormality judgment; extract abnormal features based on the evaluation results of each step in any abnormal operation record, and analyze the influence of each abnormal feature on the accuracy evaluation result;

[0008] Step S300: randomly selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation result of the manipulation record and the operation accuracy evaluation result of each step; analyzing the influence of each characteristic step in each manipulation record, and calculating the risk level of each characteristic step;

[0009] Step S400: Whenever the staff needs to use the control equipment to adjust the smart cabin, a set of control steps is pre-generated based on the environmental data currently presented by the smart cabin; feature judgment and risk level extraction are performed on each control step, and the abnormal control risk value is calculated. If it exceeds the set risk threshold, the staff is reminded during the adjustment process.

[0010] Furthermore, step S100 includes the following steps:

[0011] Step S101: When a worker performs a click operation or an environmental data adjustment operation in the control device during the use of the control device, the operation is recorded in the device control data management system and set as a step; the complete process of the worker using the control device is recorded and the control time of the worker is counted to obtain a control record;

[0012] Step S102: randomly selecting a manipulation record, presetting the desired goal presented in the manipulation record, setting a number of desired steps and the execution order between the desired steps for completing the desired goal; sorting the several desired steps according to the set execution order, and determining the position of any desired step; because the environmental data for cultivating crops in the smart cabin is required to be in a stable value, the required adjustment data can be obtained based on the current environmental data, and then the system can preset the operation steps based on the goal to achieve the goal;

[0013] Step S103: arbitrarily select the i-th step in the manipulation record, obtain the execution operation presented in the i-th step, and compare the execution operation with the execution operation presented in any expected step to obtain the similarity between the two execution operations; if there is a target expected step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the position of the target expected step is determined to be 0. i , according to the formula:

[0014]

[0015] Among them, S i is the similarity between the target expected step and the two execution operations of the i-th step, IF() is the judgment function, if O i =i, then IF(O i =i)=0, otherwise, IF(O i =i)=1; Calculate the operation accuracy A of the i-th step i If the similarity between the two executed operations is lower than the set similarity threshold, the operation accuracy A of the i-th step is i=0; the accuracy of a step is compared with the preset step. The reasons for the change in operation accuracy include abnormal operation sequence and operation omission, as well as the difference in the value of data adjustment. Therefore, by calculating the difference between the bit sequences and combining the similarity between the executed operation and the preset step, a more accurate operation accuracy can be obtained;

[0016] Step S104: If there is a similarity between an expected step and the execution operation of any step in the control record, both of which are less than the set similarity threshold, the expected step with similarity less than the similarity threshold is marked as abnormal and stored in the control record.

[0017] Further, step S200 includes the following steps:

[0018] Step S201: randomly select a control record, preset the target environment data of the control record, and capture the actual environment data after the control record is completed through the monitoring equipment in the smart cabin; divide the target environment data and the actual environment data into different dimensions, preset different difference evaluation rules for the environment data of each dimension, obtain the difference degree of the control record in each dimension, calculate the average value to obtain the comprehensive difference degree C, and obtain the accuracy ACC=1-C of the control record; each dimension includes factors such as temperature, humidity, and light;

[0019] Step S202: Obtain the operation duration and accuracy of all operation records, and calculate the average value to obtain the average operation duration and average accuracy; if there is an operation record whose operation duration exceeds the average operation duration and whose accuracy is lower than the average accuracy, the operation record is marked as an abnormal operation record; whether the operation record is abnormal depends on the operation duration, because the operation duration can reflect whether the operation is accurate, and the completion time required for the same operation is relatively close. If an abnormal situation occurs, continuous operation is required to make up for the abnormality, so the time will be longer, but considering the proficiency of the staff, the accuracy comparison is added to jointly judge the abnormal situation;

[0020] Step S203: obtaining a number of expected steps set for completing the expected goal in each control record, and generating an expected step set corresponding to each control record; comparing any two expected step sets, and if the two expected step sets are the same, classifying the control records corresponding to the two expected step sets into the same type of records;

[0021] Step S204: Select two control records from any similar records, among which there must be an abnormal control record, and extract the control accuracy of each step in the two control records respectively; set the control accuracy of the i-th step in the abnormal control record as A i ’, obtain the similarity S between the two execution operations of the i-th step in the abnormal operation record and the corresponding target expected step i ’ , if A i ’ <S i ’ , then the i-th step is marked as abnormal;

[0022] Step S205: extracting features of the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extracting features of the execution operations presented in the remaining steps in the abnormal operation record to obtain a normal feature set; if the normal feature set and the abnormal feature set have the same features, remove the same features from the abnormal feature set;

[0023] Step S206: Set the influence degree of the jth abnormal feature in the abnormal feature set to α j , assign the abnormal features in the abnormal feature set to each step to obtain the abnormal feature set of each step; set the number of abnormal features in the i-th step to m i , according to the formula:

[0024]

[0025] Among them, i1, i2 and j are all positive integers, i1∈(1,n), i2∈(1,n), j∈(1,m i ), A i is the operation accuracy of the ith step in another operation record, n1 is the number of steps in another operation record, and n2 is the number of steps in the abnormal operation record; the influence degree of each abnormal feature in any two operation records containing an abnormal operation record is calculated, and the influence degree of each abnormal feature is confirmed; the influence effect of the abnormal feature will directly affect the operation accuracy of the step itself, and the influence effect of each abnormal feature can be obtained through different results of similar operation records.

[0026] Further, step S300 includes the following steps:

[0027] Step S301: extracting features of the execution operations presented in each step in any operation record; obtaining an abnormal feature set extracted from all abnormal operation records, comparing each feature in any operation record with the abnormal features in the abnormal feature set, and obtaining the number of abnormal features distributed in each step of each abnormal feature in any operation record;

[0028] Step S302: randomly select a control record and set the operation accuracy of the i-th step in the control record to A i; If the control record is an abnormal control record, then obtain the comprehensive difference degree C of the control record, if 1-A i >C, then set the i-th step as a characteristic step; if the operation record is not an abnormal operation record, obtain the number of abnormal features contained in the i-th step as y i And the degree of influence of each abnormal feature, according to the formula:

[0029]

[0030] Where u is a positive integer and u∈(1,y i ), α u is the influence degree of the u-th abnormal feature; calculate the feature judgment value Z of the i-th step i ; If Z i >0, the i-th step is set as the characteristic step; judging the characteristic step mainly determines whether the accuracy of the step is satisfied, and if the difference degree of the step in each control record exceeds the comprehensive difference degree, it is abnormal;

[0031] Step S303: randomly select a characteristic step, obtain the number of times the characteristic step appears in all control records as P, and set the number of control records recorded in the equipment control data management system as P total , the frequency of occurrence of the characteristic step is calculated to be θ = P / P total ; According to the formula:

[0032]

[0033] Wherein, d is the number of abnormal features contained in the feature step; and the risk value F of the feature step is calculated.

[0034] Furthermore, step S400 includes the following steps:

[0035] Step S401: Generate a real-time control record in the equipment control data management system in real time, obtain the environmental data currently presented by the smart cabin, and pre-set a set of control steps in the real-time control record; obtain the risk value of each control step in the control step set as a characteristic step, according to the formula:

[0036]

[0037] Wherein, v is a positive integer and v∈(1,e), e is the number of manipulation steps in the manipulation step set, F v is the risk value of the vth manipulation step, θ v is the frequency of occurrence of the vth manipulation step as a characteristic step; the abnormal manipulation risk value F of the real-time manipulation record is calculated ’ ;

[0038] Step S402: Obtain all the control records that are not abnormal control records, obtain the abnormal control risk value of each control record, and select the abnormal control risk value with the smallest value as the risk threshold F min , if F ’ >F min , the staff will be reminded and the most frequently occurring operation steps will be marked.

[0039] A control operation management system, the management system includes an operation step analysis module, an abnormal feature analysis module, a feature step analysis module and a real-time operation analysis module;

[0040] The operation step analysis module is used to adjust the environmental data in the smart cabin through the control equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step;

[0041] The abnormal feature analysis module is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make abnormal judgments; extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the impact of each abnormal feature on the accuracy evaluation result;

[0042] The characteristic step analysis module is used to select an arbitrary operation record, extract characteristic steps from the operation record based on the evaluation results of the operation record and the operation accuracy evaluation results of each step; analyze the impact of each characteristic step in each operation record, and calculate the risk level of each characteristic step;

[0043] The real-time operation analysis module is used to generate a set of control steps in advance based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; perform feature judgment and risk level extraction on each control step, and calculate the abnormal control risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

[0044] Further, the operation step analysis module includes a manipulation record generation unit and a manipulation step evaluation unit;

[0045] The control record generation unit is used to adjust the environmental data in the smart cabin through the control equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; the control step evaluation unit is used to divide the control process recorded in each control record into steps and evaluate the operation accuracy of each step.

[0046] Further, the abnormal feature analysis module includes an abnormal record analysis unit and an impact degree analysis unit;

[0047] The abnormal record analysis unit is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make abnormal judgments; the impact degree analysis unit is used to extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the impact of each abnormal feature on the accuracy evaluation result.

[0048] Further, the characteristic step analysis module includes a characteristic step extraction unit and a risk degree calculation unit;

[0049] The characteristic step extraction unit is used to arbitrarily select a manipulation record, and extract the characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the evaluation results of the operation accuracy of each step; the risk level calculation unit is used to analyze the impact of each characteristic step in each manipulation record, and calculate the risk level of each characteristic step.

[0050] Furthermore, the real-time operation analysis module includes a control step preset unit and a control risk reminder module;

[0051] The control step preset unit is used to pre-generate a set of control steps based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; the control risk reminder module is used to perform feature judgment and risk level extraction for each control step, and calculate the abnormal control risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention adjusts the environment of the smart cabin by controlling the equipment to ensure the stability of the smart cabin; helps the staff to remind the operation steps that are prone to abnormalities during the adjustment process to avoid omissions or operational errors by the staff, improves the stability of environmental adjustment, and ensures the operating efficiency in the cabin;

[0054] 2. The present invention extracts characteristic steps that affect the control process by performing difference analysis on the accuracy evaluation of the control record itself and the operation accuracy evaluation of each step in the control record, helps the staff to capture characteristic steps that are prone to abnormalities, avoids the situation where the staff causes abnormalities due to the same steps in the future, and improves the stability of environmental regulation;

[0055] 3. The present invention analyzes the frequency of abnormalities in each step and performs abnormal risk assessment on each operation record, so as to help the staff to evaluate the risk value of abnormalities in each subsequent operation record, predict abnormal situations in advance, and remind the operation steps with the highest probability of problems, thereby reducing the frequency of operation errors and effectively improving the stability of the intelligent cabin environment adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the steps of a control and operation management system for a smart shelter;

[0057] Figure 2 The figure is a structural diagram of a control and operation management system for a smart shelter. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Example: Figure 1 to Figure 2 As shown, the present invention provides a control and operation management method for a smart cabin, and the management method comprises the following steps:

[0060] Step S100: adjust the environmental data in the smart cabin through the control device, establish an equipment control data management system to record each control process of the control device, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step;

[0061] Wherein, step S100 includes the following steps:

[0062] Step S101: When a worker performs a click operation or an environmental data adjustment operation in the control device during the use of the control device, the operation is recorded in the device control data management system and set as a step; the complete process of the worker using the control device is recorded and the control time of the worker is counted to obtain a control record;

[0063] Step S102: randomly selecting a manipulation record, presetting the expected goal presented in the manipulation record, setting a number of expected steps and the execution order between the expected steps for completing the expected goal; sorting the several expected steps according to the set execution order, and determining the position order of any expected step;

[0064] Step S103: arbitrarily select the i-th step in the manipulation record, obtain the execution operation presented in the i-th step, and compare the execution operation with the execution operation presented in any expected step to obtain the similarity between the two execution operations; if there is a target expected step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the position of the target expected step is determined to be 0. i , according to the formula:

[0065]

[0066] Among them, S i is the similarity between the target expected step and the two execution operations of the i-th step, IF() is the judgment function, if O i =i, then IF(O i =i)=0, otherwise, IF(O i =i)=1; Calculate the operation accuracy A of the i-th step i If the similarity between the two executed operations is lower than the set similarity threshold, the operation accuracy A of the i-th step is i =0;

[0067] Example 1: The similarity between one of the steps and the corresponding target expected step is set to 80%, wherein the step is the third step, and the position of the target expected step is 2, then the operation accuracy of the third step is A. i =0.8×(1-1×0.5)=0.4;

[0068] Step S104: If there is a similarity between an expected step and the execution operation of any step in the control record, both of which are less than the set similarity threshold, the expected step with similarity less than the similarity threshold is marked as abnormal and stored in the control record.

[0069] Step S200: Obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make an abnormality judgment; extract abnormal features based on the evaluation results of each step in any abnormal operation record, and analyze the influence of each abnormal feature on the accuracy evaluation result;

[0070] Wherein, step S200 includes the following steps:

[0071] Step S201: randomly select a control record, preset the target environment data of the control record, and capture the actual environment data after the control record is completed through the monitoring equipment in the smart cabin; divide the target environment data and the actual environment data into different dimensions, preset different difference evaluation rules for the environment data of each dimension, obtain the difference degree of the control record in each dimension, calculate the average value to obtain the comprehensive difference degree C, and obtain the accuracy ACC of the control record = 1-C;

[0072] Step S202: Obtain the control duration and accuracy of all control records, and calculate the average value to obtain the average control duration and the average accuracy; if there is a control record whose control duration exceeds the average control duration and whose accuracy is lower than the average accuracy, the control record is marked as an abnormal control record;

[0073] Step S203: obtaining a number of expected steps set for completing the expected goal in each control record, and generating an expected step set corresponding to each control record; comparing any two expected step sets, and if the two expected step sets are the same, classifying the control records corresponding to the two expected step sets into the same type of records;

[0074] Step S204: Select two control records from any similar records, among which there must be an abnormal control record, and extract the control accuracy of each step in the two control records respectively; set the control accuracy of the i-th step in the abnormal control record as A i ’ , obtain the similarity S between the two execution operations of the i-th step in the abnormal operation record and the corresponding target expected step i ’ , if A i ’ <S i ’ , then the i-th step is marked as abnormal;

[0075] Step S205: extracting features of the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extracting features of the execution operations presented in the remaining steps in the abnormal operation record to obtain a normal feature set; if the normal feature set and the abnormal feature set have the same features, remove the same features from the abnormal feature set;

[0076] Step S206: Set the influence degree of the jth abnormal feature in the abnormal feature set to α j, assign the abnormal features in the abnormal feature set to each step to obtain the abnormal feature set of each step; set the number of abnormal features in the i-th step to m i , according to the formula:

[0077]

[0078] Among them, i1, i2 and j are all positive integers, i1∈(1,n), i2∈(1,n), j∈(1,m i ), A i is the operation accuracy of the i-th step in another operation record, n1 is the number of steps in another operation record, and n2 is the number of steps in the abnormal operation record; the influence degree of each abnormal feature in any two operation records containing an abnormal operation record is calculated, and the influence degree of each abnormal feature is confirmed;

[0079] Example 2: Assume that there is 1 abnormal feature in the abnormal feature set, and the operation accuracy of the i-th step is 60% and 70% respectively, so we get 0.7×(1-α j )=0.6, we get α j =14.3%;

[0080] Step S300: randomly selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation result of the manipulation record and the operation accuracy evaluation result of each step; analyzing the influence of each characteristic step in each manipulation record, and calculating the risk level of each characteristic step;

[0081] Wherein, step S300 includes the following steps:

[0082] Step S301: extracting features of the execution operations presented in each step in any operation record; obtaining an abnormal feature set extracted from all abnormal operation records, comparing each feature in any operation record with the abnormal features in the abnormal feature set, and obtaining the number of abnormal features distributed in each step of each abnormal feature in any operation record;

[0083] Step S302: randomly select a control record and set the operation accuracy of the i-th step in the control record to A i ; If the control record is an abnormal control record, then obtain the comprehensive difference degree C of the control record, if 1-A i >C, then set the i-th step as a characteristic step; if the operation record is not an abnormal operation record, obtain the number of abnormal features contained in the i-th step as y i And the degree of influence of each abnormal feature, according to the formula:

[0084]

[0085] Where u is a positive integer and u∈(1,y i ), α u is the influence degree of the u-th abnormal feature; calculate the feature judgment value Z of the i-th step i ; If Z i > 0, the i-th step is set as the characteristic step;

[0086] Step S303: randomly select a characteristic step, obtain the number of times the characteristic step appears in all control records as P, and set the number of control records recorded in the equipment control data management system as P total , the frequency of occurrence of the characteristic step is calculated to be θ = P / P total ; According to the formula:

[0087]

[0088] Wherein, d is the number of abnormal features contained in the feature step; and the risk value F of the feature step is calculated.

[0089] Step S400: Whenever the staff needs to use the control equipment to adjust the smart cabin, a set of control steps is pre-generated based on the environmental data currently presented by the smart cabin; feature judgment and risk level extraction are performed on each control step, and the abnormal control risk value is calculated. If it exceeds the set risk threshold, the staff is reminded during the adjustment process;

[0090] Wherein, step S400 includes the following steps:

[0091] Step S401: Generate a real-time control record in the equipment control data management system in real time, obtain the environmental data currently presented by the smart cabin, and pre-set a set of control steps in the real-time control record; obtain the risk value of each control step in the control step set as a characteristic step, according to the formula:

[0092]

[0093] Wherein, v is a positive integer and v∈(1,e), e is the number of manipulation steps in the manipulation step set, F v is the risk value of the vth manipulation step, θ v is the frequency of occurrence of the vth manipulation step as a characteristic step; the abnormal manipulation risk value F of the real-time manipulation record is calculated ’ ;

[0094] Step S402: Obtain all the control records that are not abnormal control records, obtain the abnormal control risk value of each control record, and select the abnormal control risk value with the smallest value as the risk threshold F min , if F ’ >F min , the staff will be reminded and the most frequently occurring operation steps will be marked.

[0095] A control operation management system, the management system includes an operation step analysis module, an abnormal feature analysis module, a feature step analysis module and a real-time operation analysis module;

[0096] The operation step analysis module is used to adjust the environmental data in the smart cabin through the control equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step;

[0097] The abnormal feature analysis module is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make abnormal judgments; extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the impact of each abnormal feature on the accuracy evaluation result;

[0098] The characteristic step analysis module is used to select an arbitrary operation record, extract characteristic steps from the operation record based on the evaluation results of the operation record and the operation accuracy evaluation results of each step; analyze the impact of each characteristic step in each operation record, and calculate the risk level of each characteristic step;

[0099] The real-time operation analysis module is used to generate a set of control steps in advance based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; perform feature judgment and risk level extraction on each control step, and calculate the abnormal control risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

[0100] Among them, the operation step analysis module includes a control record generation unit and a control step evaluation unit;

[0101] The control record generation unit is used to adjust the environmental data in the smart cabin through the control equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; the control step evaluation unit is used to divide the control process recorded in each control record into steps and evaluate the operation accuracy of each step.

[0102] Among them, the abnormal feature analysis module includes an abnormal record analysis unit and an impact degree analysis unit;

[0103] The abnormal record analysis unit is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make abnormal judgments; the impact degree analysis unit is used to extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the impact of each abnormal feature on the accuracy evaluation result.

[0104] Among them, the characteristic step analysis module includes a characteristic step extraction unit and a risk degree calculation unit;

[0105] The characteristic step extraction unit is used to arbitrarily select a manipulation record, and extract the characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the evaluation results of the operation accuracy of each step; the risk level calculation unit is used to analyze the impact of each characteristic step in each manipulation record, and calculate the risk level of each characteristic step.

[0106] Among them, the real-time operation analysis module includes a control step preset unit and a control risk reminder module;

[0107] The control step preset unit is used to pre-generate a set of control steps based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; the control risk reminder module is used to perform feature judgment and risk level extraction for each control step, and calculate the abnormal control risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

[0108] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for controlling and operating a smart shelter, characterized in that: The management method comprises the following steps: Step S100: adjust the environmental data in the smart cabin through the control device, establish an equipment control data management system to record each control process of the control device, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step; Step S200: Obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make an abnormality judgment; extract abnormal features based on the evaluation results of each step in any abnormal operation record, and analyze the influence of each abnormal feature on the accuracy evaluation result; Step S300: randomly selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation result of the manipulation record and the operation accuracy evaluation result of each step; analyzing the influence of each characteristic step in each manipulation record, and calculating the risk level of each characteristic step; Step S400: Whenever the staff needs to use the control equipment to adjust the smart cabin, a set of control steps is pre-generated based on the environmental data currently presented by the smart cabin; feature judgment and risk level extraction are performed on each control step, and the abnormal control risk value is calculated. If it exceeds the set risk threshold, the staff is reminded during the adjustment process.

2. The method for controlling and operating a smart shelter according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: When a worker performs a click operation or an environmental data adjustment operation in the control device during the use of the control device, the operation is recorded in the device control data management system and set as a step; the complete process of the worker using the control device is recorded and the control time of the worker is counted to obtain a control record; Step S102: randomly selecting a manipulation record, presetting the expected goal presented in the manipulation record, setting a number of expected steps and the execution order between the expected steps for completing the expected goal; sorting the several expected steps according to the set execution order, and determining the position order of any expected step; Step S103: arbitrarily select the i-th step in the manipulation record, obtain the execution operation presented in the i-th step, and compare the execution operation with the execution operation presented in any expected step to obtain the similarity between the two execution operations; if there is a target expected step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the position of the target expected step is determined to be 0. i , according to the formula: Among them, S i is the similarity between the target expected step and the two execution operations of the i-th step, IF() is the judgment function, if O i =i, then IF(O i =i)=0, otherwise, IF(O i =i)=1; Calculate the operation accuracy A of the i-th step i If the similarity between the two executed operations is lower than the set similarity threshold, the operation accuracy A of the i-th step is i =0; Step S104: If there is a similarity between an expected step and the execution operation of any step in the control record, both of which are less than the set similarity threshold, the expected step with similarity less than the similarity threshold is marked as abnormal and stored in the control record.

3. The control and operation management method for a smart shelter according to claim 2 is characterized in that: The step S200 includes the following steps: Step S201: randomly select a control record, preset the target environment data of the control record, and capture the actual environment data after the control record is completed through the monitoring equipment in the smart cabin; divide the target environment data and the actual environment data into different dimensions, preset different difference evaluation rules for the environment data of each dimension, obtain the difference degree of the control record in each dimension, calculate the average value to obtain the comprehensive difference degree C, and obtain the accuracy ACC of the control record = 1-C; Step S202: Obtain the control duration and accuracy of all control records, and calculate the average value to obtain the average control duration and the average accuracy; if there is a control record whose control duration exceeds the average control duration and whose accuracy is lower than the average accuracy, the control record is marked as an abnormal control record; Step S203: obtaining a number of expected steps set for completing the expected goal in each control record, and generating an expected step set corresponding to each control record; comparing any two expected step sets, and if the two expected step sets are the same, classifying the control records corresponding to the two expected step sets into the same type of records; Step S204: Select two control records from any similar records, among which there must be an abnormal control record, and extract the control accuracy of each step in the two control records respectively; set the control accuracy of the i-th step in the abnormal control record as A i ’ , obtain the similarity S between the two execution operations of the i-th step in the abnormal operation record and the corresponding target expected step i ’ , if A i ’ <S i ’ , then the i-th step is marked as abnormal; Step S205: extracting features of the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extracting features of the execution operations presented in the remaining steps in the abnormal operation record to obtain a normal feature set; if the normal feature set and the abnormal feature set have the same features, remove the same features from the abnormal feature set; Step S206: Set the influence degree of the jth abnormal feature in the abnormal feature set to α j , assign the abnormal features in the abnormal feature set to each step to obtain the abnormal feature set of each step; set the number of abnormal features in the i-th step to m i , according to the formula: Among them, i1, i2 and j are all positive integers, i1∈(1,n), i2∈(1,n), j∈(1,m i ), A i is the operation accuracy of the i-th step in another operation record, n1 is the number of steps in another operation record, and n2 is the number of steps in the abnormal operation record; the influence degree of each abnormal feature in any two operation records containing an abnormal operation record is calculated, and the influence degree of each abnormal feature is confirmed.

4. The method for controlling and operating a smart shelter according to claim 3 is characterized in that: The step S300 includes the following steps: Step S301: extracting features of the execution operations presented in each step in any operation record; obtaining an abnormal feature set extracted from all abnormal operation records, comparing each feature in any operation record with the abnormal features in the abnormal feature set, and obtaining the number of abnormal features distributed in each step of each abnormal feature in any operation record; Step S302: randomly select a control record and set the operation accuracy of the i-th step in the control record to A i ; If the control record is an abnormal control record, then obtain the comprehensive difference degree C of the control record, if 1-A i >C, then set the i-th step as a characteristic step; if the operation record is not an abnormal operation record, obtain the number of abnormal features contained in the i-th step as y i And the degree of influence of each abnormal feature, according to the formula: Where u is a positive integer and u∈(1,y i ), α u is the influence degree of the u-th abnormal feature; calculate the feature judgment value Z of the i-th step i ; If Z i > 0, the i-th step is set as the characteristic step; Step S303: randomly select a characteristic step, obtain the number of times the characteristic step appears in all control records as P, and set the number of control records recorded in the equipment control data management system as P total , the frequency of occurrence of the characteristic step is calculated to be θ = P / P total ; According to the formula: Wherein, d is the number of abnormal features contained in the feature step; and the risk value F of the feature step is calculated.

5. The method for controlling and operating a smart shelter according to claim 4 is characterized in that: The step S400 includes the following steps: Step S401: Generate a real-time control record in the equipment control data management system in real time, obtain the environmental data currently presented by the smart cabin, and pre-set a set of control steps in the real-time control record; obtain the risk value of each control step in the control step set as a characteristic step, according to the formula: Wherein, v is a positive integer and v∈(1,e), e is the number of manipulation steps in the manipulation step set, F v is the risk value of the vth manipulation step, θ v is the frequency of occurrence of the vth manipulation step as a characteristic step; the abnormal manipulation risk value F of the real-time manipulation record is calculated ’ ; Step S402: Obtain all the control records that are not abnormal control records, obtain the abnormal control risk value of each control record, and select the abnormal control risk value with the smallest value as the risk threshold F min , if F ’ >F min , the staff will be reminded and the most frequently occurring operation steps will be marked.

6. A control and operation management system, used to execute a control and operation management method for a smart cabin according to any one of claims 1 to 5, characterized in that: The management system includes an operation step analysis module, an abnormal feature analysis module, a feature step analysis module and a real-time operation analysis module; The operation step analysis module is used to adjust the environmental data in the smart cabin through the control equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; divide the control process recorded in each control record into steps, and evaluate the operation accuracy of each step; The abnormal feature analysis module is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make an abnormality judgment; extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the impact of each abnormal feature on the accuracy evaluation result; The characteristic step analysis module is used to arbitrarily select a manipulation record and extract characteristic steps from the manipulation record based on the evaluation result of the manipulation record and the operation accuracy evaluation result of each step; analyze The impact of each characteristic step in each control record is calculated to obtain the risk level of each characteristic step; The real-time operation analysis module is used to generate a set of control steps in advance based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; perform feature judgment and risk level extraction on each control step, and calculate the abnormal control risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

7. A control operation management system according to claim 6, characterized in that: The operation step analysis module includes a manipulation record generation unit and a manipulation step evaluation unit; The control record generating unit is used to adjust the environmental data in the smart cabin by controlling the equipment, establish an equipment control data management system to record each control process of the control equipment, and generate a control record; The manipulation step evaluation unit is used to divide the manipulation process recorded in each manipulation record into steps and evaluate the operation accuracy of each step.

8. The control operation management system according to claim 6, characterized in that: The abnormal feature analysis module includes an abnormal record analysis unit and an impact degree analysis unit; The abnormal record analysis unit is used to obtain the environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record and make abnormal judgments; the impact degree analysis unit is used to extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the degree of influence of each abnormal feature on the accuracy evaluation result.

9. The control operation management system according to claim 6, characterized in that: The characteristic step analysis module It includes a characteristic step extraction unit and a risk degree calculation unit; The characteristic step extraction unit is used to arbitrarily select a manipulation record, and extract the characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the evaluation results of the operation accuracy of each step; the risk level calculation unit is used to analyze the impact of each characteristic step in each manipulation record, and calculate the risk level of each characteristic step.

10. The control operation management system according to claim 6, characterized in that: The real-time operation analysis module includes a control step preset unit and a control risk reminder module; The control step preset unit is used to pre-generate a set of control steps based on the environmental data currently presented by the smart cabin whenever the staff needs to use the control equipment to adjust the smart cabin; the control risk reminder module is used to perform feature judgment and risk level extraction on each control step, calculate the abnormal control risk value, and if it exceeds the set risk threshold, remind the staff during the adjustment process.

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