A control and operation management system and method for intelligent shelter

Abstract

The present invention discloses an operation and management system and method for an intelligent cabin, which relates to the technical field of intelligent cabins. The management method comprises the following steps: dividing each operation record into steps and evaluating the operation accuracy of each step; based on the changes in environmental data presented in any operation record, evaluating the accuracy of the operation record and making an abnormality judgment; extracting abnormal features from the operation record with abnormalities, and analyzing the influence of each abnormal feature; based on the evaluation results of any operation record and the operation accuracy evaluation results of each step, extracting characteristic steps from the operation record, and calculating the risk level of each characteristic step; whenever a staff member needs to use an operation device to adjust the intelligent cabin, calculating the abnormal operation risk value, and if it exceeds the set risk threshold, reminding the staff member during the adjustment 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] The smart shelter is an agricultural production facility that integrates advanced technologies. It aims to improve crop production efficiency through intelligent means. By integrating multiple advanced technologies, it realizes intelligent management of agricultural production, which can significantly improve the growth efficiency and quality of crops and promote the sustainable development of agriculture.

[0003] In the existing technology, a number of monitoring devices and surveillance equipment can be used to adjust the environment in the cabin 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 staff's proficiency is low, it will lead to omissions or errors in the operating steps, resulting in abnormalities in the 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 shelter to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling and operating a smart shelter, the method comprising the following steps:

[0006] Step S100: 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 accuracy of each step;

[0007] Step S200: Obtaining environmental data changes presented by the smart cabin in any operation record, evaluating the accuracy of the operation record and determining anomalies; extracting abnormal features based on the evaluation results of each step in any operation record with an abnormality, and analyzing the degree of impact of each abnormal feature on the accuracy evaluation result;

[0008] Step S300: arbitrarily selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the operation accuracy evaluation results of each step; analyzing the impact 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 will be 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 on a control device during use, the operation is recorded in the device control data management system and set as a step; the entire process of the worker using the control device is recorded and the worker's operation time is counted to obtain a control record;

[0012] Step S102: arbitrarily select a manipulation record, preset a desired goal for the manipulation record, set a number of desired steps and an execution order for each desired step to achieve the desired goal; sort the desired steps according to the set execution order, and determine the order of any desired step; because the environmental data for cultivating crops in the smart cabin is required to be at a stable value, the required adjustment data can be obtained based on the current environmental data, and the system can then preset 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, compare the execution operation with the execution operation presented in any desired step, and obtain the similarity between the two execution operations; if there is a target desired step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the order of the target desired 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 a judgment function, if 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. Causes of changes in operation accuracy include abnormal operation sequence and operation omission, as well as differences in data adjustment values. 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 the similarity between an expected step and the execution operation of any step in the manipulation record is less than the set similarity threshold, the expected step with the similarity less than the similarity threshold is marked as abnormal and stored in the manipulation record.

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

[0018] Step S201: Randomly select a control record, preset the target environment data of the control record, and use the monitoring equipment in the smart cabin to capture the actual environment data after the control record is completed; divide the target environment data and the actual environment data into different dimensions, preset different difference assessment rules for the environment data in 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 of the control record ACC = 1-C; 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 values 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. The same operation requires a similar completion time. If an abnormality occurs, continuous operation is required to correct the abnormality, which will take longer. However, considering the proficiency of the staff, the accuracy comparison is added to jointly determine the abnormality.

[0020] Step S203: Acquire a number of expected steps set for completing the expected goal in each manipulation record, and generate an expected step set corresponding to each manipulation record; compare any two expected step sets, and if the two expected step sets are the same, classify the manipulation records corresponding to the two expected step sets into the same category;

[0021] Step S204: Select any two operation records from any similar records, among which there must be an abnormal operation record, and extract the operation accuracy of each step in the two operation records respectively; set the operation accuracy of the i-th step in the abnormal operation 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: Extract features from the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extract features from 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 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; 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] Furthermore, step S300 includes the following steps:

[0027] Step S301: extracting features of the execution operations presented in each step of any operation record; obtaining a set of abnormal features 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 any operation record;

[0028] Step S302: Select any one operation record and set the operation accuracy of the i-th step in the operation record to A iIf the operation record is an abnormal operation record, then obtain the comprehensive difference degree C of the operation 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, then the i-th step is set as the characteristic step; judging the characteristic step mainly determines whether the accuracy of the step is satisfied. If the difference degree of the step in each operation record exceeds the comprehensive difference degree, it is abnormal;

[0031] Step S303: arbitrarily 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; 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, 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 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 operation records that are not abnormal operation records, obtain the abnormal operation risk value of each operation record, select the abnormal operation risk value with the smallest value and set it as the risk threshold F min , if F ’ >F min , the staff will be reminded and the operation steps that occur most frequently will be marked.

[0039] An operation and 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 operation process of the control equipment, and generate a control record; the control process recorded in each control record is divided into steps and the operation accuracy of each step is evaluated;

[0041] The abnormal feature analysis module is used to obtain environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record, and determine abnormalities. Based on the evaluation results of each step in any operation record with abnormalities, the module extracts abnormal features and analyzes the impact of each abnormal feature on the accuracy evaluation result.

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

[0043] The real-time operation analysis module is used to pre-generate a set of operation 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. It also performs feature judgment and risk level extraction on each operation step, and calculates the abnormal operation risk value. If it exceeds the set risk threshold, the staff will be reminded during the adjustment process.

[0044] Furthermore, 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] Furthermore, 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 degree of impact of each abnormal feature on the accuracy evaluation result.

[0048] Furthermore, 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 an operation record and extract the characteristic steps from the operation record based on the evaluation results of the operation record and the operation accuracy evaluation results of each step; the risk level calculation unit is used to analyze the impact of each characteristic step in each operation 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, calculate the abnormal control risk value, and if it exceeds the set risk threshold, remind the staff during the adjustment process.

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

[0053] 1. The present invention regulates the environment of the smart cabin by controlling the equipment to ensure its stability. It helps staff to be reminded of operation steps that are prone to abnormalities during the adjustment process, thus avoiding omissions or operational errors by staff, improving the stability of environmental regulation, and ensuring the operating efficiency within the cabin.

[0054] 2. The present invention performs differential analysis on the accuracy assessment of the control record itself and the accuracy assessment of each step in the control record to extract characteristic steps that affect the control process. This helps staff identify characteristic steps that are prone to anomalies, preventing staff from subsequently causing anomalies due to the same steps, thereby improving the stability of environmental regulation.

[0055] 3. The present invention analyzes the frequency of abnormalities in each step and conducts abnormal risk assessment on each operation record, helping staff to evaluate the risk value of abnormalities in each subsequent operation record, predict abnormal situations in advance, and remind staff of 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0059] Example: Figures 1 to 2 As shown, the present invention provides a method for controlling and operating a smart shelter, and the management method includes the following steps:

[0060] Step S100: 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 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 on a control device during use, the operation is recorded in the device control data management system and set as a step; the entire process of the worker using the control device is recorded and the worker's operation time is counted to obtain a control record;

[0063] Step S102: randomly selecting a manipulation record, presetting a desired goal for the manipulation record, setting a number of desired steps for achieving the desired goal and an execution order for each of the desired steps; sorting the desired steps according to the set execution order, and determining the position of any desired step;

[0064] Step S103: arbitrarily select the i-th step in the manipulation record, obtain the execution operation presented in the i-th step, compare the execution operation with the execution operation presented in any desired step, and obtain the similarity between the two execution operations; if there is a target desired step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the order of the target desired 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 a judgment function, if 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 order 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 the similarity between an expected step and the execution operation of any step in the manipulation record is less than the set similarity threshold, the expected step with the similarity less than the similarity threshold is marked as abnormal and stored in the manipulation record.

[0069] Step S200: Obtaining environmental data changes presented by the smart cabin in any operation record, evaluating the accuracy of the operation record and determining anomalies; extracting abnormal features based on the evaluation results of each step in any operation record with an abnormality, and analyzing the degree of impact of each abnormal feature on the accuracy evaluation result;

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

[0071] Step S201: arbitrarily select a manipulation record, preset target environment data for the manipulation record, and capture the actual environment data after the manipulation 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 assessment rules for the environment data in each dimension, obtain the difference degree of the manipulation record in each dimension, calculate the average value to obtain the comprehensive difference degree C, and obtain the accuracy of the manipulation record ACC = 1-C;

[0072] Step S202: Obtain the operation duration and accuracy of all operation records, and calculate the average values 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.

[0073] Step S203: Acquire a number of expected steps set for completing the expected goal in each manipulation record, and generate an expected step set corresponding to each manipulation record; compare any two expected step sets, and if the two expected step sets are the same, classify the manipulation records corresponding to the two expected step sets into the same category;

[0074] Step S204: Select any two operation records from any similar records, among which there must be an abnormal operation record, and extract the operation accuracy of each step in the two operation records respectively; set the operation accuracy of the i-th step in the abnormal operation 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: Extract features from the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extract features from 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 the other 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 one 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: arbitrarily selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the operation accuracy evaluation results of each step; analyzing the impact 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 of any operation record; obtaining a set of abnormal features 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 any operation record;

[0083] Step S302: Select any one operation record and set the operation accuracy of the i-th step in the operation record to A i If the operation record is an abnormal operation record, then obtain the comprehensive difference degree C of the operation 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, then the i-th step is set as the characteristic step;

[0086] Step S303: arbitrarily 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; the risk value F of the feature step is calculated.

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

[0090] Step S400 includes the following steps:

[0091] Step S401: Generate a real-time control record in the equipment control data management system, 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 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 operation records that are not abnormal operation records, obtain the abnormal operation risk value of each operation record, select the abnormal operation risk value with the smallest value and set it as the risk threshold F min , if F ’ >F min , the staff will be reminded and the operation steps that occur most frequently will be marked.

[0095] An operation and 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 operation process of the control equipment, and generate a control record; the control process recorded in each control record is divided into steps and the operation accuracy of each step is evaluated;

[0097] The abnormal feature analysis module is used to obtain environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record, and determine abnormalities. Based on the evaluation results of each step in any operation record with abnormalities, the module extracts abnormal features and analyzes the impact of each abnormal feature on the accuracy evaluation result.

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

[0099] The real-time operation analysis module is used to pre-generate a set of operation 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. It also performs feature judgment and risk level extraction on each operation step, and calculates the abnormal operation 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 manipulation record generation unit and a manipulation 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 degree of 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 an operation record and extract the characteristic steps from the operation record based on the evaluation results of the operation record and the operation accuracy evaluation results of each step; the risk level calculation unit is used to analyze the impact of each characteristic step in each operation 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, calculate the abnormal control risk value, and if it exceeds the set risk threshold, remind the staff during the adjustment process.

[0108] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for controlling and operating a smart shelter, characterized by: The management method comprises the following steps: Step S100: 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 accuracy of each step; Step S200: Obtaining environmental data changes presented by the smart cabin in any operation record, evaluating the accuracy of the operation record and determining anomalies; extracting abnormal features based on the evaluation results of each step in any operation record with an abnormality, and analyzing the degree of impact of each abnormal feature on the accuracy evaluation result; Step S300: arbitrarily selecting a manipulation record, extracting characteristic steps from the manipulation record based on the evaluation results of the manipulation record and the operation accuracy evaluation results of each step; analyzing the impact of each characteristic step in each manipulation record, and calculating the risk level of each characteristic step; Step S400: Whenever a worker needs to use a control device to adjust the smart cabin, a set of control steps is pre-generated based on the current environmental data presented by the smart cabin. Feature judgment and risk level extraction are performed on each control step, and an abnormal control risk value is calculated. If the risk exceeds the set risk threshold, the worker is reminded during the adjustment process. The step S300 includes the following steps: Step S301: extracting features of the execution operations presented in each step of any operation record; obtaining a set of abnormal features 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 any operation record; Step S302: Select any one operation record and set the operation accuracy of the i-th step in the operation record to A i If the operation record is an abnormal operation record, then obtain the comprehensive difference degree C of the operation 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, then the i-th step is set as the characteristic step; Step S303: arbitrarily 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; the risk value F of the feature step is calculated; The step S400 includes the following steps: Step S401: Generate a real-time control record in the equipment control data management system, 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 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 operation records that are not abnormal operation records, obtain the abnormal operation risk value of each operation record, select the abnormal operation risk value with the smallest value and set it as the risk threshold F min , if F ’ >F min , the staff will be reminded and the operation steps that occur most frequently will be marked.

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 on a control device during use, the operation is recorded in the device control data management system and set as a step; the entire process of the worker using the control device is recorded and the worker's operation time is counted to obtain a control record; Step S102: randomly selecting a manipulation record, presetting a desired goal for the manipulation record, setting a number of desired steps for achieving the desired goal and an execution order for each of the desired steps; sorting the desired steps according to the set execution order, and determining the position of any desired step; Step S103: arbitrarily select the i-th step in the manipulation record, obtain the execution operation presented in the i-th step, compare the execution operation with the execution operation presented in any desired step, and obtain the similarity between the two execution operations; if there is a target desired step and the similarity between the two execution operations of the i-th step exceeds the set similarity threshold, the order of the target desired 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 a judgment function, if 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 the similarity between an expected step and the execution operation of any step in the manipulation record is less than the set similarity threshold, the expected step with the similarity less than the similarity threshold is marked as abnormal and stored in the manipulation record.

3. The method for controlling and operating a smart shelter according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: arbitrarily select a manipulation record, preset target environment data for the manipulation record, and capture the actual environment data after the manipulation 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 assessment rules for the environment data in each dimension, obtain the difference degree of the manipulation record in each dimension, calculate the average value to obtain the comprehensive difference degree C, and obtain the accuracy of the manipulation record ACC = 1-C; Step S202: Obtain the operation duration and accuracy of all operation records, and calculate the average values 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. Step S203: Acquire a number of expected steps set for completing the expected goal in each manipulation record, and generate an expected step set corresponding to each manipulation record; compare any two expected step sets, and if the two expected step sets are the same, classify the manipulation records corresponding to the two expected step sets into the same category; Step S204: Select any two operation records from any similar records, among which there must be an abnormal operation record, and extract the operation accuracy of each step in the two operation records respectively; set the operation accuracy of the i-th step in the abnormal operation 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: Extract features from the execution operations presented in each step with an abnormal mark in the abnormal operation record to obtain an abnormal feature set; extract features from 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. A control and operation management system for executing the control and operation management method for a smart shelter according to any one of claims 1 to 3, 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 by controlling the equipment, establish an equipment control data management system to record each operation 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 environmental data changes presented by the smart cabin in any operation record, evaluate the accuracy of the operation record, and determine abnormalities; extract abnormal features based on the evaluation results of each step in any operation record with abnormalities, and analyze the degree of 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 results of the manipulation record and the operation accuracy evaluation results of each step; analyze The impact of each characteristic step in each manipulation record is calculated to obtain the risk level of each characteristic step; The real-time operation analysis module is used to pre-generate a set of operation steps based on the current environmental data presented by the smart cabin whenever a staff member needs to use the control equipment to adjust the smart cabin; perform feature judgment and risk level extraction on each operation step, calculate the abnormal operation risk value, and if it exceeds the set risk threshold, remind the staff member during the adjustment process.

5. The control and operation management system according to claim 4, 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.

6. The control and operation management system according to claim 4, 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 impact of each abnormal feature on the accuracy evaluation result.

7. The control and operation management system according to claim 4, characterized in that: The characteristic step analysis module It includes a feature 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 operation accuracy evaluation results 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.

8. The control and operation management system according to claim 4, characterized in that: The real-time operation analysis module includes a manipulation step preset unit and a manipulation 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 a staff member 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 remind the staff during the adjustment process if it exceeds the set risk threshold.

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